diff --git a/.gitignore b/.gitignore
index a89d60808ca..20415f123e3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -52,53 +52,6 @@ cpplint.py
/src/configure-*-*.c*
/src/build-local
-# modular interfaces
-/src/interfaces/*_modular/*.doxy
-/src/interfaces/*_modular/Evaluation.i
-/src/interfaces/*_modular/Regression.i
-/src/interfaces/*_modular/Library.i
-/src/interfaces/*_modular/Distribution.i
-/src/interfaces/*_modular/Structure.i
-/src/interfaces/*_modular/Classifier.i
-/src/interfaces/*_modular/Features.i
-/src/interfaces/*_modular/Kernel.i
-/src/interfaces/*_modular/Preprocessor.i
-/src/interfaces/*_modular/Distance.i
-/src/interfaces/*_modular/Clustering.i
-/src/interfaces/*_modular/SGBase.i
-/src/interfaces/*_modular/IO.i
-/src/interfaces/*_modular/Mathematics.i
-/src/interfaces/*_modular/ModelSelection.i
-/src/interfaces/*_modular/modshogun.i
-/src/interfaces/*_modular/modshogun_ignores.i
-/src/interfaces/*_modular/*_includes.i
-/src/interfaces/*_modular/Makefile
-/src/interfaces/*_modular/Converter.i
-/src/interfaces/*_modular/Multiclass.i
-/src/interfaces/*_modular/Machine.i
-/src/interfaces/*_modular/Transfer.i
-/src/interfaces/*_modular/Loss.i
-/src/interfaces/*_modular/Statistics.i
-/src/interfaces/*_modular/Latent.i
-/src/interfaces/*_modular/GaussianProcess.i
-
-# particular modular ones
-/src/interfaces/csharp_modular/*.cs
-/src/interfaces/csharp_modular/abstract_types_extension.i
-/src/interfaces/csharp_modular/modshogun.dll
-/src/interfaces/java_modular/*.java
-/src/interfaces/java_modular/*.jar
-/src/interfaces/java_modular/*.class
-/src/interfaces/java_modular/org/*
-/src/interfaces/java_modular/shogun/*
-/src/interfaces/python_modular/*.py
-/src/interfaces/python_modular/abstract_types_extension.i
-/src/interfaces/r_modular/*.R
-/src/interfaces/r_modular/*.RData
-/src/interfaces/perl_modular/*.pm
-/src/interfaces/octave_modular/abstract_types_extension.i
-/.duped_py_pl.pb
-
# /examples/
*.log
*.exe
@@ -108,9 +61,9 @@ cpplint.py
!/examples/undocumented/libshogun/*.cpp
!/examples/undocumented/libshogun/CMakeLists.txt
!/examples/undocumented/libshogun/tools/
-!examples/undocumented/python_modular/graphical/
-!examples/undocumented/python_modular/*.py
-!examples/undocumented/python_modular/CMakeLists.txt
+!examples/undocumented/python/graphical/
+!examples/undocumented/python/*.py
+!examples/undocumented/python/CMakeLists.txt
# /tests
/tests/unit/shogun-unit-test
@@ -120,12 +73,8 @@ cpplint.py
/tests/unit/*.json
/tests/unit/combined_kernel.weights
Testing/
-examples/undocumented/python_modular/serialized_svm.bz2
-examples/undocumented/python_modular/tmp/blaah.asc
-examples/undocumented/python_modular/tmp/blaah.h5
-examples/undocumented/python_modular/tmp/blaah.json
-examples/undocumented/python_modular/tmp/blaah.xml
-examples/undocumented/python_modular/vw_cache.dat.cache
+examples/undocumented/python/serialized_svm.bz2
+examples/undocumented/python/vw_cache.dat.cache
# cmake
/CMakeFiles/
diff --git a/applications/classification/evaluate_multiclass_labels.py b/applications/classification/evaluate_multiclass_labels.py
index 030f5361db1..f720bb687d0 100644
--- a/applications/classification/evaluate_multiclass_labels.py
+++ b/applications/classification/evaluate_multiclass_labels.py
@@ -32,7 +32,7 @@
import argparse
import logging
import numpy as np
-from modshogun import (LibSVMFile, MulticlassLabels, MulticlassAccuracy)
+from shogun import (LibSVMFile, MulticlassLabels, MulticlassAccuracy)
from utils import get_features_and_labels
LOGGER = logging.getLogger(__file__)
diff --git a/applications/classification/predict_multiclass_svm.py b/applications/classification/predict_multiclass_svm.py
index 88ce0d6b504..79585ec6784 100644
--- a/applications/classification/predict_multiclass_svm.py
+++ b/applications/classification/predict_multiclass_svm.py
@@ -32,7 +32,7 @@
import argparse
import logging
from contextlib import closing
-from modshogun import (LibSVMFile, SparseRealFeatures, MulticlassLabels,
+from shogun import (LibSVMFile, SparseRealFeatures, MulticlassLabels,
MulticlassLibSVM, SerializableHdf5File,
MulticlassAccuracy)
from utils import get_features_and_labels
diff --git a/applications/classification/random_fourier_classification.py b/applications/classification/random_fourier_classification.py
index bf5468a893f..07350cde384 100644
--- a/applications/classification/random_fourier_classification.py
+++ b/applications/classification/random_fourier_classification.py
@@ -32,7 +32,7 @@ def parse_arguments():
return parser.parse_args()
def evaluate(predicted_labels, labels, prefix="Results"):
- from modshogun import PRCEvaluation, ROCEvaluation, AccuracyMeasure
+ from shogun import PRCEvaluation, ROCEvaluation, AccuracyMeasure
prc_evaluator = PRCEvaluation()
roc_evaluator = ROCEvaluation()
@@ -58,9 +58,9 @@ def load_sparse_data(filename, dimension=None):
return {'data':sparse_feats, 'labels':labels}
if __name__=='__main__':
- from modshogun import SparseRealFeatures, RandomFourierDotFeatures, GAUSSIAN
- from modshogun import LibSVMFile, BinaryLabels, SVMOcas
- from modshogun import Time
+ from shogun import SparseRealFeatures, RandomFourierDotFeatures, GAUSSIAN
+ from shogun import LibSVMFile, BinaryLabels, SVMOcas
+ from shogun import Time
from numpy import array
args = parse_arguments()
diff --git a/applications/classification/train_multiclass_svm.py b/applications/classification/train_multiclass_svm.py
index 5dfb1fa4c41..39e17d2619c 100644
--- a/applications/classification/train_multiclass_svm.py
+++ b/applications/classification/train_multiclass_svm.py
@@ -32,7 +32,7 @@
import argparse
import logging
from contextlib import contextmanager, closing
-from modshogun import (LibSVMFile, GaussianKernel, MulticlassLibSVM,
+from shogun import (LibSVMFile, GaussianKernel, MulticlassLibSVM,
SerializableHdf5File, LinearKernel)
from utils import get_features_and_labels, track_execution
diff --git a/applications/classification/utils.py b/applications/classification/utils.py
index 40da45d9ae0..e459c39e97e 100644
--- a/applications/classification/utils.py
+++ b/applications/classification/utils.py
@@ -30,7 +30,7 @@
import logging
from contextlib import contextmanager
-from modshogun import MulticlassLabels, SparseRealFeatures, Time
+from shogun import MulticlassLabels, SparseRealFeatures, Time
logging.basicConfig(level=logging.INFO, format='[%(asctime)-15s %(module)s] %(message)s')
diff --git a/applications/easysvm/galaxy/README b/applications/easysvm/galaxy/README
index a45fe881a7b..1e7b2566082 100644
--- a/applications/easysvm/galaxy/README
+++ b/applications/easysvm/galaxy/README
@@ -1,3 +1,3 @@
The files in this directory are a copy of
svn/projects/galaxy/tools/agr. If you edit them, make sure the
-changes are also integrated into the main version.
\ No newline at end of file
+changes are also integrated into the main version.
diff --git a/applications/msplicer/signal_detectors.py b/applications/msplicer/signal_detectors.py
index f10096d35cb..e12002c2d5f 100644
--- a/applications/msplicer/signal_detectors.py
+++ b/applications/msplicer/signal_detectors.py
@@ -13,7 +13,7 @@
import numpy
import seqdict
-from modshogun import KernelMachine,StringCharFeatures,DNA,WeightedDegreeStringKernel
+from shogun import KernelMachine,StringCharFeatures,DNA,WeightedDegreeStringKernel
class svm_splice_model(object):
def __init__(self, order, traindat, alphas, b, (window_left,offset,window_right), consensus):
diff --git a/applications/ocr/Ai.py b/applications/ocr/Ai.py
index 73edfb261b0..f8a3284ae52 100644
--- a/applications/ocr/Ai.py
+++ b/applications/ocr/Ai.py
@@ -1,9 +1,9 @@
# File : $HeadURL$
# Version: $Id$
-from modshogun import RealFeatures, MulticlassLabels
-from modshogun import GaussianKernel
-from modshogun import GMNPSVM
+from shogun import RealFeatures, MulticlassLabels
+from shogun import GaussianKernel
+from shogun import GMNPSVM
import numpy as np
import gzip as gz
diff --git a/applications/tapkee/faces_embedding.py b/applications/tapkee/faces_embedding.py
index 1931d9fad93..24cf327031d 100644
--- a/applications/tapkee/faces_embedding.py
+++ b/applications/tapkee/faces_embedding.py
@@ -8,7 +8,7 @@
# Written (W) 2011 Sergey Lisitsyn
# Copyright (C) 2011 Sergey Lisitsyn
-from modshogun import *
+from shogun import *
from numpy import *
from matplotlib.offsetbox import TextArea, DrawingArea, OffsetImage, AnnotationBbox
import re,os,time
diff --git a/applications/tapkee/samples/dm.py b/applications/tapkee/samples/dm.py
index 8c1d3b87efd..036028dfd44 100644
--- a/applications/tapkee/samples/dm.py
+++ b/applications/tapkee/samples/dm.py
@@ -1,4 +1,4 @@
-import modshogun as sg
+import shogun as sg
import data
import numpy as np
diff --git a/applications/tapkee/samples/hlle.py b/applications/tapkee/samples/hlle.py
index 7360941b614..0dc860931c0 100644
--- a/applications/tapkee/samples/hlle.py
+++ b/applications/tapkee/samples/hlle.py
@@ -1,4 +1,4 @@
-import modshogun as sg
+import shogun as sg
import data
# load data
diff --git a/applications/tapkee/samples/isomap.py b/applications/tapkee/samples/isomap.py
index 086bf2cbc2e..1d0569e1662 100644
--- a/applications/tapkee/samples/isomap.py
+++ b/applications/tapkee/samples/isomap.py
@@ -1,4 +1,4 @@
-import modshogun as sg
+import shogun as sg
import data
import numpy as np
diff --git a/applications/tapkee/samples/klle.py b/applications/tapkee/samples/klle.py
index 1075a357c1e..77b9908cd68 100644
--- a/applications/tapkee/samples/klle.py
+++ b/applications/tapkee/samples/klle.py
@@ -1,4 +1,4 @@
-import modshogun as sg
+import shogun as sg
import data
import numpy as np
diff --git a/applications/tapkee/samples/la.py b/applications/tapkee/samples/la.py
index 46e61451277..b125fcc67c8 100644
--- a/applications/tapkee/samples/la.py
+++ b/applications/tapkee/samples/la.py
@@ -1,4 +1,4 @@
-import modshogun as sg
+import shogun as sg
import data
import numpy as np
diff --git a/applications/tapkee/samples/lle.py b/applications/tapkee/samples/lle.py
index 60740d3eb71..f0a05dd4599 100644
--- a/applications/tapkee/samples/lle.py
+++ b/applications/tapkee/samples/lle.py
@@ -1,4 +1,4 @@
-import modshogun as sg
+import shogun as sg
import data
# load data
diff --git a/applications/tapkee/samples/lltsa.py b/applications/tapkee/samples/lltsa.py
index 12c19da02a4..f59a3fb2ea3 100644
--- a/applications/tapkee/samples/lltsa.py
+++ b/applications/tapkee/samples/lltsa.py
@@ -1,4 +1,4 @@
-import modshogun as sg
+import shogun as sg
import data
# load data
diff --git a/applications/tapkee/samples/lpp.py b/applications/tapkee/samples/lpp.py
index f5d44662cbb..9c9c4e87f37 100644
--- a/applications/tapkee/samples/lpp.py
+++ b/applications/tapkee/samples/lpp.py
@@ -1,4 +1,4 @@
-import modshogun as sg
+import shogun as sg
import data
# load data
diff --git a/applications/tapkee/samples/ltsa.py b/applications/tapkee/samples/ltsa.py
index 6d4f32734aa..2c2d760cd4a 100644
--- a/applications/tapkee/samples/ltsa.py
+++ b/applications/tapkee/samples/ltsa.py
@@ -1,4 +1,4 @@
-import modshogun as sg
+import shogun as sg
import data
# load data
diff --git a/applications/tapkee/samples/mds.py b/applications/tapkee/samples/mds.py
index d758dce1ed4..4f0f8ffe633 100644
--- a/applications/tapkee/samples/mds.py
+++ b/applications/tapkee/samples/mds.py
@@ -1,4 +1,4 @@
-import modshogun as sg
+import shogun as sg
import data
import numpy as np
diff --git a/applications/tapkee/samples/npe.py b/applications/tapkee/samples/npe.py
index a413a08856c..cefa4880bdf 100644
--- a/applications/tapkee/samples/npe.py
+++ b/applications/tapkee/samples/npe.py
@@ -1,4 +1,4 @@
-import modshogun as sg
+import shogun as sg
import data
# load data
diff --git a/applications/tapkee/words_embedding.py b/applications/tapkee/words_embedding.py
index faa1e8af613..d74deb215e6 100644
--- a/applications/tapkee/words_embedding.py
+++ b/applications/tapkee/words_embedding.py
@@ -10,7 +10,7 @@
from numpy import *
from pylab import *
-from modshogun import *
+from shogun import *
import random
import difflib
diff --git a/cmake/ShogunInterfaces.cmake b/cmake/ShogunInterfaces.cmake
index 238ad13a7ca..7d91d36d9d7 100644
--- a/cmake/ShogunInterfaces.cmake
+++ b/cmake/ShogunInterfaces.cmake
@@ -37,12 +37,12 @@ ADD_CUSTOM_TARGET(${INTERFACE_TARGET_SRC}
COMMENT "copying SWIG files")
INCLUDE(${SWIG_USE_FILE})
-SET_SOURCE_FILES_PROPERTIES(modshogun.i PROPERTIES CPLUSPLUS ON)
+SET_SOURCE_FILES_PROPERTIES(shogun.i PROPERTIES CPLUSPLUS ON)
IF(DEFINED TARGET_SWIGFLAGS)
- SET_SOURCE_FILES_PROPERTIES(modshogun.i PROPERTIES SWIG_FLAGS ${TARGET_SWIGFLAGS})
+ SET_SOURCE_FILES_PROPERTIES(shogun.i PROPERTIES SWIG_FLAGS ${TARGET_SWIGFLAGS})
ENDIF()
SET(SWIG_MODULE_${INTERFACE_NAME}_EXTRA_DEPS ${INTERFACE_FILES})
-SWIG_ADD_MODULE(${INTERFACE_TARGET} ${INTERFACE_NAME} modshogun.i sg_print_functions.cpp)
+SWIG_ADD_MODULE(${INTERFACE_TARGET} ${INTERFACE_NAME} shogun.i sg_print_functions.cpp)
SWIG_LINK_LIBRARIES(${INTERFACE_TARGET} shogun::shogun ${INTERFACE_LIBRARIES})
@@ -52,7 +52,7 @@ SWIG_LINK_LIBRARIES(${INTERFACE_TARGET} shogun::shogun ${INTERFACE_LIBRARIES})
# endforeach()
SET(INTERFACE_REAL_NAME ${SWIG_MODULE_interface_${INTERFACE_NAME}_REAL_NAME})
-SET_TARGET_PROPERTIES(${INTERFACE_REAL_NAME} PROPERTIES OUTPUT_NAME ${PREPEND_TARGET}modshogun)
+SET_TARGET_PROPERTIES(${INTERFACE_REAL_NAME} PROPERTIES OUTPUT_NAME ${PREPEND_TARGET}shogun)
ADD_DEPENDENCIES(${INTERFACE_REAL_NAME} ${INTERFACE_TARGET_SRC})
#ADD_CUSTOM_COMMAND(TARGETS ${PREPEND_TARGET}interface_${INTERFACE_NAME}
@@ -61,23 +61,23 @@ ADD_DEPENDENCIES(${INTERFACE_REAL_NAME} ${INTERFACE_TARGET_SRC})
# ARGS ${CMAKE_SOURCE_DIR}/src/.scrub_docstrings.py )
IF(DOXYGEN_FOUND)
- configure_file(${COMMON_INTERFACE_SRC_DIR}/modshogun.doxy.in modshogun.doxy)
+ configure_file(${COMMON_INTERFACE_SRC_DIR}/shogun.doxy.in shogun.doxy)
ADD_CUSTOM_COMMAND(
- OUTPUT modshogun
+ OUTPUT shogun
COMMAND ${DOXYGEN_EXECUTABLE}
- ARGS modshogun.doxy
+ ARGS shogun.doxy
DEPENDS shogun::shogun
COMMENT "Generating doxygen doc"
)
ADD_CUSTOM_COMMAND(
- OUTPUT modshogun_doxygen.i
+ OUTPUT shogun_doxygen.i
COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_SOURCE_DIR}/src/.doxy2swig.py
- ARGS --quiet --no-function-definition modshogun/doxygen_xml/index.xml modshogun_doxygen.i
- DEPENDS modshogun
+ ARGS --quiet --no-function-definition shogun/doxygen_xml/index.xml shogun_doxygen.i
+ DEPENDS shogun
)
- ADD_CUSTOM_TARGET(${INTERFACE_NAME}_doxy2swig DEPENDS modshogun_doxygen.i)
+ ADD_CUSTOM_TARGET(${INTERFACE_NAME}_doxy2swig DEPENDS shogun_doxygen.i)
ADD_DEPENDENCIES(${INTERFACE_REAL_NAME} ${INTERFACE_NAME}_doxy2swig)
ELSE()
#TODO add scrubing
diff --git a/doc/ipython-notebooks/classification/Classification.ipynb b/doc/ipython-notebooks/classification/Classification.ipynb
index 8cf310c7280..e9364f1b4a5 100644
--- a/doc/ipython-notebooks/classification/Classification.ipynb
+++ b/doc/ipython-notebooks/classification/Classification.ipynb
@@ -48,7 +48,7 @@
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import os\nSHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')\n",
- "from modshogun import *"
+ "from shogun import *"
]
},
{
diff --git a/doc/ipython-notebooks/classification/HashedDocDotFeatures.ipynb b/doc/ipython-notebooks/classification/HashedDocDotFeatures.ipynb
index a413c70e7d1..34395685bbb 100644
--- a/doc/ipython-notebooks/classification/HashedDocDotFeatures.ipynb
+++ b/doc/ipython-notebooks/classification/HashedDocDotFeatures.ipynb
@@ -77,7 +77,7 @@
"The response to that is to read our collection as it is and compute the hash of every token only when it's required, on-the-fly.
\n",
"
On-the-fly Hashing with Shogun
\n",
"We will now have a look at how the above idea is represented in the Shogun Toolbox. That is we will see how we can load our document collection in memory and consider a hashed document-term matrix with the hashing of every document (or token more specifically) happening on-the-fly, only when it's required to be computed. Altough it may sound a bit tricky, it's actually pretty straightforward and here is how.
\n",
- "First of all we import the required components from the modshogun library."
+ "First of all we import the required components from the shogun library."
]
},
{
@@ -90,7 +90,7 @@
"source": [
"%matplotlib inline\n",
"import os\nSHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')\n",
- "from modshogun import StringCharFeatures, RAWBYTE, HashedDocDotFeatures, NGramTokenizer"
+ "from shogun import StringCharFeatures, RAWBYTE, HashedDocDotFeatures, NGramTokenizer"
]
},
{
@@ -133,7 +133,7 @@
},
"outputs": [],
"source": [
- "from modshogun import BinaryLabels\n",
+ "from shogun import BinaryLabels\n",
"from numpy import array\n",
"\n",
"labels = BinaryLabels(array([-1, 1, 1]))"
@@ -199,7 +199,7 @@
},
"outputs": [],
"source": [
- "from modshogun import SVMOcas\n",
+ "from shogun import SVMOcas\n",
"\n",
"C = 0.1\n",
"epsilon = 0.01\n",
diff --git a/doc/ipython-notebooks/classification/MKL.ipynb b/doc/ipython-notebooks/classification/MKL.ipynb
index f429c88fec4..d7d8bf5bffa 100644
--- a/doc/ipython-notebooks/classification/MKL.ipynb
+++ b/doc/ipython-notebooks/classification/MKL.ipynb
@@ -48,7 +48,7 @@
"%matplotlib inline\n",
"import os\nSHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')\n",
"# import all shogun classes\n",
- "from modshogun import *"
+ "from shogun import *"
]
},
{
diff --git a/doc/ipython-notebooks/classification/SupportVectorMachines.ipynb b/doc/ipython-notebooks/classification/SupportVectorMachines.ipynb
index 702a013bc53..a5b5bb51a2c 100644
--- a/doc/ipython-notebooks/classification/SupportVectorMachines.ipynb
+++ b/doc/ipython-notebooks/classification/SupportVectorMachines.ipynb
@@ -119,7 +119,7 @@
"import os\nSHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')\n",
"import matplotlib.patches as patches\n",
"#To import all shogun classes\n",
- "import modshogun as sg\n",
+ "import shogun as sg\n",
"import numpy as np\n",
"\n",
"#Generate some random data\n",
diff --git a/doc/ipython-notebooks/clustering/GMM.ipynb b/doc/ipython-notebooks/clustering/GMM.ipynb
index 884399a3b77..28b9f7cd373 100644
--- a/doc/ipython-notebooks/clustering/GMM.ipynb
+++ b/doc/ipython-notebooks/clustering/GMM.ipynb
@@ -114,7 +114,7 @@
"%matplotlib inline\n",
"import os\nSHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')\n",
"# import all Shogun classes\n",
- "from modshogun import *"
+ "from shogun import *"
]
},
{
diff --git a/doc/ipython-notebooks/clustering/KMeans.ipynb b/doc/ipython-notebooks/clustering/KMeans.ipynb
index eacb76706ff..83c070e0247 100644
--- a/doc/ipython-notebooks/clustering/KMeans.ipynb
+++ b/doc/ipython-notebooks/clustering/KMeans.ipynb
@@ -111,7 +111,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import *\n",
+ "from shogun import *\n",
"\n",
"train_features = RealFeatures(rectangle)"
],
diff --git a/doc/ipython-notebooks/computer_vision/Scene_classification.ipynb b/doc/ipython-notebooks/computer_vision/Scene_classification.ipynb
index cb773bddc6d..cff09beb824 100644
--- a/doc/ipython-notebooks/computer_vision/Scene_classification.ipynb
+++ b/doc/ipython-notebooks/computer_vision/Scene_classification.ipynb
@@ -107,7 +107,7 @@
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
- "from modshogun import *\n",
+ "from shogun import *\n",
"\n",
"# get the list of all jpg images from the path provided\n",
"import os\n",
diff --git a/doc/ipython-notebooks/converter/Tapkee.ipynb b/doc/ipython-notebooks/converter/Tapkee.ipynb
index ace962f0905..307c024eb54 100644
--- a/doc/ipython-notebooks/converter/Tapkee.ipynb
+++ b/doc/ipython-notebooks/converter/Tapkee.ipynb
@@ -130,7 +130,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import Isomap, RealFeatures, MultidimensionalScaling\n",
+ "from shogun import Isomap, RealFeatures, MultidimensionalScaling\n",
"\n",
"# wrap data into Shogun features\n",
"data, colors = generate_data('swissroll')\n",
@@ -175,7 +175,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import StochasticProximityEmbedding\n",
+ "from shogun import StochasticProximityEmbedding\n",
"\n",
"# wrap data into Shogun features\n",
"data, colors = generate_data('helix')\n",
diff --git a/doc/ipython-notebooks/distributions/KernelDensity.ipynb b/doc/ipython-notebooks/distributions/KernelDensity.ipynb
index e28226916e3..f93c15563ab 100644
--- a/doc/ipython-notebooks/distributions/KernelDensity.ipynb
+++ b/doc/ipython-notebooks/distributions/KernelDensity.ipynb
@@ -123,7 +123,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import KernelDensity, RealFeatures, K_GAUSSIAN, D_EUCLIDEAN, EM_KDTREE_SINGLE\n",
+ "from shogun import KernelDensity, RealFeatures, K_GAUSSIAN, D_EUCLIDEAN, EM_KDTREE_SINGLE\n",
"\n",
"def get_kde_result(bandwidth,samples):\n",
" # set model parameters\n",
@@ -352,7 +352,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import KernelDensity, RealFeatures, K_GAUSSIAN, D_EUCLIDEAN, EM_BALLTREE_DUAL\n",
+ "from shogun import KernelDensity, RealFeatures, K_GAUSSIAN, D_EUCLIDEAN, EM_BALLTREE_DUAL\n",
"import scipy.interpolate as interpolate\n",
"\n",
"def get_kde(samples):\n",
diff --git a/doc/ipython-notebooks/evaluation/xval_modelselection.ipynb b/doc/ipython-notebooks/evaluation/xval_modelselection.ipynb
index cb88db11067..9a786d8bdf5 100644
--- a/doc/ipython-notebooks/evaluation/xval_modelselection.ipynb
+++ b/doc/ipython-notebooks/evaluation/xval_modelselection.ipynb
@@ -69,7 +69,7 @@
"%matplotlib inline\n",
"# include all Shogun classes\n",
"import os\nSHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')\n",
- "from modshogun import *\n",
+ "from shogun import *\n",
"# generate some ultra easy training data\n",
"gray()\n",
"n=20\n",
diff --git a/doc/ipython-notebooks/gaussian_process/gaussian_processes.ipynb b/doc/ipython-notebooks/gaussian_process/gaussian_processes.ipynb
index a74e9d118fa..374b9a10093 100644
--- a/doc/ipython-notebooks/gaussian_process/gaussian_processes.ipynb
+++ b/doc/ipython-notebooks/gaussian_process/gaussian_processes.ipynb
@@ -31,7 +31,7 @@
"source": [
"%matplotlib inline\n",
"# import all shogun classes\n",
- "from modshogun import *\n",
+ "from shogun import *\n",
"import random\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
diff --git a/doc/ipython-notebooks/gaussian_process/variational_classifier.ipynb b/doc/ipython-notebooks/gaussian_process/variational_classifier.ipynb
index f8e5db47a6a..6031bf5ad52 100644
--- a/doc/ipython-notebooks/gaussian_process/variational_classifier.ipynb
+++ b/doc/ipython-notebooks/gaussian_process/variational_classifier.ipynb
@@ -90,7 +90,7 @@
"%matplotlib inline\n",
"# import all shogun classes\n",
"import os\nSHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')\n",
- "from modshogun import *\n",
+ "from shogun import *\n",
"\n",
"# import all required libraries\n",
"import scipy\n",
diff --git a/doc/ipython-notebooks/ica/bss_image.ipynb b/doc/ipython-notebooks/ica/bss_image.ipynb
index 7d92ceb1feb..a843d7816be 100644
--- a/doc/ipython-notebooks/ica/bss_image.ipynb
+++ b/doc/ipython-notebooks/ica/bss_image.ipynb
@@ -161,8 +161,8 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import RealFeatures\n",
- "from modshogun import Jade\n",
+ "from shogun import RealFeatures\n",
+ "from shogun import Jade\n",
"\n",
"mixed_signals = RealFeatures(X)\n",
"\n",
diff --git a/doc/ipython-notebooks/intro/Introduction.ipynb b/doc/ipython-notebooks/intro/Introduction.ipynb
index f0a8e41fecf..f939e1b52ef 100644
--- a/doc/ipython-notebooks/intro/Introduction.ipynb
+++ b/doc/ipython-notebooks/intro/Introduction.ipynb
@@ -61,7 +61,7 @@
"%matplotlib inline\n",
"import os\nSHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')\n",
"#To import all Shogun classes\n",
- "from modshogun import *"
+ "from shogun import *"
]
},
{
diff --git a/doc/ipython-notebooks/logdet/logdet.ipynb b/doc/ipython-notebooks/logdet/logdet.ipynb
index 48d502246f7..c9e1e4d9391 100644
--- a/doc/ipython-notebooks/logdet/logdet.ipynb
+++ b/doc/ipython-notebooks/logdet/logdet.ipynb
@@ -93,7 +93,7 @@
},
"outputs": [],
"source": [
- "from modshogun import RealSparseMatrixOperator, LanczosEigenSolver\n",
+ "from shogun import RealSparseMatrixOperator, LanczosEigenSolver\n",
"\n",
"op = RealSparseMatrixOperator(A.tocsc())\n",
"\n",
@@ -125,7 +125,7 @@
"source": [
"# We can specify the power of the sparse-matrix that is to be used for coloring, default values will apply a\n",
"# 2-distance greedy graph coloring algorithm on the sparse-matrix itself. Matrix-power, if specified, is computed in O(lg p)\n",
- "from modshogun import ProbingSampler\n",
+ "from shogun import ProbingSampler\n",
"\n",
"trace_sampler = ProbingSampler(op)\n",
"# apply the graph coloring algorithm and generate the number of colors, i.e. number of trace samples\n",
@@ -150,7 +150,7 @@
},
"outputs": [],
"source": [
- "from modshogun import SerialComputationEngine, CGMShiftedFamilySolver, LogRationalApproximationCGM\n",
+ "from shogun import SerialComputationEngine, CGMShiftedFamilySolver, LogRationalApproximationCGM\n",
"\n",
"engine = SerialComputationEngine()\n",
"cgm = CGMShiftedFamilySolver()\n",
@@ -182,7 +182,7 @@
"outputs": [],
"source": [
"import numpy as np\n",
- "from modshogun import LogDetEstimator\n",
+ "from shogun import LogDetEstimator\n",
"\n",
"# number of log-det samples (use a higher number to get better estimates)\n",
"# (this is 5 times number of colors estimate in practice, so usually 1 probing estimate is enough)\n",
@@ -213,7 +213,7 @@
"# the following method requires massive amount of memory, for demonstration purpose\n",
"# the following code is commented out and direct value obtained from running it once is used\n",
"\n",
- "# from modshogun import Statistics\n",
+ "# from shogun import Statistics\n",
"# actual_logdet = Statistics.log_det(A)\n",
"\n",
"actual_logdet = 7120357.73878\n",
@@ -282,7 +282,7 @@
"probing_estimates = log_det_estimator.sample(num_probing_estimates)\n",
"\n",
"# computing log-det estimates using Gaussian sampler\n",
- "from modshogun import NormalSampler, Statistics\n",
+ "from shogun import NormalSampler, Statistics\n",
"\n",
"num_colors = probing_sampler.get_num_samples()\n",
"normal_sampler = NormalSampler(op.get_dimension())\n",
@@ -447,7 +447,7 @@
},
"outputs": [],
"source": [
- "from modshogun import RealSparseMatrixOperator, ComplexDenseMatrixOperator\n",
+ "from shogun import RealSparseMatrixOperator, ComplexDenseMatrixOperator\n",
"\n",
"dim = 5\n",
"np.random.seed(10)\n",
@@ -487,7 +487,7 @@
"source": [
"from scipy.sparse import csc_matrix\n",
"from scipy.sparse import identity\n",
- "from modshogun import ConjugateGradientSolver\n",
+ "from shogun import ConjugateGradientSolver\n",
"\n",
"# creating a random spd matrix\n",
"dim = 5\n",
@@ -532,8 +532,8 @@
},
"outputs": [],
"source": [
- "from modshogun import ComplexSparseMatrixOperator\n",
- "from modshogun import ConjugateOrthogonalCGSolver\n",
+ "from shogun import ComplexSparseMatrixOperator\n",
+ "from shogun import ConjugateOrthogonalCGSolver\n",
"\n",
"# creating a random spd matrix\n",
"dim = 5\n",
@@ -580,7 +580,7 @@
},
"outputs": [],
"source": [
- "from modshogun import CGMShiftedFamilySolver\n",
+ "from shogun import CGMShiftedFamilySolver\n",
"\n",
"cgm = CGMShiftedFamilySolver()\n",
"\n",
@@ -652,7 +652,7 @@
},
"outputs": [],
"source": [
- "from modshogun import DirectSparseLinearSolver\n",
+ "from shogun import DirectSparseLinearSolver\n",
"\n",
"# creating a random spd matrix\n",
"dim = 5\n",
@@ -691,7 +691,7 @@
},
"outputs": [],
"source": [
- "from modshogun import DirectLinearSolverComplex\n",
+ "from shogun import DirectLinearSolverComplex\n",
"\n",
"# creating a random spd matrix\n",
"dim = 5\n",
diff --git a/doc/ipython-notebooks/metric/LMNN.ipynb b/doc/ipython-notebooks/metric/LMNN.ipynb
index 3ee07c04ba6..112ea1abc93 100644
--- a/doc/ipython-notebooks/metric/LMNN.ipynb
+++ b/doc/ipython-notebooks/metric/LMNN.ipynb
@@ -177,7 +177,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import RealFeatures, MulticlassLabels\n",
+ "from shogun import RealFeatures, MulticlassLabels\n",
"\n",
"features = RealFeatures(x.T)\n",
"labels = MulticlassLabels(y.astype(numpy.float64))"
@@ -197,7 +197,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import LMNN\n",
+ "from shogun import LMNN\n",
"\n",
"# number of target neighbours per example\n",
"k = 1\n",
@@ -384,7 +384,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import KNN, EuclideanDistance, LMNN, RealFeatures, MulticlassLabels\n",
+ "from shogun import KNN, EuclideanDistance, LMNN, RealFeatures, MulticlassLabels\n",
"\n",
"def plot_neighborhood_graph(x, nn, axis=pyplot, cols=['r', 'b', 'g', 'm', 'k', 'y']):\n",
"\tfor i in xrange(x.shape[0]):\n",
@@ -480,7 +480,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import CSVFile, RealFeatures, MulticlassLabels\n",
+ "from shogun import CSVFile, RealFeatures, MulticlassLabels\n",
"\n",
"ape_features = RealFeatures(CSVFile(os.path.join(SHOGUN_DATA_DIR, 'multiclass/fm_ape_gut.dat')))\n",
"ape_labels = MulticlassLabels(CSVFile(os.path.join(SHOGUN_DATA_DIR, 'multiclass/label_ape_gut.dat')))"
@@ -518,7 +518,7 @@
"collapsed": false,
"input": [
"def visualize_tdsne(features, labels):\n",
- " from modshogun import TDistributedStochasticNeighborEmbedding\n",
+ " from shogun import TDistributedStochasticNeighborEmbedding\n",
" \n",
" converter = TDistributedStochasticNeighborEmbedding()\n",
" converter.set_target_dim(2)\n",
@@ -561,9 +561,9 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import KNN, EuclideanDistance\n",
- "from modshogun import StratifiedCrossValidationSplitting, CrossValidation\n",
- "from modshogun import CrossValidationResult, MulticlassAccuracy\n",
+ "from shogun import KNN, EuclideanDistance\n",
+ "from shogun import StratifiedCrossValidationSplitting, CrossValidation\n",
+ "from shogun import CrossValidationResult, MulticlassAccuracy\n",
"\n",
"# set up the classifier\n",
"knn = KNN()\n",
@@ -622,7 +622,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import LMNN\n",
+ "from shogun import LMNN\n",
"import numpy\n",
"\n",
"# to make training faster, use a portion of the features\n",
@@ -712,7 +712,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import CSVFile, RealFeatures, MulticlassLabels\n",
+ "from shogun import CSVFile, RealFeatures, MulticlassLabels\n",
"\n",
"wine_features = RealFeatures(CSVFile(os.path.join(SHOGUN_DATA_DIR, 'uci/wine/fm_wine.dat')))\n",
"wine_labels = MulticlassLabels(CSVFile(os.path.join(SHOGUN_DATA_DIR, 'uci/wine/label_wine.dat')))\n",
@@ -736,9 +736,9 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import KNN, EuclideanDistance\n",
- "from modshogun import StratifiedCrossValidationSplitting, CrossValidation\n",
- "from modshogun import CrossValidationResult, MulticlassAccuracy\n",
+ "from shogun import KNN, EuclideanDistance\n",
+ "from shogun import StratifiedCrossValidationSplitting, CrossValidation\n",
+ "from shogun import CrossValidationResult, MulticlassAccuracy\n",
"import numpy\n",
"\n",
"# kNN classifier\n",
@@ -775,7 +775,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import LMNN\n",
+ "from shogun import LMNN\n",
"\n",
"# train LMNN\n",
"lmnn = LMNN(wine_features, wine_labels, k)\n",
@@ -838,7 +838,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import RescaleFeatures\n",
+ "from shogun import RescaleFeatures\n",
"\n",
"# preprocess features so that all of them vary within [0,1]\n",
"preprocessor = RescaleFeatures()\n",
diff --git a/doc/ipython-notebooks/multiclass/KNN.ipynb b/doc/ipython-notebooks/multiclass/KNN.ipynb
index c5de58b52ab..68b2b174973 100644
--- a/doc/ipython-notebooks/multiclass/KNN.ipynb
+++ b/doc/ipython-notebooks/multiclass/KNN.ipynb
@@ -136,8 +136,8 @@
},
"outputs": [],
"source": [
- "from modshogun import MulticlassLabels, RealFeatures\n",
- "from modshogun import KNN, EuclideanDistance\n",
+ "from shogun import MulticlassLabels, RealFeatures\n",
+ "from shogun import KNN, EuclideanDistance\n",
"\n",
"labels = MulticlassLabels(Ytrain)\n",
"feats = RealFeatures(Xtrain)\n",
@@ -151,7 +151,7 @@
"print \"Predictions\", pred[:5]\n",
"print \"Ground Truth\", Ytest[:5]\n",
"\n",
- "from modshogun import MulticlassAccuracy\n",
+ "from shogun import MulticlassAccuracy\n",
"evaluator = MulticlassAccuracy()\n",
"accuracy = evaluator.evaluate(pred, labels_test)\n",
"\n",
@@ -249,7 +249,7 @@
},
"outputs": [],
"source": [
- "from modshogun import Time, KNN_COVER_TREE, KNN_BRUTE\n",
+ "from shogun import Time, KNN_COVER_TREE, KNN_BRUTE\n",
"start = Time.get_curtime()\n",
"knn.set_k(3)\n",
"knn.set_knn_solver_type(KNN_BRUTE)\n",
@@ -280,7 +280,7 @@
"outputs": [],
"source": [
"def evaluate(labels, feats, use_cover_tree=False):\n",
- " from modshogun import MulticlassAccuracy, CrossValidationSplitting\n",
+ " from shogun import MulticlassAccuracy, CrossValidationSplitting\n",
" import time\n",
" split = CrossValidationSplitting(labels, Nsplit)\n",
" split.build_subsets()\n",
@@ -422,7 +422,7 @@
},
"outputs": [],
"source": [
- "from modshogun import GaussianKernel, GMNPSVM\n",
+ "from shogun import GaussianKernel, GMNPSVM\n",
"\n",
"width=80\n",
"C=1\n",
diff --git a/doc/ipython-notebooks/multiclass/Tree/DecisionTrees.ipynb b/doc/ipython-notebooks/multiclass/Tree/DecisionTrees.ipynb
index 81ebc07ef7a..02f35db823f 100644
--- a/doc/ipython-notebooks/multiclass/Tree/DecisionTrees.ipynb
+++ b/doc/ipython-notebooks/multiclass/Tree/DecisionTrees.ipynb
@@ -199,7 +199,7 @@
},
"outputs": [],
"source": [
- "from modshogun import ID3ClassifierTree, RealFeatures, MulticlassLabels\n",
+ "from shogun import ID3ClassifierTree, RealFeatures, MulticlassLabels\n",
"from numpy import array, concatenate\n",
"\n",
"# encoding dictionary\n",
@@ -561,7 +561,7 @@
},
"outputs": [],
"source": [
- "from modshogun import MulticlassAccuracy\n",
+ "from shogun import MulticlassAccuracy\n",
"\n",
"# Shogun object for calculating multiclass accuracy\n",
"accuracy = MulticlassAccuracy()\n",
@@ -698,7 +698,7 @@
"source": [
"import matplotlib.pyplot as plt\n",
"from numpy import ones, zeros, random, concatenate\n",
- "from modshogun import RealFeatures, MulticlassLabels\n",
+ "from shogun import RealFeatures, MulticlassLabels\n",
"% matplotlib inline\n",
"\n",
"def create_toy_classification_dataset(ncat,do_plot):\n",
@@ -780,7 +780,7 @@
"outputs": [],
"source": [
"from numpy import array\n",
- "from modshogun import C45ClassifierTree\n",
+ "from shogun import C45ClassifierTree\n",
"\n",
"# steps in C4.5 Tree training bundled together in a python method\n",
"def train_tree(feats,types,labels):\n",
@@ -1028,7 +1028,7 @@
},
"outputs": [],
"source": [
- "from modshogun import RealFeatures, MulticlassLabels\n",
+ "from shogun import RealFeatures, MulticlassLabels\n",
"\n",
"# training data\n",
"feats_train = RealFeatures(feats_train)\n",
@@ -1138,7 +1138,7 @@
},
"outputs": [],
"source": [
- "from modshogun import MulticlassAccuracy\n",
+ "from shogun import MulticlassAccuracy\n",
"\n",
"# Shogun object for calculating multiclass accuracy\n",
"accuracy = MulticlassAccuracy()\n",
@@ -1258,7 +1258,7 @@
},
"outputs": [],
"source": [
- "from modshogun import PT_MULTICLASS, CARTree\n",
+ "from shogun import PT_MULTICLASS, CARTree\n",
"from numpy import array\n",
"\n",
"def train_carttree(feat_types,problem_type,num_folds,use_cv_pruning,labels,features):\n",
@@ -1342,7 +1342,7 @@
},
"outputs": [],
"source": [
- "from modshogun import RegressionLabels, RealFeatures\n",
+ "from shogun import RegressionLabels, RealFeatures\n",
"from numpy import random, sin, linspace\n",
"import matplotlib.pyplot as plt\n",
"% matplotlib inline\n",
@@ -1400,7 +1400,7 @@
},
"outputs": [],
"source": [
- "from modshogun import PT_REGRESSION\n",
+ "from shogun import PT_REGRESSION\n",
"from numpy import array\n",
"\n",
"# feature type - continuous\n",
@@ -1539,7 +1539,7 @@
},
"outputs": [],
"source": [
- "from modshogun import CARTree, PT_MULTICLASS\n",
+ "from shogun import CARTree, PT_MULTICLASS\n",
"\n",
"# set attribute types - all continuous\n",
"feature_types = array([False, False, False, False])\n",
@@ -1568,8 +1568,8 @@
},
"outputs": [],
"source": [
- "from modshogun import RealFeatures, MulticlassLabels\n",
- "from modshogun import CrossValidation, MulticlassAccuracy, CrossValidationSplitting, CrossValidationResult\n",
+ "from shogun import RealFeatures, MulticlassLabels\n",
+ "from shogun import CrossValidation, MulticlassAccuracy, CrossValidationSplitting, CrossValidationResult\n",
"\n",
"# training features\n",
"feats_train = RealFeatures(feat)\n",
@@ -1673,8 +1673,8 @@
},
"outputs": [],
"source": [
- "from modshogun import CARTree, RegressionLabels, PT_REGRESSION, MeanSquaredError\n",
- "from modshogun import CrossValidation, CrossValidationSplitting, CrossValidationResult\n",
+ "from shogun import CARTree, RegressionLabels, PT_REGRESSION, MeanSquaredError\n",
+ "from shogun import CrossValidation, CrossValidationSplitting, CrossValidationResult\n",
"\n",
"# form training features\n",
"feats_train = RealFeatures(feat)\n",
@@ -1818,7 +1818,7 @@
},
"outputs": [],
"source": [
- "from modshogun import PT_MULTICLASS, CHAIDTree\n",
+ "from shogun import PT_MULTICLASS, CHAIDTree\n",
"from numpy import array, dtype, int32\n",
"\n",
"def train_chaidtree(dependent_var_type,feature_types,num_bins,features,labels):\n",
@@ -2002,7 +2002,7 @@
},
"outputs": [],
"source": [
- "from modshogun import CSVFile, RealFeatures, MulticlassLabels\n",
+ "from shogun import CSVFile, RealFeatures, MulticlassLabels\n",
"\n",
"train_feats=RealFeatures(CSVFile( os.path.join(SHOGUN_DATA_DIR, 'uci/wine/fm_wine.dat')))\n",
"train_labels=MulticlassLabels(CSVFile( os.path.join(SHOGUN_DATA_DIR, 'uci/wine/label_wine.dat')))"
@@ -2028,7 +2028,7 @@
},
"outputs": [],
"source": [
- "from modshogun import CHAIDTree, MulticlassLabels\n",
+ "from shogun import CHAIDTree, MulticlassLabels\n",
"\n",
"# set attribute types - all attributes are continuous(2)\n",
"feature_types = array([2 for i in range(13)],dtype=int32) \n",
@@ -2059,7 +2059,7 @@
"source": [
"# set up cross validation class\n",
"\n",
- "from modshogun import CrossValidation, CrossValidationSplitting, CrossValidationResult, MulticlassAccuracy\n",
+ "from shogun import CrossValidation, CrossValidationSplitting, CrossValidationResult, MulticlassAccuracy\n",
"\n",
"# set evaluation criteria - multiclass accuracy\n",
"accuracy = MulticlassAccuracy()\n",
@@ -2106,7 +2106,7 @@
},
"outputs": [],
"source": [
- "from modshogun import CSVFile, RealFeatures, RegressionLabels\n",
+ "from shogun import CSVFile, RealFeatures, RegressionLabels\n",
"from numpy import ptp\n",
"\n",
"train_feats=RealFeatures(CSVFile( os.path.join(SHOGUN_DATA_DIR, 'uci/housing/fm_housing.dat')))\n",
@@ -2136,8 +2136,8 @@
},
"outputs": [],
"source": [
- "from modshogun import CHAIDTree, MeanSquaredError\n",
- "from modshogun import CrossValidation, CrossValidationSplitting, CrossValidationResult\n",
+ "from shogun import CHAIDTree, MeanSquaredError\n",
+ "from shogun import CrossValidation, CrossValidationSplitting, CrossValidationResult\n",
"from numpy import array, dtype, int32\n",
"\n",
"def get_cv_error(max_depth):\n",
diff --git a/doc/ipython-notebooks/multiclass/Tree/TreeEnsemble.ipynb b/doc/ipython-notebooks/multiclass/Tree/TreeEnsemble.ipynb
index 5ee0e83915b..249e14b0ff1 100644
--- a/doc/ipython-notebooks/multiclass/Tree/TreeEnsemble.ipynb
+++ b/doc/ipython-notebooks/multiclass/Tree/TreeEnsemble.ipynb
@@ -66,7 +66,7 @@
"collapsed": false,
"input": [
"import os\nSHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../../data')\n",
- "from modshogun import CSVFile,RealFeatures,MulticlassLabels\n",
+ "from shogun import CSVFile,RealFeatures,MulticlassLabels\n",
"\n",
"def load_file(feat_file,label_file):\n",
" feats=RealFeatures(CSVFile(feat_file))\n",
@@ -92,7 +92,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import RandomForest, MajorityVote\n",
+ "from shogun import RandomForest, MajorityVote\n",
"from numpy import array\n",
"\n",
"def setup_random_forest(num_trees,rand_subset_size,combination_rule,feature_types):\n",
@@ -149,7 +149,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import CARTree, PT_MULTICLASS\n",
+ "from shogun import CARTree, PT_MULTICLASS\n",
"\n",
"def train_cart(train_feats,train_labels,feature_types,problem_type):\n",
" c=CARTree(feature_types,problem_type,2,False)\n",
@@ -180,7 +180,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import MulticlassAccuracy\n",
+ "from shogun import MulticlassAccuracy\n",
"\n",
"accuracy=MulticlassAccuracy()\n",
"\n",
diff --git a/doc/ipython-notebooks/multiclass/multiclass_reduction.ipynb b/doc/ipython-notebooks/multiclass/multiclass_reduction.ipynb
index 3f36e306e7f..22e3c21d651 100644
--- a/doc/ipython-notebooks/multiclass/multiclass_reduction.ipynb
+++ b/doc/ipython-notebooks/multiclass/multiclass_reduction.ipynb
@@ -185,10 +185,10 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import RealFeatures, MulticlassLabels\n",
- "from modshogun import LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine\n",
- "from modshogun import MulticlassOneVsOneStrategy, MulticlassOneVsRestStrategy\n",
- "from modshogun import MulticlassAccuracy\n",
+ "from shogun import RealFeatures, MulticlassLabels\n",
+ "from shogun import LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine\n",
+ "from shogun import MulticlassOneVsOneStrategy, MulticlassOneVsRestStrategy\n",
+ "from shogun import MulticlassAccuracy\n",
"\n",
"import time\n",
"\n",
@@ -273,7 +273,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import MulticlassLibLinear\n",
+ "from shogun import MulticlassLibLinear\n",
"mcsvm = MulticlassLibLinear(5.0, feats_train, lab_train)\n",
"mcsvm.set_use_bias(True)\n",
"\n",
@@ -449,7 +449,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import ECOCStrategy, ECOCRandomDenseEncoder, ECOCLLBDecoder\n",
+ "from shogun import ECOCStrategy, ECOCRandomDenseEncoder, ECOCLLBDecoder\n",
"\n",
"print \"\\nRandom Dense Encoder + Margin Loss based Decoder\"\n",
"print \"=\"*60\n",
@@ -482,7 +482,7 @@
"collapsed": false,
"input": [
"def evaluate_multiclass_kernel(strategy):\n",
- " from modshogun import KernelMulticlassMachine, LibSVM, GaussianKernel\n",
+ " from shogun import KernelMulticlassMachine, LibSVM, GaussianKernel\n",
" width=2.1\n",
" epsilon=1e-5\n",
" \n",
@@ -532,7 +532,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import *\n",
+ "from shogun import *\n",
"from numpy import *\n",
"\n",
"num=1000;\n",
@@ -624,7 +624,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import KernelMulticlassMachine, LibSVM, GaussianKernel\n",
+ "from shogun import KernelMulticlassMachine, LibSVM, GaussianKernel\n",
"\n",
"width=2.1\n",
"epsilon=1e-5\n",
diff --git a/doc/ipython-notebooks/multiclass/naive_bayes.ipynb b/doc/ipython-notebooks/multiclass/naive_bayes.ipynb
index 9a833ef583f..661a4963ed1 100644
--- a/doc/ipython-notebooks/multiclass/naive_bayes.ipynb
+++ b/doc/ipython-notebooks/multiclass/naive_bayes.ipynb
@@ -140,9 +140,9 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import GaussianNaiveBayes\n",
- "from modshogun import RealFeatures\n",
- "from modshogun import MulticlassLabels\n",
+ "from shogun import GaussianNaiveBayes\n",
+ "from shogun import RealFeatures\n",
+ "from shogun import MulticlassLabels\n",
"\n",
"X_train, Y_train = gen_samples(n_train)\n",
"\n",
diff --git a/doc/ipython-notebooks/neuralnets/autoencoders.ipynb b/doc/ipython-notebooks/neuralnets/autoencoders.ipynb
index 64949fdab5d..8444fc9e03e 100644
--- a/doc/ipython-notebooks/neuralnets/autoencoders.ipynb
+++ b/doc/ipython-notebooks/neuralnets/autoencoders.ipynb
@@ -55,7 +55,7 @@
"%matplotlib inline\n",
"import os\nSHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')\n",
"from scipy.io import loadmat\n",
- "from modshogun import RealFeatures, MulticlassLabels, Math\n",
+ "from shogun import RealFeatures, MulticlassLabels, Math\n",
"\n",
"# load the dataset\n",
"dataset = loadmat(os.path.join(SHOGUN_DATA_DIR, 'multiclass/usps.mat'))\n",
@@ -101,7 +101,7 @@
},
"outputs": [],
"source": [
- "from modshogun import NeuralLayers, DeepAutoencoder\n",
+ "from shogun import NeuralLayers, DeepAutoencoder\n",
"\n",
"layers = NeuralLayers()\n",
"layers = layers.input(256).rectified_linear(512).rectified_linear(128).rectified_linear(512).linear(256).done()\n",
@@ -137,7 +137,7 @@
},
"outputs": [],
"source": [
- "from modshogun import AENT_DROPOUT, NNOM_GRADIENT_DESCENT\n",
+ "from shogun import AENT_DROPOUT, NNOM_GRADIENT_DESCENT\n",
"\n",
"ae.pt_noise_type.set_const(AENT_DROPOUT) # use dropout noise\n",
"ae.pt_noise_parameter.set_const(0.5) # each input has a 50% chance of being set to zero\n",
@@ -150,7 +150,7 @@
"ae.pt_epsilon.set_const(0.0) # disable automatic convergence testing\n",
"\n",
"# uncomment this line to allow the training progress to be printed on the console\n",
- "#from modshogun import MSG_INFO; ae.io.set_loglevel(MSG_INFO)\n",
+ "#from shogun import MSG_INFO; ae.io.set_loglevel(MSG_INFO)\n",
"\n",
"# start pre-training. this might take some time\n",
"ae.pre_train(Xtrain)"
@@ -287,7 +287,7 @@
},
"outputs": [],
"source": [
- "from modshogun import NeuralSoftmaxLayer\n",
+ "from shogun import NeuralSoftmaxLayer\n",
"\n",
"nn = ae.convert_to_neural_network(NeuralSoftmaxLayer(10))\n",
"\n",
@@ -312,7 +312,7 @@
},
"outputs": [],
"source": [
- "from modshogun import MulticlassAccuracy\n",
+ "from shogun import MulticlassAccuracy\n",
"\n",
"predictions = nn.apply_multiclass(Xtest)\n",
"accuracy = MulticlassAccuracy().evaluate(predictions, Ytest) * 100\n",
@@ -344,7 +344,7 @@
},
"outputs": [],
"source": [
- "from modshogun import DynamicObjectArray, NeuralInputLayer, NeuralConvolutionalLayer, CMAF_RECTIFIED_LINEAR\n",
+ "from shogun import DynamicObjectArray, NeuralInputLayer, NeuralConvolutionalLayer, CMAF_RECTIFIED_LINEAR\n",
"\n",
"conv_layers = DynamicObjectArray()\n",
"# 16x16 single channel images\n",
diff --git a/doc/ipython-notebooks/neuralnets/neuralnets_digits.ipynb b/doc/ipython-notebooks/neuralnets/neuralnets_digits.ipynb
index 98c0bc14061..02e056b18cd 100644
--- a/doc/ipython-notebooks/neuralnets/neuralnets_digits.ipynb
+++ b/doc/ipython-notebooks/neuralnets/neuralnets_digits.ipynb
@@ -51,7 +51,7 @@
"%matplotlib inline\n",
"import os\nSHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')\n",
"from scipy.io import loadmat\n",
- "from modshogun import RealFeatures, MulticlassLabels, Math\n",
+ "from shogun import RealFeatures, MulticlassLabels, Math\n",
"\n",
"# load the dataset\n",
"dataset = loadmat(os.path.join(SHOGUN_DATA_DIR, 'multiclass/usps.mat'))\n",
@@ -109,8 +109,8 @@
},
"outputs": [],
"source": [
- "from modshogun import NeuralNetwork, NeuralInputLayer, NeuralLogisticLayer, NeuralSoftmaxLayer\n",
- "from modshogun import DynamicObjectArray\n",
+ "from shogun import NeuralNetwork, NeuralInputLayer, NeuralLogisticLayer, NeuralSoftmaxLayer\n",
+ "from shogun import DynamicObjectArray\n",
"\n",
"# setup the layers\n",
"layers = DynamicObjectArray()\n",
@@ -215,7 +215,7 @@
},
"outputs": [],
"source": [
- "from modshogun import MulticlassAccuracy\n",
+ "from shogun import MulticlassAccuracy\n",
"\n",
"def compute_accuracy(net, X, Y):\n",
" predictions = net.apply_multiclass(X)\n",
@@ -246,7 +246,7 @@
"net_no_reg.set_max_num_epochs(600)\n",
"\n",
"# uncomment this line to allow the training progress to be printed on the console\n",
- "#from modshogun import MSG_INFO; net_no_reg.io.set_loglevel(MSG_INFO)\n",
+ "#from shogun import MSG_INFO; net_no_reg.io.set_loglevel(MSG_INFO)\n",
"\n",
"net_no_reg.set_labels(Ytrain)\n",
"net_no_reg.train(Xtrain) # this might take a while, depending on your machine\n",
@@ -336,7 +336,7 @@
},
"outputs": [],
"source": [
- "from modshogun import NNOM_GRADIENT_DESCENT\n",
+ "from shogun import NNOM_GRADIENT_DESCENT\n",
"\n",
"# set the dropout probabilty for neurons in the hidden layers\n",
"net_dropout.set_dropout_hidden(0.5)\n",
@@ -391,7 +391,7 @@
},
"outputs": [],
"source": [
- "from modshogun import NeuralConvolutionalLayer, CMAF_RECTIFIED_LINEAR\n",
+ "from shogun import NeuralConvolutionalLayer, CMAF_RECTIFIED_LINEAR\n",
"\n",
"# prepere the layers\n",
"layers_conv = DynamicObjectArray()\n",
diff --git a/doc/ipython-notebooks/neuralnets/rbms_dbns.ipynb b/doc/ipython-notebooks/neuralnets/rbms_dbns.ipynb
index 3d2e8c16589..0e7a05fe47b 100644
--- a/doc/ipython-notebooks/neuralnets/rbms_dbns.ipynb
+++ b/doc/ipython-notebooks/neuralnets/rbms_dbns.ipynb
@@ -174,7 +174,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import RBM, RBMVUT_BINARY, Math\n",
+ "from shogun import RBM, RBMVUT_BINARY, Math\n",
"\n",
"# initialize the random number generator with a fixed seed, for repeatability\n",
"Math.init_random(10)\n",
@@ -223,10 +223,10 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import RealFeatures, RBMMM_PSEUDO_LIKELIHOOD\n",
+ "from shogun import RealFeatures, RBMMM_PSEUDO_LIKELIHOOD\n",
"\n",
"# uncomment this line to allow the training progress to be printed on the console\n",
- "#from modshogun import MSG_INFO; rbms[0].io.set_loglevel(MSG_INFO)\n",
+ "#from shogun import MSG_INFO; rbms[0].io.set_loglevel(MSG_INFO)\n",
"\n",
"for i in range(10):\n",
" # obtain the data for digit i\n",
@@ -305,7 +305,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import DeepBeliefNetwork\n",
+ "from shogun import DeepBeliefNetwork\n",
"\n",
"dbn = DeepBeliefNetwork(256) # 256 visible units\n",
"dbn.add_hidden_layer(200) # 200 units in the first hidden layer\n",
@@ -386,7 +386,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import NeuralSoftmaxLayer, MulticlassLabels\n",
+ "from shogun import NeuralSoftmaxLayer, MulticlassLabels\n",
"\n",
"# get the neural network\n",
"nn = dbn.convert_to_neural_network(NeuralSoftmaxLayer(10))\n",
@@ -413,7 +413,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import MulticlassAccuracy\n",
+ "from shogun import MulticlassAccuracy\n",
"\n",
"predictions = nn.apply_multiclass(RealFeatures(Xtest))\n",
"accuracy = MulticlassAccuracy().evaluate(predictions, MulticlassLabels(Ytest)) * 100\n",
diff --git a/doc/ipython-notebooks/pca/pca_notebook.ipynb b/doc/ipython-notebooks/pca/pca_notebook.ipynb
index 177e328dcae..8d29e3f66a5 100644
--- a/doc/ipython-notebooks/pca/pca_notebook.ipynb
+++ b/doc/ipython-notebooks/pca/pca_notebook.ipynb
@@ -33,7 +33,7 @@
"%matplotlib inline\n",
"import os\nSHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')\n",
"# import all shogun classes\n",
- "from modshogun import *"
+ "from shogun import *"
]
},
{
diff --git a/doc/ipython-notebooks/regression/Regression.ipynb b/doc/ipython-notebooks/regression/Regression.ipynb
index 8d69552897a..e7ebc689932 100644
--- a/doc/ipython-notebooks/regression/Regression.ipynb
+++ b/doc/ipython-notebooks/regression/Regression.ipynb
@@ -106,7 +106,7 @@
"import os\nSHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')\n",
"from cycler import cycler\n",
"# import all shogun classes\n",
- "from modshogun import *\n",
+ "from shogun import *\n",
"slope = 3\n",
"\n",
"X_train = rand(30)*10\n",
diff --git a/doc/ipython-notebooks/statistical_testing/mmd_two_sample_testing.ipynb b/doc/ipython-notebooks/statistical_testing/mmd_two_sample_testing.ipynb
index ebc165f1a19..2bedd3a02a9 100644
--- a/doc/ipython-notebooks/statistical_testing/mmd_two_sample_testing.ipynb
+++ b/doc/ipython-notebooks/statistical_testing/mmd_two_sample_testing.ipynb
@@ -41,7 +41,7 @@
"%pylab inline\n",
"%matplotlib inline\n",
"import os\nSHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')\n",
- "import modshogun as sg\n",
+ "import shogun as sg\n",
"import numpy as np"
]
},
diff --git a/doc/ipython-notebooks/structure/Binary_Denoising.ipynb b/doc/ipython-notebooks/structure/Binary_Denoising.ipynb
index 292b9245ba3..9b3d2039356 100644
--- a/doc/ipython-notebooks/structure/Binary_Denoising.ipynb
+++ b/doc/ipython-notebooks/structure/Binary_Denoising.ipynb
@@ -262,10 +262,10 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import Factor, TableFactorType, FactorGraph\n",
- "from modshogun import FactorGraphObservation, FactorGraphLabels, FactorGraphFeatures\n",
- "from modshogun import FactorGraphModel, GRAPH_CUT, LP_RELAXATION\n",
- "from modshogun import MAPInference"
+ "from shogun import Factor, TableFactorType, FactorGraph\n",
+ "from shogun import FactorGraphObservation, FactorGraphLabels, FactorGraphFeatures\n",
+ "from shogun import FactorGraphModel, GRAPH_CUT, LP_RELAXATION\n",
+ "from shogun import MAPInference"
],
"language": "python",
"metadata": {},
@@ -518,7 +518,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import StochasticSOSVM\n",
+ "from shogun import StochasticSOSVM\n",
"import time\n",
"\n",
"# Training with Stocastic Gradient Descent\n",
diff --git a/doc/ipython-notebooks/structure/FGM.ipynb b/doc/ipython-notebooks/structure/FGM.ipynb
index a89cae5d57f..8d724e7ba6e 100644
--- a/doc/ipython-notebooks/structure/FGM.ipynb
+++ b/doc/ipython-notebooks/structure/FGM.ipynb
@@ -248,7 +248,7 @@
},
"outputs": [],
"source": [
- "from modshogun import TableFactorType\n",
+ "from shogun import TableFactorType\n",
"\n",
"# unary, type_id = 0\n",
"cards_u = np.array([n_stats], np.int32)\n",
@@ -294,8 +294,8 @@
"source": [
"def prepare_data(x, y, ftype, num_samples):\n",
" \"\"\"prepare FactorGraphFeatures and FactorGraphLabels \"\"\"\n",
- " from modshogun import Factor, TableFactorType, FactorGraph\n",
- " from modshogun import FactorGraphObservation, FactorGraphLabels, FactorGraphFeatures\n",
+ " from shogun import Factor, TableFactorType, FactorGraph\n",
+ " from shogun import FactorGraphObservation, FactorGraphLabels, FactorGraphFeatures\n",
"\n",
" samples = FactorGraphFeatures(num_samples)\n",
" labels = FactorGraphLabels(num_samples)\n",
@@ -443,7 +443,7 @@
},
"outputs": [],
"source": [
- "from modshogun import FactorGraphModel, TREE_MAX_PROD\n",
+ "from shogun import FactorGraphModel, TREE_MAX_PROD\n",
"\n",
"# create model and register factor types\n",
"model = FactorGraphModel(samples, labels, TREE_MAX_PROD)\n",
@@ -468,8 +468,8 @@
},
"outputs": [],
"source": [
- "from modshogun import DualLibQPBMSOSVM\n",
- "from modshogun import BmrmStatistics\n",
+ "from shogun import DualLibQPBMSOSVM\n",
+ "from shogun import BmrmStatistics\n",
"import pickle\n",
"import time\n",
"\n",
@@ -577,7 +577,7 @@
},
"outputs": [],
"source": [
- "from modshogun import StochasticSOSVM\n",
+ "from shogun import StochasticSOSVM\n",
"\n",
"# the 3rd parameter is do_weighted_averaging, by turning this on, \n",
"# a possibly faster convergence rate may be achieved.\n",
@@ -760,7 +760,7 @@
},
"outputs": [],
"source": [
- "from modshogun import FactorGraphFeatures, FactorGraphObservation, TREE_MAX_PROD, MAPInference\n",
+ "from shogun import FactorGraphFeatures, FactorGraphObservation, TREE_MAX_PROD, MAPInference\n",
"\n",
"# get a factor graph instance from test data\n",
"fg0 = samples_ts.get_sample(100)\n",
@@ -800,7 +800,7 @@
},
"outputs": [],
"source": [
- "from modshogun import LabelsFactory, SOSVMHelper\n",
+ "from shogun import LabelsFactory, SOSVMHelper\n",
"\n",
"# training error of BMRM method\n",
"bmrm.set_w(w_bmrm)\n",
diff --git a/doc/ipython-notebooks/structure/multilabel_structured_prediction.ipynb b/doc/ipython-notebooks/structure/multilabel_structured_prediction.ipynb
index 6e73a76082f..7eb42989a03 100644
--- a/doc/ipython-notebooks/structure/multilabel_structured_prediction.ipynb
+++ b/doc/ipython-notebooks/structure/multilabel_structured_prediction.ipynb
@@ -165,14 +165,14 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import RealFeatures, MultilabelSOLabels, MultilabelModel\n",
+ "from shogun import RealFeatures, MultilabelSOLabels, MultilabelModel\n",
"\n",
"def create_features(X, constant):\n",
" features = RealFeatures(\n",
" np.c_[X, constant * np.ones(X.shape[0])].T)\n",
" \n",
" return features\n",
- "from modshogun import MultilabelSOLabels\n",
+ "from shogun import MultilabelSOLabels\n",
"\n",
"def create_labels(Y, n_classes):\n",
" try:\n",
@@ -236,7 +236,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import StochasticSOSVM, DualLibQPBMSOSVM, StructuredAccuracy, LabelsFactory\n",
+ "from shogun import StochasticSOSVM, DualLibQPBMSOSVM, StructuredAccuracy, LabelsFactory\n",
"from time import time\n",
"\n",
"sgd = StochasticSOSVM(model, labels)\n",
@@ -386,7 +386,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "from modshogun import SparseMultilabel_obtain_from_generic\n",
+ "from shogun import SparseMultilabel_obtain_from_generic\n",
"\n",
"def plot_decision_plane(machine,\n",
" title,\n",
diff --git a/doc/ipython-notebooks/template.ipynb b/doc/ipython-notebooks/template.ipynb
index 678beeca981..8a3c7846fbe 100644
--- a/doc/ipython-notebooks/template.ipynb
+++ b/doc/ipython-notebooks/template.ipynb
@@ -108,4 +108,4 @@
"metadata": {}
}
]
-}
\ No newline at end of file
+}
diff --git a/doc/readme/INTERFACES.md b/doc/readme/INTERFACES.md
index b348a5b3217..b5e2aef34ca 100644
--- a/doc/readme/INTERFACES.md
+++ b/doc/readme/INTERFACES.md
@@ -39,53 +39,53 @@ Running it:
./native_example
### Python
-This needs `modshogun.py` to be visible, which is either in `path/to/build/src/interfaces/python_modular/` or in something similar to `path/to/shogun-install/lib/python2.7/dist-packages/`
+This needs `shogun.py` to be visible, which is either in `path/to/build/src/interfaces/python_modular/` or in something similar to `path/to/shogun-install/lib/python2.7/dist-packages/`
- export PYTHONPATH="path/to/modshogun.py:$PYTHONPATH"
+ export PYTHONPATH="path/to/shogun.py:$PYTHONPATH"
Running an example:
python path/to/python_example.py
### Octave
-This needs `modshogun.oct` to be visible, which is either in `path/to/build/src/interfaces/octave_modular/` or in something similar to `path/to/shogun-install/lib/x86_64-linux-gnu/octave/site/oct/api-v50+/x86_64-pc-linux-gnu/shogun/`
+This needs `shogun.oct` to be visible, which is either in `path/to/build/src/interfaces/octave_modular/` or in something similar to `path/to/shogun-install/lib/x86_64-linux-gnu/octave/site/oct/api-v50+/x86_64-pc-linux-gnu/shogun/`
- export OCTAVE_PATH="path/to/modshogun.oct:$OCTAVE_PATH"
+ export OCTAVE_PATH="path/to/shogun.oct:$OCTAVE_PATH"
Running an example:
python path/to/octave_example.py
### Ruby
-This needs `modshogun.rb` to be visible, which is either in `path/to/build/src/interfaces/ruby_modular/` or in something similar to `path/to/shogun-install/lib/x86_64-linux-gnu/site_ruby`
- export RUBYLIB="path/to/modshogun.rb:$RUBYLIB"
+This needs `shogun.rb` to be visible, which is either in `path/to/build/src/interfaces/ruby_modular/` or in something similar to `path/to/shogun-install/lib/x86_64-linux-gnu/site_ruby`
+ export RUBYLIB="path/to/shogun.rb:$RUBYLIB"
Running an example:
ruby path/to/ruby_example.rb
### R
-This needs `modshogun.R` to be visible, which is either in `path/to/build/src/interfaces/r_modular/` or in something similar to `path/to/shogun-install/lib/R/site-library`
- export R_LIBS_USER="path/to/modshogun.R:$R_LIBS_USER"
+This needs `shogun.R` to be visible, which is either in `path/to/build/src/interfaces/r_modular/` or in something similar to `path/to/shogun-install/lib/R/site-library`
+ export R_LIBS_USER="path/to/shogun.R:$R_LIBS_USER"
Running an example:
R --no-restore --no-save --no-readline --slave -f path/to/r_example.rb
### Lua
-This needs `libmodshogun.so` (this is the interface file, not the shared library file `libshogun.so`) to be visible, which is either in `path/to/build/src/interfaces/lua_modular/` or in something similar to `path/to/shogun-install/lib/lua/5.1/`
+This needs `libshogun.so` (this is the interface file, not the shared library file `libshogun.so`) to be visible, which is either in `path/to/build/src/interfaces/lua_modular/` or in something similar to `path/to/shogun-install/lib/lua/5.1/`
- export LUA_CPATH="path/to/libmodshogun.so:$LUA_CPATH"
+ export LUA_CPATH="path/to/libshogun.so:$LUA_CPATH"
Running an example:
R --no-restore --no-save --no-readline --slave -f path/to/r_example.R
### CSharp
-This needs `modshogun.dll` to be visible, which is either in `path/to/build/src/interfaces/csharp_modular` or in something similar to `path/to/shogun-install/lib/cli/shogun/`
+This needs `shogun.dll` to be visible, which is either in `path/to/build/src/interfaces/csharp_modular` or in something similar to `path/to/shogun-install/lib/cli/shogun/`
Compiling code works with the mono C# compiler and passing location of the above file
- mcs path/to/csharp_example.cs /lib:path/to/modshogun.dll/r:modshogun -out:csharp_example.exe
+ mcs path/to/csharp_example.cs /lib:path/to/shogun.dll/r:shogun -out:csharp_example.exe
Running requires setting the mono path
@@ -103,7 +103,7 @@ usually in `/usr/share/java/`.
Compiling code works with the java compiler and passing location of `shogun.jar`,
`jblas.jar`, and the example itself in the class path
- javac -cp /path/to/jblas.jar:/path/to/modshogun.jar:path/to/java_example.java -d /path/to/output/ /path/to/java_example.java
+ javac -cp /path/to/jblas.jar:/path/to/shogun.jar:path/to/java_example.java -d /path/to/output/ /path/to/java_example.java
Running it again requires the above class path and some more options
diff --git a/examples/meta/csharp/CMakeLists.txt b/examples/meta/csharp/CMakeLists.txt
index d38f9b4136e..7e0d86bfc81 100644
--- a/examples/meta/csharp/CMakeLists.txt
+++ b/examples/meta/csharp/CMakeLists.txt
@@ -1,4 +1,4 @@
-SET(CSHARP_FLAGS "/lib:${INTERFACE_CSHARP_BUILD_DIR};/r:modshogun")
+SET(CSHARP_FLAGS "/lib:${INTERFACE_CSHARP_BUILD_DIR};/r:shogun")
# add test case for each generated example
# (not generated yet so have to fake filenames from META_EXAMPLES list)
diff --git a/examples/meta/generator/targets/csharp.json b/examples/meta/generator/targets/csharp.json
index 50d40a6baf0..e227101a66f 100644
--- a/examples/meta/generator/targets/csharp.json
+++ b/examples/meta/generator/targets/csharp.json
@@ -1,5 +1,5 @@
{
- "Program": "using System;\n\npublic class classifier_knn_modular {\npublic static void Main() {\nmodshogun.init_shogun_with_defaults();\n\n$program\n}\n}\n",
+ "Program": "using System;\n\npublic class Application {\npublic static void Main() {\nshogun.init_shogun_with_defaults();\n\n$program\n}\n}\n",
"Statement": "$statement;\n",
"Comment": "//$comment\n",
"Init": {
diff --git a/examples/meta/generator/targets/java.json b/examples/meta/generator/targets/java.json
index 59ec7da993c..f3e08a1b84d 100644
--- a/examples/meta/generator/targets/java.json
+++ b/examples/meta/generator/targets/java.json
@@ -1,5 +1,5 @@
{
- "Program": "import org.jblas.DoubleMatrix;\nimport org.jblas.FloatMatrix;\n\nimport org.shogun.modshogun;\n$dependencies\n\npublic class $programName {\nstatic {\nSystem.loadLibrary(\"modshogun\");\n}\n\npublic static void main(String argv[]) {\nmodshogun.init_shogun_with_defaults();\n\n$program\n}\n}\n",
+ "Program": "import org.jblas.DoubleMatrix;\nimport org.jblas.FloatMatrix;\n\nimport org.shogun.shogun;\n$dependencies\n\npublic class $programName {\nstatic {\nSystem.loadLibrary(\"shogun\");\n}\n\npublic static void main(String argv[]) {\nshogun.init_shogun_with_defaults();\n\n$program\n}\n}\n",
"Dependencies": {
"IncludeAllClasses": true,
"IncludeEnums": true,
diff --git a/examples/meta/generator/targets/lua.json b/examples/meta/generator/targets/lua.json
index a5a5171f525..0f125668727 100644
--- a/examples/meta/generator/targets/lua.json
+++ b/examples/meta/generator/targets/lua.json
@@ -1,9 +1,9 @@
{
- "Program": "require 'modshogun'\n\n$program",
+ "Program": "require 'shogun'\n\n$program",
"Statement": "$statement\n",
"Comment": "--$comment\n",
"Init": {
- "Construct": "$name = modshogun.$typeName($arguments)",
+ "Construct": "$name = shogun.$typeName($arguments)",
"Copy": "$name = $expr"
},
"Assign": "$identifier = $expr",
@@ -23,7 +23,7 @@
"MethodCall": "$object:$method($arguments)",
"StaticCall": "$typeName:$method($arguments)",
"Identifier": "$identifier",
- "Enum":"modshogun.$value"
+ "Enum":"shogun.$value"
},
"Element": {
"Access": {
diff --git a/examples/meta/generator/targets/octave.json b/examples/meta/generator/targets/octave.json
index 010aa47f040..ddcc0054e99 100644
--- a/examples/meta/generator/targets/octave.json
+++ b/examples/meta/generator/targets/octave.json
@@ -1,5 +1,5 @@
{
- "Program": "modshogun\n\n$program",
+ "Program": "shogun\n\n$program",
"Statement": "$statement;\n",
"Comment": "%$comment\n",
"Init": {
diff --git a/examples/meta/generator/targets/python.json b/examples/meta/generator/targets/python.json
index b83e113b50b..cc618cbfb37 100644
--- a/examples/meta/generator/targets/python.json
+++ b/examples/meta/generator/targets/python.json
@@ -4,8 +4,8 @@
"IncludeAllClasses": false,
"IncludeInterfacedClasses": true,
"IncludeEnums": true,
- "DependencyListElement": "from modshogun import $typeName",
- "DependencyListElementEnum": "from modshogun import $value",
+ "DependencyListElement": "from shogun import $typeName",
+ "DependencyListElementEnum": "from shogun import $value",
"DependencyListSeparator": "\n"
},
"Statement": "$statement\n",
diff --git a/examples/meta/generator/targets/ruby.json b/examples/meta/generator/targets/ruby.json
index d6dd8c9441a..c3f06660cf0 100644
--- a/examples/meta/generator/targets/ruby.json
+++ b/examples/meta/generator/targets/ruby.json
@@ -1,9 +1,9 @@
{
- "Program": "require 'modshogun'\n\n$dependencies$program",
+ "Program": "require 'shogun'\n\n$dependencies$program",
"Statement": "$statement\n",
"Comment": "#$comment\n",
"Init": {
- "Construct": "$name = Modshogun::$typeName.new $arguments",
+ "Construct": "$name = Shogun::$typeName.new $arguments",
"Copy": "$name = $expr",
"CharVector": "$name = NArray.byte($arguments)",
"ByteVector": "$name = NArray.byte($arguments)",
@@ -41,9 +41,9 @@
"RealLiteral": "$number",
"FloatLiteral": "$number",
"MethodCall": "$object.$method $arguments",
- "StaticCall": "Modshogun::$typeName.$method $arguments",
+ "StaticCall": "Shogun::$typeName.$method $arguments",
"Identifier": "$identifier",
- "Enum":"Modshogun::$value"
+ "Enum":"Shogun::$value"
},
"Element": {
"Access": {
diff --git a/examples/meta/generator/translate.py b/examples/meta/generator/translate.py
index 25477a85241..2637f7e5519 100644
--- a/examples/meta/generator/translate.py
+++ b/examples/meta/generator/translate.py
@@ -241,7 +241,7 @@ def injectVarsStoring(self, statementList, programName, varsToStore):
def dependenciesString(self, allClasses, interfacedClasses, enums):
""" Returns dependency import string
- e.g. for python: "from modshogun import RealFeatures\n\n"
+ e.g. for python: "from shogun import RealFeatures\n\n"
"""
if "Dependencies" not in self.targetDict:
diff --git a/examples/meta/lua/CMakeLists.txt b/examples/meta/lua/CMakeLists.txt
index 4cd9bc27bc5..fa79808184f 100644
--- a/examples/meta/lua/CMakeLists.txt
+++ b/examples/meta/lua/CMakeLists.txt
@@ -8,5 +8,5 @@ FOREACH(META_EXAMPLE ${META_EXAMPLES})
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/{EXAMPLE_REL_DIR}
COMMAND ${LUA_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/${EXAMPLE_REL_DIR}/${EXAMPLE_NAME}.lua)
set_property(TEST generated_lua-${EXAMPLE_NAME_WITH_DIR} PROPERTY
- ENVIRONMENT "LUA_CPATH=${INTERFACE_LUA_BUILD_DIR}/libmodshogun.so")
+ ENVIRONMENT "LUA_CPATH=${INTERFACE_LUA_BUILD_DIR}/libshogun.so")
ENDFOREACH()
diff --git a/examples/undocumented/python/classifier_custom_kernel.py b/examples/undocumented/python/classifier_custom_kernel.py
index 3edab6e2194..89e3ffef759 100644
--- a/examples/undocumented/python/classifier_custom_kernel.py
+++ b/examples/undocumented/python/classifier_custom_kernel.py
@@ -2,7 +2,7 @@
parameter_list = [[1,7],[2,8]]
def classifier_custom_kernel (C=1,dim=7):
- from modshogun import RealFeatures, BinaryLabels, CustomKernel, LibSVM
+ from shogun import RealFeatures, BinaryLabels, CustomKernel, LibSVM
from numpy import diag,ones,sign
from numpy.random import rand,seed
diff --git a/examples/undocumented/python/classifier_domainadaptationsvm.py b/examples/undocumented/python/classifier_domainadaptationsvm.py
index 4c4baed4df6..a92f7f98f98 100644
--- a/examples/undocumented/python/classifier_domainadaptationsvm.py
+++ b/examples/undocumented/python/classifier_domainadaptationsvm.py
@@ -1,17 +1,17 @@
#!/usr/bin/env python
import numpy
-from modshogun import StringCharFeatures, BinaryLabels, DNA
-from modshogun import WeightedDegreeStringKernel
-from modshogun import MSG_DEBUG
+from shogun import StringCharFeatures, BinaryLabels, DNA
+from shogun import WeightedDegreeStringKernel
+from shogun import MSG_DEBUG
try:
- from modshogun import DomainAdaptationSVM
+ from shogun import DomainAdaptationSVM
except ImportError:
print("DomainAdaptationSVM not available")
exit(0)
try:
- from modshogun import SVMLight
+ from shogun import SVMLight
except ImportError:
print("SVMLight not available")
exit(0)
diff --git a/examples/undocumented/python/classifier_featureblock_logistic_regression.py b/examples/undocumented/python/classifier_featureblock_logistic_regression.py
index 2f52c02b1f0..1e4da7837fa 100644
--- a/examples/undocumented/python/classifier_featureblock_logistic_regression.py
+++ b/examples/undocumented/python/classifier_featureblock_logistic_regression.py
@@ -12,9 +12,9 @@
def classifier_featureblock_logistic_regression (fm_train=traindat,fm_test=testdat,label_train=label_traindat):
- from modshogun import BinaryLabels, RealFeatures, IndexBlock, IndexBlockGroup
+ from shogun import BinaryLabels, RealFeatures, IndexBlock, IndexBlockGroup
try:
- from modshogun import FeatureBlockLogisticRegression
+ from shogun import FeatureBlockLogisticRegression
except ImportError:
print("FeatureBlockLogisticRegression not available")
exit(0)
diff --git a/examples/undocumented/python/classifier_gmnpsvm.py b/examples/undocumented/python/classifier_gmnpsvm.py
index 2fc34b8c658..a3a1afb3f07 100644
--- a/examples/undocumented/python/classifier_gmnpsvm.py
+++ b/examples/undocumented/python/classifier_gmnpsvm.py
@@ -6,8 +6,8 @@
parameter_list = [[traindat,testdat,label_traindat,2.1,1,1e-5],[traindat,testdat,label_traindat,2.2,1,1e-5]]
def classifier_gmnpsvm (train_fname=traindat,test_fname=testdat,label_fname=label_traindat,width=2.1,C=1,epsilon=1e-5):
- from modshogun import RealFeatures, MulticlassLabels
- from modshogun import GaussianKernel, GMNPSVM, CSVFile
+ from shogun import RealFeatures, MulticlassLabels
+ from shogun import GaussianKernel, GMNPSVM, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/classifier_gpbtsvm.py b/examples/undocumented/python/classifier_gpbtsvm.py
index 814798c16f6..f74d7ed26b0 100644
--- a/examples/undocumented/python/classifier_gpbtsvm.py
+++ b/examples/undocumented/python/classifier_gpbtsvm.py
@@ -6,11 +6,11 @@
parameter_list = [[traindat,testdat,label_traindat,2.1,1,1e-5],[traindat,testdat,label_traindat,2.2,1,1e-5]]
def classifier_gpbtsvm (train_fname=traindat,test_fname=testdat,label_fname=label_traindat,width=2.1,C=1,epsilon=1e-5):
- from modshogun import RealFeatures, BinaryLabels
- from modshogun import GaussianKernel
- from modshogun import CSVFile
+ from shogun import RealFeatures, BinaryLabels
+ from shogun import GaussianKernel
+ from shogun import CSVFile
try:
- from modshogun import GPBTSVM
+ from shogun import GPBTSVM
except ImportError:
print("GPBTSVM not available")
exit(0)
diff --git a/examples/undocumented/python/classifier_larank.py b/examples/undocumented/python/classifier_larank.py
index cd3b1784089..a6a152d0f35 100644
--- a/examples/undocumented/python/classifier_larank.py
+++ b/examples/undocumented/python/classifier_larank.py
@@ -3,10 +3,10 @@
parameter_list = [[10,3,15,0.9,1,2000,1],[20,4,15,0.9,1,5000,2]]
def classifier_larank (num_vec,num_class,distance,C=0.9,num_threads=1,num_iter=5,seed=1):
- from modshogun import RealFeatures, MulticlassLabels
- from modshogun import GaussianKernel
- from modshogun import LaRank
- from modshogun import Math_init_random
+ from shogun import RealFeatures, MulticlassLabels
+ from shogun import GaussianKernel
+ from shogun import LaRank
+ from shogun import Math_init_random
# reproducible results
Math_init_random(seed)
diff --git a/examples/undocumented/python/classifier_lda.py b/examples/undocumented/python/classifier_lda.py
index c6e3e1ee214..917c1a8b2a7 100644
--- a/examples/undocumented/python/classifier_lda.py
+++ b/examples/undocumented/python/classifier_lda.py
@@ -6,7 +6,7 @@
parameter_list = [[traindat,testdat,label_traindat,3,1],[traindat,testdat,label_traindat,4,1]]
def classifier_lda (train_fname=traindat,test_fname=testdat,label_fname=label_traindat,gamma=3,num_threads=1):
- from modshogun import RealFeatures, BinaryLabels, LDA, CSVFile
+ from shogun import RealFeatures, BinaryLabels, LDA, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/classifier_libsvmoneclass.py b/examples/undocumented/python/classifier_libsvmoneclass.py
index b3e20133bbd..43126195910 100644
--- a/examples/undocumented/python/classifier_libsvmoneclass.py
+++ b/examples/undocumented/python/classifier_libsvmoneclass.py
@@ -5,7 +5,7 @@
parameter_list = [[traindat,testdat,2.2,1,1e-7],[traindat,testdat,2.1,1,1e-5]]
def classifier_libsvmoneclass (train_fname=traindat,test_fname=testdat,width=2.1,C=1,epsilon=1e-5):
- from modshogun import RealFeatures, GaussianKernel, LibSVMOneClass, CSVFile
+ from shogun import RealFeatures, GaussianKernel, LibSVMOneClass, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/classifier_mpdsvm.py b/examples/undocumented/python/classifier_mpdsvm.py
index 1ad5251fbda..eb05a6ea52b 100644
--- a/examples/undocumented/python/classifier_mpdsvm.py
+++ b/examples/undocumented/python/classifier_mpdsvm.py
@@ -7,9 +7,9 @@
def classifier_mpdsvm (train_fname=traindat,test_fname=testdat,label_fname=label_traindat,C=1,epsilon=1e-5):
- from modshogun import RealFeatures, BinaryLabels
- from modshogun import GaussianKernel
- from modshogun import MPDSVM, CSVFile
+ from shogun import RealFeatures, BinaryLabels
+ from shogun import GaussianKernel
+ from shogun import MPDSVM, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/classifier_multiclass_ecoc.py b/examples/undocumented/python/classifier_multiclass_ecoc.py
index b80b4a9c557..4c6311c3b1e 100644
--- a/examples/undocumented/python/classifier_multiclass_ecoc.py
+++ b/examples/undocumented/python/classifier_multiclass_ecoc.py
@@ -12,21 +12,21 @@
def classifier_multiclass_ecoc (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,label_test_multiclass=label_testdat,lawidth=2.1,C=1,epsilon=1e-5):
- import modshogun
- from modshogun import ECOCStrategy, LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine
- from modshogun import MulticlassAccuracy
- from modshogun import RealFeatures, MulticlassLabels
+ import shogun
+ from shogun import ECOCStrategy, LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine
+ from shogun import MulticlassAccuracy
+ from shogun import RealFeatures, MulticlassLabels
def nonabstract_class(name):
try:
- getattr(modshogun, name)()
+ getattr(shogun, name)()
except TypeError:
return False
return True
- encoders = [x for x in dir(modshogun)
+ encoders = [x for x in dir(shogun)
if re.match(r'ECOC.+Encoder', x) and nonabstract_class(x)]
- decoders = [x for x in dir(modshogun)
+ decoders = [x for x in dir(shogun)
if re.match(r'ECOC.+Decoder', x) and nonabstract_class(x)]
fea_train = RealFeatures(fm_train_real)
@@ -46,8 +46,8 @@ def nonabstract_class(name):
#print((format_str % ('s', 's', 's')) % ('encoder', 'decoder', 'codelen', 'time', 'accuracy'))
def run_ecoc(ier, idr):
- encoder = getattr(modshogun, encoders[ier])()
- decoder = getattr(modshogun, decoders[idr])()
+ encoder = getattr(shogun, encoders[ier])()
+ decoder = getattr(shogun, decoders[idr])()
# whether encoder is data dependent
if hasattr(encoder, 'set_labels'):
diff --git a/examples/undocumented/python/classifier_multiclassliblinear.py b/examples/undocumented/python/classifier_multiclassliblinear.py
index a0e63d96b59..cd6be65156e 100644
--- a/examples/undocumented/python/classifier_multiclassliblinear.py
+++ b/examples/undocumented/python/classifier_multiclassliblinear.py
@@ -6,8 +6,8 @@
parameter_list = [[traindat,testdat,label_traindat,label_testdat,2.1,1,1e-5],[traindat,testdat,label_traindat,label_testdat,2.2,1,1e-5]]
def classifier_multiclassliblinear (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,label_test_multiclass=label_testdat,width=2.1,C=1,epsilon=1e-5):
- from modshogun import RealFeatures, MulticlassLabels
- from modshogun import MulticlassLibLinear
+ from shogun import RealFeatures, MulticlassLabels
+ from shogun import MulticlassLibLinear
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
@@ -21,7 +21,7 @@ def classifier_multiclassliblinear (fm_train_real=traindat,fm_test_real=testdat,
out = label_pred.get_labels()
if label_test_multiclass is not None:
- from modshogun import MulticlassAccuracy
+ from shogun import MulticlassAccuracy
labels_test = MulticlassLabels(label_test_multiclass)
evaluator = MulticlassAccuracy()
acc = evaluator.evaluate(label_pred, labels_test)
diff --git a/examples/undocumented/python/classifier_multiclassmachine.py b/examples/undocumented/python/classifier_multiclassmachine.py
index 9ffddb838be..1f670b084f6 100644
--- a/examples/undocumented/python/classifier_multiclassmachine.py
+++ b/examples/undocumented/python/classifier_multiclassmachine.py
@@ -6,9 +6,9 @@
parameter_list = [[traindat,testdat,label_traindat,2.1,1,1e-5],[traindat,testdat,label_traindat,2.2,1,1e-5]]
def classifier_multiclassmachine (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,width=2.1,C=1,epsilon=1e-5):
- from modshogun import RealFeatures, MulticlassLabels
- from modshogun import GaussianKernel
- from modshogun import LibSVM, KernelMulticlassMachine, MulticlassOneVsRestStrategy
+ from shogun import RealFeatures, MulticlassLabels
+ from shogun import GaussianKernel
+ from shogun import LibSVM, KernelMulticlassMachine, MulticlassOneVsRestStrategy
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
diff --git a/examples/undocumented/python/classifier_multiclassocas.py b/examples/undocumented/python/classifier_multiclassocas.py
index f423b79004e..f4984854f0d 100644
--- a/examples/undocumented/python/classifier_multiclassocas.py
+++ b/examples/undocumented/python/classifier_multiclassocas.py
@@ -3,10 +3,10 @@
parameter_list = [[10,3,15,2.1,1,1e-5,1],[20,4,15,2.2,2,1e-5,2]]
def classifier_multiclassocas (num_vec=10,num_class=3,distance=15,width=2.1,C=1,epsilon=1e-5,seed=1):
- from modshogun import RealFeatures, MulticlassLabels
- from modshogun import Math_init_random
+ from shogun import RealFeatures, MulticlassLabels
+ from shogun import Math_init_random
try:
- from modshogun import MulticlassOCAS
+ from shogun import MulticlassOCAS
except ImportError:
print("MulticlassOCAS not available")
return
diff --git a/examples/undocumented/python/classifier_multilabeloutputliblinear.py b/examples/undocumented/python/classifier_multilabeloutputliblinear.py
index 3c191cd2369..57099cf9ec2 100644
--- a/examples/undocumented/python/classifier_multilabeloutputliblinear.py
+++ b/examples/undocumented/python/classifier_multilabeloutputliblinear.py
@@ -6,8 +6,8 @@
parameter_list = [[traindat,testdat,label_traindat,label_testdat,2.1,1,1e-5],[traindat,testdat,label_traindat,label_testdat,2.2,1,1e-5]]
def classifier_multilabeloutputliblinear (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,label_test_multiclass=label_testdat,width=2.1,C=1,epsilon=1e-5):
- from modshogun import RealFeatures, MulticlassLabels, MultilabelLabels
- from modshogun import MulticlassLibLinear
+ from shogun import RealFeatures, MulticlassLabels, MultilabelLabels
+ from shogun import MulticlassLibLinear
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
diff --git a/examples/undocumented/python/classifier_perceptron.py b/examples/undocumented/python/classifier_perceptron.py
index a31ec7869a5..7d6743f40a5 100644
--- a/examples/undocumented/python/classifier_perceptron.py
+++ b/examples/undocumented/python/classifier_perceptron.py
@@ -5,8 +5,8 @@
parameter_list = [[100, 2, 5,1.,1000,1,1], [100, 2, 5,1.,1000,1,2]]
def classifier_perceptron (n=100, dim=2, distance=5,learn_rate=1.,max_iter=1000,num_threads=1,seed=1):
- from modshogun import RealFeatures, BinaryLabels
- from modshogun import Perceptron
+ from shogun import RealFeatures, BinaryLabels
+ from shogun import Perceptron
random.seed(seed)
diff --git a/examples/undocumented/python/classifier_ssk.py b/examples/undocumented/python/classifier_ssk.py
index 0f87a342744..ccb983a62b7 100644
--- a/examples/undocumented/python/classifier_ssk.py
+++ b/examples/undocumented/python/classifier_ssk.py
@@ -20,9 +20,9 @@
def classifier_ssk (fm_train_dna=traindat,fm_test_dna=testdat,
label_train_dna=label_traindat,C=1,maxlen=1,decay=1):
- from modshogun import StringCharFeatures, BinaryLabels
- from modshogun import LibSVM, SubsequenceStringKernel, DNA
- from modshogun import ErrorRateMeasure
+ from shogun import StringCharFeatures, BinaryLabels
+ from shogun import LibSVM, SubsequenceStringKernel, DNA
+ from shogun import ErrorRateMeasure
feats_train=StringCharFeatures(fm_train_dna, DNA)
feats_test=StringCharFeatures(fm_test_dna, DNA)
diff --git a/examples/undocumented/python/classifier_svmlight.py b/examples/undocumented/python/classifier_svmlight.py
index 273a0b75009..156c7be45e4 100644
--- a/examples/undocumented/python/classifier_svmlight.py
+++ b/examples/undocumented/python/classifier_svmlight.py
@@ -9,10 +9,10 @@
parameter_list = [[traindat,testdat,label_traindat,1.1,1e-5,1],[traindat,testdat,label_traindat,1.2,1e-5,1]]
def classifier_svmlight (fm_train_dna=traindat,fm_test_dna=testdat,label_train_dna=label_traindat,C=1.2,epsilon=1e-5,num_threads=1):
- from modshogun import StringCharFeatures, BinaryLabels, DNA
- from modshogun import WeightedDegreeStringKernel
+ from shogun import StringCharFeatures, BinaryLabels, DNA
+ from shogun import WeightedDegreeStringKernel
try:
- from modshogun import SVMLight
+ from shogun import SVMLight
except ImportError:
print('No support for SVMLight available.')
return
diff --git a/examples/undocumented/python/classifier_svmlight_batch_linadd.py b/examples/undocumented/python/classifier_svmlight_batch_linadd.py
index 6181c498be3..abe957423aa 100644
--- a/examples/undocumented/python/classifier_svmlight_batch_linadd.py
+++ b/examples/undocumented/python/classifier_svmlight_batch_linadd.py
@@ -12,10 +12,10 @@
def classifier_svmlight_batch_linadd (fm_train_dna, fm_test_dna,
label_train_dna, degree, C, epsilon, num_threads):
- from modshogun import StringCharFeatures, BinaryLabels, DNA
- from modshogun import WeightedDegreeStringKernel, MSG_DEBUG
+ from shogun import StringCharFeatures, BinaryLabels, DNA
+ from shogun import WeightedDegreeStringKernel, MSG_DEBUG
try:
- from modshogun import SVMLight
+ from shogun import SVMLight
except ImportError:
print('No support for SVMLight available.')
return
diff --git a/examples/undocumented/python/classifier_svmlight_linear_term.py b/examples/undocumented/python/classifier_svmlight_linear_term.py
index 20e9949dce7..fa319153f74 100644
--- a/examples/undocumented/python/classifier_svmlight_linear_term.py
+++ b/examples/undocumented/python/classifier_svmlight_linear_term.py
@@ -30,10 +30,10 @@ def classifier_svmlight_linear_term (fm_train_dna=traindna,fm_test_dna=testdna,
label_train_dna=label_traindna,degree=3, \
C=10,epsilon=1e-5,num_threads=1):
- from modshogun import StringCharFeatures, BinaryLabels, DNA
- from modshogun import WeightedDegreeStringKernel
+ from shogun import StringCharFeatures, BinaryLabels, DNA
+ from shogun import WeightedDegreeStringKernel
try:
- from modshogun import SVMLight
+ from shogun import SVMLight
except ImportError:
print("SVMLight is not available")
exit(0)
diff --git a/examples/undocumented/python/classifier_svmlin.py b/examples/undocumented/python/classifier_svmlin.py
index 3526fc87fd7..c35990fe6bf 100644
--- a/examples/undocumented/python/classifier_svmlin.py
+++ b/examples/undocumented/python/classifier_svmlin.py
@@ -6,8 +6,8 @@
parameter_list = [[traindat,testdat,label_traindat,0.9,1e-5,1],[traindat,testdat,label_traindat,0.8,1e-5,1]]
def classifier_svmlin (train_fname=traindat,test_fname=testdat,label_fname=label_traindat,C=0.9,epsilon=1e-5,num_threads=1):
- from modshogun import RealFeatures, SparseRealFeatures, BinaryLabels
- from modshogun import SVMLin, CSVFile
+ from shogun import RealFeatures, SparseRealFeatures, BinaryLabels
+ from shogun import SVMLin, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/classifier_svmocas.py b/examples/undocumented/python/classifier_svmocas.py
index 0ebd7848125..69d9328d8fd 100644
--- a/examples/undocumented/python/classifier_svmocas.py
+++ b/examples/undocumented/python/classifier_svmocas.py
@@ -6,10 +6,10 @@
parameter_list = [[traindat,testdat,label_traindat,0.9,1e-5,1],[traindat,testdat,label_traindat,0.8,1e-5,1]]
def classifier_svmocas (train_fname=traindat,test_fname=testdat,label_fname=label_traindat,C=0.9,epsilon=1e-5,num_threads=1):
- from modshogun import RealFeatures, BinaryLabels
- from modshogun import CSVFile
+ from shogun import RealFeatures, BinaryLabels
+ from shogun import CSVFile
try:
- from modshogun import SVMOcas
+ from shogun import SVMOcas
except ImportError:
print("SVMOcas not available")
return
diff --git a/examples/undocumented/python/classifier_svmsgd.py b/examples/undocumented/python/classifier_svmsgd.py
index 0996ab322b7..de289376aff 100644
--- a/examples/undocumented/python/classifier_svmsgd.py
+++ b/examples/undocumented/python/classifier_svmsgd.py
@@ -6,8 +6,8 @@
parameter_list = [[traindat,testdat,label_traindat,0.9,1,6],[traindat,testdat,label_traindat,0.8,1,5]]
def classifier_svmsgd (train_fname=traindat,test_fname=testdat,label_fname=label_traindat,C=0.9,num_threads=1,num_iter=5):
- from modshogun import RealFeatures, SparseRealFeatures, BinaryLabels
- from modshogun import SVMSGD, CSVFile
+ from shogun import RealFeatures, SparseRealFeatures, BinaryLabels
+ from shogun import SVMSGD, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/converter_diffusionmaps.py b/examples/undocumented/python/converter_diffusionmaps.py
index c66ddb8f44b..ce134aae07f 100644
--- a/examples/undocumented/python/converter_diffusionmaps.py
+++ b/examples/undocumented/python/converter_diffusionmaps.py
@@ -4,7 +4,7 @@
def converter_diffusionmaps (data_fname,t):
try:
- from modshogun import RealFeatures, DiffusionMaps, GaussianKernel, CSVFile
+ from shogun import RealFeatures, DiffusionMaps, GaussianKernel, CSVFile
features = RealFeatures(CSVFile(data_fname))
diff --git a/examples/undocumented/python/converter_factoranalysis.py b/examples/undocumented/python/converter_factoranalysis.py
index 37c8182b3d1..775a8aa09a1 100644
--- a/examples/undocumented/python/converter_factoranalysis.py
+++ b/examples/undocumented/python/converter_factoranalysis.py
@@ -5,7 +5,7 @@
def converter_factoranalysis(data_fname):
try:
import numpy
- from modshogun import RealFeatures, FactorAnalysis, EuclideanDistance, CSVFile
+ from shogun import RealFeatures, FactorAnalysis, EuclideanDistance, CSVFile
features = RealFeatures(CSVFile(data_fname))
diff --git a/examples/undocumented/python/converter_hasheddoc.py b/examples/undocumented/python/converter_hasheddoc.py
index d9f7e76d802..99299a3a964 100644
--- a/examples/undocumented/python/converter_hasheddoc.py
+++ b/examples/undocumented/python/converter_hasheddoc.py
@@ -5,9 +5,9 @@
parameter_list=[[strings]]
def converter_hasheddoc(strings):
- from modshogun import SparseRealFeatures, RAWBYTE, StringCharFeatures, Features, HashedDocDotFeatures
- from modshogun import NGramTokenizer
- from modshogun import HashedDocConverter
+ from shogun import SparseRealFeatures, RAWBYTE, StringCharFeatures, Features, HashedDocDotFeatures
+ from shogun import NGramTokenizer
+ from shogun import HashedDocConverter
from numpy import array
#create string features
diff --git a/examples/undocumented/python/converter_hessianlocallylinearembedding.py b/examples/undocumented/python/converter_hessianlocallylinearembedding.py
index ff124d83c37..61c07f43926 100644
--- a/examples/undocumented/python/converter_hessianlocallylinearembedding.py
+++ b/examples/undocumented/python/converter_hessianlocallylinearembedding.py
@@ -4,9 +4,9 @@
def converter_hessianlocallylinearembedding (data_fname,k):
try:
- from modshogun import RealFeatures, CSVFile
+ from shogun import RealFeatures, CSVFile
try:
- from modshogun import HessianLocallyLinearEmbedding
+ from shogun import HessianLocallyLinearEmbedding
except ImportError:
print("HessianLocallyLinearEmbedding not available")
exit(0)
diff --git a/examples/undocumented/python/converter_isomap.py b/examples/undocumented/python/converter_isomap.py
index 17ff5993ac3..fb41999abd9 100644
--- a/examples/undocumented/python/converter_isomap.py
+++ b/examples/undocumented/python/converter_isomap.py
@@ -3,8 +3,8 @@
parameter_list = [[data]]
def converter_isomap (data_fname):
- from modshogun import RealFeatures, CSVFile
- from modshogun import Isomap
+ from shogun import RealFeatures, CSVFile
+ from shogun import Isomap
features = RealFeatures(CSVFile(data))
diff --git a/examples/undocumented/python/converter_kernellocallylinearembedding.py b/examples/undocumented/python/converter_kernellocallylinearembedding.py
index c81d2918e2b..b8cf370b7a8 100644
--- a/examples/undocumented/python/converter_kernellocallylinearembedding.py
+++ b/examples/undocumented/python/converter_kernellocallylinearembedding.py
@@ -4,9 +4,9 @@
def converter_kernellocallylinearembedding (data_fname,k):
try:
- from modshogun import RealFeatures, LinearKernel, CSVFile
+ from shogun import RealFeatures, LinearKernel, CSVFile
try:
- from modshogun import KernelLocallyLinearEmbedding
+ from shogun import KernelLocallyLinearEmbedding
except ImportError:
print("KernelLocallyLinearEmbedding not available")
exit(0)
diff --git a/examples/undocumented/python/converter_laplacianeigenmaps.py b/examples/undocumented/python/converter_laplacianeigenmaps.py
index 8016315f51c..810ded4cb55 100644
--- a/examples/undocumented/python/converter_laplacianeigenmaps.py
+++ b/examples/undocumented/python/converter_laplacianeigenmaps.py
@@ -4,9 +4,9 @@
def converter_laplacianeigenmaps (data_fname,k):
try:
- from modshogun import RealFeatures, CSVFile
+ from shogun import RealFeatures, CSVFile
try:
- from modshogun import LaplacianEigenmaps
+ from shogun import LaplacianEigenmaps
except ImportError:
print("LaplacianEigenmaps not available")
exit(0)
diff --git a/examples/undocumented/python/converter_linearlocaltangentspacealignment.py b/examples/undocumented/python/converter_linearlocaltangentspacealignment.py
index 47d81f19992..65e7218c963 100644
--- a/examples/undocumented/python/converter_linearlocaltangentspacealignment.py
+++ b/examples/undocumented/python/converter_linearlocaltangentspacealignment.py
@@ -4,9 +4,9 @@
def converter_linearlocaltangentspacealignment (data_fname,k):
try:
- from modshogun import RealFeatures, CSVFile
+ from shogun import RealFeatures, CSVFile
try:
- from modshogun import LinearLocalTangentSpaceAlignment
+ from shogun import LinearLocalTangentSpaceAlignment
except ImportError:
print("LinearLocalTangentSpaceAlignment not available")
exit(0)
diff --git a/examples/undocumented/python/converter_localitypreservingprojections.py b/examples/undocumented/python/converter_localitypreservingprojections.py
index c050a9c2c9d..2ff78bae35f 100644
--- a/examples/undocumented/python/converter_localitypreservingprojections.py
+++ b/examples/undocumented/python/converter_localitypreservingprojections.py
@@ -3,8 +3,8 @@
parameter_list = [[data,20],[data,30]]
def converter_localitypreservingprojections (data_fname,k):
- from modshogun import RealFeatures, CSVFile
- from modshogun import LocalityPreservingProjections
+ from shogun import RealFeatures, CSVFile
+ from shogun import LocalityPreservingProjections
features = RealFeatures(CSVFile(data_fname))
converter = LocalityPreservingProjections()
diff --git a/examples/undocumented/python/converter_locallylinearembedding.py b/examples/undocumented/python/converter_locallylinearembedding.py
index cd1ae691de9..1c85d3ffd11 100644
--- a/examples/undocumented/python/converter_locallylinearembedding.py
+++ b/examples/undocumented/python/converter_locallylinearembedding.py
@@ -4,9 +4,9 @@
def converter_locallylinearembedding (data_fname,k):
try:
- from modshogun import RealFeatures, CSVFile
+ from shogun import RealFeatures, CSVFile
try:
- from modshogun import LocallyLinearEmbedding
+ from shogun import LocallyLinearEmbedding
except ImportError:
print("LocallyLinearEmbedding not available")
exit(0)
diff --git a/examples/undocumented/python/converter_localtangentspacealignment.py b/examples/undocumented/python/converter_localtangentspacealignment.py
index 81b8e29ffd1..d47e706b5d7 100644
--- a/examples/undocumented/python/converter_localtangentspacealignment.py
+++ b/examples/undocumented/python/converter_localtangentspacealignment.py
@@ -4,9 +4,9 @@
def converter_localtangentspacealignment (data_fname,k):
try:
- from modshogun import RealFeatures, CSVFile
+ from shogun import RealFeatures, CSVFile
try:
- from modshogun import LocalTangentSpaceAlignment
+ from shogun import LocalTangentSpaceAlignment
except ImportError:
print("LocalTangentSpaceAlignment not available")
exit(0)
diff --git a/examples/undocumented/python/converter_multidimensionalscaling.py b/examples/undocumented/python/converter_multidimensionalscaling.py
index 2a2c16b57c9..8acaac5de4a 100644
--- a/examples/undocumented/python/converter_multidimensionalscaling.py
+++ b/examples/undocumented/python/converter_multidimensionalscaling.py
@@ -5,7 +5,7 @@
def converter_multidimensionalscaling (data_fname):
try:
import numpy
- from modshogun import RealFeatures, MultidimensionalScaling, EuclideanDistance, CSVFile
+ from shogun import RealFeatures, MultidimensionalScaling, EuclideanDistance, CSVFile
features = RealFeatures(CSVFile(data_fname))
diff --git a/examples/undocumented/python/converter_stochasticproximityembedding.py b/examples/undocumented/python/converter_stochasticproximityembedding.py
index cd4aea9a86f..16684987d13 100644
--- a/examples/undocumented/python/converter_stochasticproximityembedding.py
+++ b/examples/undocumented/python/converter_stochasticproximityembedding.py
@@ -4,7 +4,7 @@
def converter_stochasticproximityembedding (data_fname, k):
try:
- from modshogun import RealFeatures,StochasticProximityEmbedding, SPE_GLOBAL, SPE_LOCAL, CSVFile
+ from shogun import RealFeatures,StochasticProximityEmbedding, SPE_GLOBAL, SPE_LOCAL, CSVFile
features = RealFeatures(CSVFile(data_fname))
diff --git a/examples/undocumented/python/converter_tdistributedstochasticneighborembedding.py b/examples/undocumented/python/converter_tdistributedstochasticneighborembedding.py
index ebfe9ccbaff..9d93083ec32 100644
--- a/examples/undocumented/python/converter_tdistributedstochasticneighborembedding.py
+++ b/examples/undocumented/python/converter_tdistributedstochasticneighborembedding.py
@@ -4,8 +4,8 @@
def converter_tdistributedstochasticneighborembedding(data_fname, seed=1):
try:
- from modshogun import RealFeatures, TDistributedStochasticNeighborEmbedding
- from modshogun import Math_init_random, CSVFile
+ from shogun import RealFeatures, TDistributedStochasticNeighborEmbedding
+ from shogun import Math_init_random, CSVFile
# reproducible results
Math_init_random(seed)
diff --git a/examples/undocumented/python/distance_canberra.py b/examples/undocumented/python/distance_canberra.py
index 2a20024ae7f..6c006827a54 100644
--- a/examples/undocumented/python/distance_canberra.py
+++ b/examples/undocumented/python/distance_canberra.py
@@ -5,7 +5,7 @@
parameter_list = [[traindat,testdat],[traindat,testdat]]
def distance_canberra (train_fname=traindat,test_fname=testdat):
- from modshogun import RealFeatures, CanberraMetric, CSVFile
+ from shogun import RealFeatures, CanberraMetric, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/distance_canberraword.py b/examples/undocumented/python/distance_canberraword.py
index 7ced3bb7ef7..b01198783b6 100644
--- a/examples/undocumented/python/distance_canberraword.py
+++ b/examples/undocumented/python/distance_canberraword.py
@@ -9,9 +9,9 @@
parameter_list = [[traindna,testdna,3,0,False],[traindna,testdna,3,0,False]]
def distance_canberraword (fm_train_dna=traindna,fm_test_dna=testdna,order=3,gap=0,reverse=False):
- from modshogun import StringCharFeatures, StringWordFeatures, DNA
- from modshogun import SortWordString
- from modshogun import CanberraWordDistance
+ from shogun import StringCharFeatures, StringWordFeatures, DNA
+ from shogun import SortWordString
+ from shogun import CanberraWordDistance
charfeat=StringCharFeatures(DNA)
charfeat.set_features(fm_train_dna)
diff --git a/examples/undocumented/python/distance_chebyshew.py b/examples/undocumented/python/distance_chebyshew.py
index e2e1e1c8a55..1a0a0bb6164 100644
--- a/examples/undocumented/python/distance_chebyshew.py
+++ b/examples/undocumented/python/distance_chebyshew.py
@@ -5,7 +5,7 @@
parameter_list = [[traindat,testdat],[traindat,testdat]]
def distance_chebyshew (train_fname=traindat,test_fname=testdat):
- from modshogun import RealFeatures, ChebyshewMetric, CSVFile
+ from shogun import RealFeatures, ChebyshewMetric, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/distance_chisquare.py b/examples/undocumented/python/distance_chisquare.py
index 24673c8eb2e..ce8588b4a34 100644
--- a/examples/undocumented/python/distance_chisquare.py
+++ b/examples/undocumented/python/distance_chisquare.py
@@ -5,7 +5,7 @@
parameter_list = [[traindat,testdat,],[traindat,testdat]]
def distance_chisquare (train_fname=traindat,test_fname=testdat):
- from modshogun import RealFeatures, ChiSquareDistance, CSVFile
+ from shogun import RealFeatures, ChiSquareDistance, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/distance_director_euclidean.py b/examples/undocumented/python/distance_director_euclidean.py
index 96b137f3c30..deab1aa6aee 100644
--- a/examples/undocumented/python/distance_director_euclidean.py
+++ b/examples/undocumented/python/distance_director_euclidean.py
@@ -1,6 +1,6 @@
#!/usr/bin/env python
import numpy
-from modshogun import RealFeatures, MSG_DEBUG
+from shogun import RealFeatures, MSG_DEBUG
numpy.random.seed(17)
traindat = numpy.random.random_sample((10,10))
@@ -9,7 +9,7 @@
def distance_director_euclidean (fm_train_real=traindat,fm_test_real=testdat,scale=1.2):
try:
- from modshogun import DirectorDistance
+ from shogun import DirectorDistance
except ImportError:
print("recompile shogun with --enable-swig-directors")
return
@@ -22,8 +22,8 @@ def distance_function(self, idx_a, idx_b):
seq2 = self.get_rhs().get_feature_vector(idx_b)
return numpy.linalg.norm(seq1-seq2)
- from modshogun import EuclideanDistance
- from modshogun import Time
+ from shogun import EuclideanDistance
+ from shogun import Time
feats_train=RealFeatures(fm_train_real)
#feats_train.io.set_loglevel(MSG_DEBUG)
diff --git a/examples/undocumented/python/distance_geodesic.py b/examples/undocumented/python/distance_geodesic.py
index 3c49193ca21..eab7ba89811 100644
--- a/examples/undocumented/python/distance_geodesic.py
+++ b/examples/undocumented/python/distance_geodesic.py
@@ -6,7 +6,7 @@
def distance_geodesic (train_fname=traindat,test_fname=testdat):
- from modshogun import RealFeatures, GeodesicMetric, CSVFile
+ from shogun import RealFeatures, GeodesicMetric, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/distance_hammingword.py b/examples/undocumented/python/distance_hammingword.py
index 7dc32478e1e..e0534c0cbf9 100644
--- a/examples/undocumented/python/distance_hammingword.py
+++ b/examples/undocumented/python/distance_hammingword.py
@@ -12,9 +12,9 @@
def distance_hammingword (fm_train_dna=traindna,fm_test_dna=testdna,
fm_test_real=testdat,order=3,gap=0,reverse=False,use_sign=False):
- from modshogun import StringCharFeatures, StringWordFeatures, DNA
- from modshogun import SortWordString
- from modshogun import HammingWordDistance
+ from shogun import StringCharFeatures, StringWordFeatures, DNA
+ from shogun import SortWordString
+ from shogun import HammingWordDistance
charfeat=StringCharFeatures(DNA)
charfeat.set_features(fm_train_dna)
diff --git a/examples/undocumented/python/distance_jensen.py b/examples/undocumented/python/distance_jensen.py
index 40e58c66e95..43cd0233a95 100644
--- a/examples/undocumented/python/distance_jensen.py
+++ b/examples/undocumented/python/distance_jensen.py
@@ -6,7 +6,7 @@
def distance_jensen (train_fname=traindat,test_fname=testdat):
- from modshogun import RealFeatures, JensenMetric, CSVFile
+ from shogun import RealFeatures, JensenMetric, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/distance_manhattenword.py b/examples/undocumented/python/distance_manhattenword.py
index 18f925aa164..e377f757285 100644
--- a/examples/undocumented/python/distance_manhattenword.py
+++ b/examples/undocumented/python/distance_manhattenword.py
@@ -5,8 +5,8 @@
parameter_list = [[traindna,testdna,3,0,False],[traindna,testdna,4,0,False]]
def distance_manhattenword (train_fname=traindna,test_fname=testdna,order=3,gap=0,reverse=False):
- from modshogun import StringCharFeatures, StringWordFeatures, DNA
- from modshogun import SortWordString, ManhattanWordDistance, CSVFile
+ from shogun import StringCharFeatures, StringWordFeatures, DNA
+ from shogun import SortWordString, ManhattanWordDistance, CSVFile
charfeat=StringCharFeatures(CSVFile(train_fname), DNA)
feats_train=StringWordFeatures(charfeat.get_alphabet())
diff --git a/examples/undocumented/python/distance_minkowski.py b/examples/undocumented/python/distance_minkowski.py
index 685e643be80..765e9818e9b 100644
--- a/examples/undocumented/python/distance_minkowski.py
+++ b/examples/undocumented/python/distance_minkowski.py
@@ -5,7 +5,7 @@
parameter_list = [[traindat,testdat,3],[traindat,testdat,4]]
def distance_minkowski (train_fname=traindat,test_fname=testdat,k=3):
- from modshogun import RealFeatures, MinkowskiMetric, CSVFile
+ from shogun import RealFeatures, MinkowskiMetric, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/distance_normsquared.py b/examples/undocumented/python/distance_normsquared.py
index 68def2bd218..0e1d435514b 100644
--- a/examples/undocumented/python/distance_normsquared.py
+++ b/examples/undocumented/python/distance_normsquared.py
@@ -5,7 +5,7 @@
parameter_list = [[traindat,testdat],[traindat,testdat]]
def distance_normsquared (train_fname=traindat,test_fname=testdat):
- from modshogun import RealFeatures, EuclideanDistance, CSVFile
+ from shogun import RealFeatures, EuclideanDistance, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/distance_sparseeuclidean.py b/examples/undocumented/python/distance_sparseeuclidean.py
index 2b9ebd0e615..9f2c48eac01 100644
--- a/examples/undocumented/python/distance_sparseeuclidean.py
+++ b/examples/undocumented/python/distance_sparseeuclidean.py
@@ -5,7 +5,7 @@
parameter_list = [[traindat,testdat],[traindat,testdat]]
def distance_sparseeuclidean (train_fname=traindat,test_fname=testdat):
- from modshogun import RealFeatures, SparseRealFeatures, SparseEuclideanDistance, CSVFile
+ from shogun import RealFeatures, SparseRealFeatures, SparseEuclideanDistance, CSVFile
realfeat=RealFeatures(CSVFile(train_fname))
feats_train=SparseRealFeatures()
diff --git a/examples/undocumented/python/distance_tanimoto.py b/examples/undocumented/python/distance_tanimoto.py
index 59d33323bac..471ec4bec2a 100644
--- a/examples/undocumented/python/distance_tanimoto.py
+++ b/examples/undocumented/python/distance_tanimoto.py
@@ -5,7 +5,7 @@
parameter_list = [[traindat,testdat],[traindat,testdat]]
def distance_tanimoto (train_fname=traindat,test_fname=testdat):
- from modshogun import RealFeatures, TanimotoDistance, CSVFile
+ from shogun import RealFeatures, TanimotoDistance, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/distribution_histogram.py b/examples/undocumented/python/distribution_histogram.py
index a1e4acac8f5..52f1752c1f4 100644
--- a/examples/undocumented/python/distribution_histogram.py
+++ b/examples/undocumented/python/distribution_histogram.py
@@ -7,8 +7,8 @@
parameter_list = [[traindna,3,0,False],[traindna,4,0,False]]
def distribution_histogram (fm_dna=traindna,order=3,gap=0,reverse=False):
- from modshogun import StringWordFeatures, StringCharFeatures, DNA
- from modshogun import Histogram
+ from shogun import StringWordFeatures, StringCharFeatures, DNA
+ from shogun import Histogram
charfeat=StringCharFeatures(DNA)
charfeat.set_features(fm_dna)
diff --git a/examples/undocumented/python/distribution_hmm.py b/examples/undocumented/python/distribution_hmm.py
index ae4ce639b51..a3019d33c8e 100644
--- a/examples/undocumented/python/distribution_hmm.py
+++ b/examples/undocumented/python/distribution_hmm.py
@@ -6,8 +6,8 @@
parameter_list=[[data, 1, 64, 1e-5, 2, 0, False, 5], [data, 3, 6, 1e-1, 1, 0, False, 2]]
def distribution_hmm(fm_cube, N, M, pseudo, order, gap, reverse, num_examples):
- from modshogun import StringWordFeatures, StringCharFeatures, CUBE
- from modshogun import HMM, BW_NORMAL
+ from shogun import StringWordFeatures, StringCharFeatures, CUBE
+ from shogun import HMM, BW_NORMAL
charfeat=StringCharFeatures(CUBE)
charfeat.set_features(fm_cube)
diff --git a/examples/undocumented/python/distribution_linearhmm.py b/examples/undocumented/python/distribution_linearhmm.py
index 128f726b88d..defb8e0f366 100644
--- a/examples/undocumented/python/distribution_linearhmm.py
+++ b/examples/undocumented/python/distribution_linearhmm.py
@@ -8,8 +8,8 @@
def distribution_linearhmm (fm_dna=traindna,order=3,gap=0,reverse=False):
- from modshogun import StringWordFeatures, StringCharFeatures, DNA
- from modshogun import LinearHMM
+ from shogun import StringWordFeatures, StringCharFeatures, DNA
+ from shogun import LinearHMM
charfeat=StringCharFeatures(DNA)
charfeat.set_features(fm_dna)
diff --git a/examples/undocumented/python/distribution_ppwm.py b/examples/undocumented/python/distribution_ppwm.py
index a6d9151583f..11097a4f631 100644
--- a/examples/undocumented/python/distribution_ppwm.py
+++ b/examples/undocumented/python/distribution_ppwm.py
@@ -7,8 +7,8 @@
parameter_list = [[traindna,3],[traindna,4]]
def distribution_ppwm (fm_dna=traindna, order=3):
- from modshogun import StringByteFeatures, StringCharFeatures, DNA
- from modshogun import PositionalPWM
+ from shogun import StringByteFeatures, StringCharFeatures, DNA
+ from shogun import PositionalPWM
from numpy import array,e,log,exp
diff --git a/examples/undocumented/python/evaluation_clustering.py b/examples/undocumented/python/evaluation_clustering.py
index 22c5388eb3f..b0376772240 100644
--- a/examples/undocumented/python/evaluation_clustering.py
+++ b/examples/undocumented/python/evaluation_clustering.py
@@ -26,9 +26,9 @@ def prepare_data():
def run_clustering(data, k):
- from modshogun import KMeans
- from modshogun import EuclideanDistance
- from modshogun import RealFeatures
+ from shogun import KMeans
+ from shogun import EuclideanDistance
+ from shogun import RealFeatures
fea = RealFeatures(data)
distance = EuclideanDistance(fea, fea)
@@ -40,9 +40,9 @@ def run_clustering(data, k):
return kmeans.get_cluster_centers()
def assign_labels(data, centroids, ncenters):
- from modshogun import EuclideanDistance
- from modshogun import RealFeatures, MulticlassLabels
- from modshogun import KNN
+ from shogun import EuclideanDistance
+ from shogun import RealFeatures, MulticlassLabels
+ from shogun import KNN
from numpy import arange
labels = MulticlassLabels(arange(0.,ncenters))
@@ -54,9 +54,9 @@ def assign_labels(data, centroids, ncenters):
return knn.apply(fea)
def evaluation_clustering (features=fea, ground_truth=gnd_raw, ncenters=10):
- from modshogun import ClusteringAccuracy, ClusteringMutualInformation
- from modshogun import MulticlassLabels
- from modshogun import Math
+ from shogun import ClusteringAccuracy, ClusteringMutualInformation
+ from shogun import MulticlassLabels
+ from shogun import Math
# reproducable results
Math.init_random(1)
diff --git a/examples/undocumented/python/evaluation_clustering_simple.py b/examples/undocumented/python/evaluation_clustering_simple.py
index a98c9d2c138..fbce11084ad 100644
--- a/examples/undocumented/python/evaluation_clustering_simple.py
+++ b/examples/undocumented/python/evaluation_clustering_simple.py
@@ -5,10 +5,10 @@
#from pylab import *
def run_clustering(data, k):
- from modshogun import KMeans
- from modshogun import Math_init_random
- from modshogun import EuclideanDistance
- from modshogun import RealFeatures
+ from shogun import KMeans
+ from shogun import Math_init_random
+ from shogun import EuclideanDistance
+ from shogun import RealFeatures
fea = RealFeatures(data)
distance = EuclideanDistance(fea, fea)
@@ -20,9 +20,9 @@ def run_clustering(data, k):
return kmeans.get_cluster_centers()
def assign_labels(data, centroids, ncenters):
- from modshogun import EuclideanDistance
- from modshogun import RealFeatures, MulticlassLabels
- from modshogun import KNN
+ from shogun import EuclideanDistance
+ from shogun import RealFeatures, MulticlassLabels
+ from shogun import KNN
from numpy import arange
labels = MulticlassLabels(arange(0.,ncenters))
@@ -34,9 +34,9 @@ def assign_labels(data, centroids, ncenters):
return knn.apply(fea)
def evaluation_clustering_simple (n_data=100, sqrt_num_blobs=4, distance=5):
- from modshogun import ClusteringAccuracy, ClusteringMutualInformation
- from modshogun import MulticlassLabels, GaussianBlobsDataGenerator
- from modshogun import Math
+ from shogun import ClusteringAccuracy, ClusteringMutualInformation
+ from shogun import MulticlassLabels, GaussianBlobsDataGenerator
+ from shogun import Math
# reproducable results
Math.init_random(1)
diff --git a/examples/undocumented/python/evaluation_contingencytableevaluation.py b/examples/undocumented/python/evaluation_contingencytableevaluation.py
index 7496a893c7c..8868e2ca310 100644
--- a/examples/undocumented/python/evaluation_contingencytableevaluation.py
+++ b/examples/undocumented/python/evaluation_contingencytableevaluation.py
@@ -10,11 +10,11 @@
parameter_list = [[ground_truth,predicted]]
def evaluation_contingencytableevaluation (ground_truth, predicted):
- from modshogun import BinaryLabels
- from modshogun import ContingencyTableEvaluation
- from modshogun import AccuracyMeasure,ErrorRateMeasure,BALMeasure
- from modshogun import WRACCMeasure,F1Measure,CrossCorrelationMeasure
- from modshogun import RecallMeasure,PrecisionMeasure,SpecificityMeasure
+ from shogun import BinaryLabels
+ from shogun import ContingencyTableEvaluation
+ from shogun import AccuracyMeasure,ErrorRateMeasure,BALMeasure
+ from shogun import WRACCMeasure,F1Measure,CrossCorrelationMeasure
+ from shogun import RecallMeasure,PrecisionMeasure,SpecificityMeasure
ground_truth_labels = BinaryLabels(ground_truth)
predicted_labels = BinaryLabels(predicted)
diff --git a/examples/undocumented/python/evaluation_cross_validation_classification.py b/examples/undocumented/python/evaluation_cross_validation_classification.py
index deafd88a9e6..5a7def4bace 100644
--- a/examples/undocumented/python/evaluation_cross_validation_classification.py
+++ b/examples/undocumented/python/evaluation_cross_validation_classification.py
@@ -22,12 +22,12 @@
parameter_list = [[traindat,label_traindat]]
def evaluation_cross_validation_classification (traindat=traindat, label_traindat=label_traindat):
- from modshogun import CrossValidation, CrossValidationResult
- from modshogun import ContingencyTableEvaluation, ACCURACY
- from modshogun import StratifiedCrossValidationSplitting
- from modshogun import BinaryLabels
- from modshogun import RealFeatures
- from modshogun import LibLinear, L2R_L2LOSS_SVC
+ from shogun import CrossValidation, CrossValidationResult
+ from shogun import ContingencyTableEvaluation, ACCURACY
+ from shogun import StratifiedCrossValidationSplitting
+ from shogun import BinaryLabels
+ from shogun import RealFeatures
+ from shogun import LibLinear, L2R_L2LOSS_SVC
# training data
features=RealFeatures(traindat)
diff --git a/examples/undocumented/python/evaluation_cross_validation_mkl_weight_storage.py b/examples/undocumented/python/evaluation_cross_validation_mkl_weight_storage.py
index 9c8bd61d398..cf607d35bba 100644
--- a/examples/undocumented/python/evaluation_cross_validation_mkl_weight_storage.py
+++ b/examples/undocumented/python/evaluation_cross_validation_mkl_weight_storage.py
@@ -22,15 +22,15 @@
parameter_list = [[traindat,label_traindat]]
def evaluation_cross_validation_mkl_weight_storage(traindat=traindat, label_traindat=label_traindat):
- from modshogun import CrossValidation, CrossValidationResult
- from modshogun import CrossValidationPrintOutput
- from modshogun import CrossValidationMKLStorage
- from modshogun import ContingencyTableEvaluation, ACCURACY
- from modshogun import StratifiedCrossValidationSplitting
- from modshogun import BinaryLabels
- from modshogun import RealFeatures, CombinedFeatures
- from modshogun import GaussianKernel, CombinedKernel
- from modshogun import LibSVM, MKLClassification
+ from shogun import CrossValidation, CrossValidationResult
+ from shogun import CrossValidationPrintOutput
+ from shogun import CrossValidationMKLStorage
+ from shogun import ContingencyTableEvaluation, ACCURACY
+ from shogun import StratifiedCrossValidationSplitting
+ from shogun import BinaryLabels
+ from shogun import RealFeatures, CombinedFeatures
+ from shogun import GaussianKernel, CombinedKernel
+ from shogun import LibSVM, MKLClassification
# training data, combined features all on same data
features=RealFeatures(traindat)
diff --git a/examples/undocumented/python/evaluation_cross_validation_multiclass_storage.py b/examples/undocumented/python/evaluation_cross_validation_multiclass_storage.py
index 03dad281383..4d2236c5866 100644
--- a/examples/undocumented/python/evaluation_cross_validation_multiclass_storage.py
+++ b/examples/undocumented/python/evaluation_cross_validation_multiclass_storage.py
@@ -23,16 +23,16 @@
parameter_list = [[traindat,label_traindat]]
def evaluation_cross_validation_multiclass_storage (traindat=traindat, label_traindat=label_traindat):
- from modshogun import CrossValidation, CrossValidationResult
- from modshogun import CrossValidationPrintOutput
- from modshogun import CrossValidationMKLStorage, CrossValidationMulticlassStorage
- from modshogun import MulticlassAccuracy, F1Measure
- from modshogun import StratifiedCrossValidationSplitting
- from modshogun import MulticlassLabels
- from modshogun import RealFeatures, CombinedFeatures
- from modshogun import GaussianKernel, CombinedKernel
- from modshogun import MKLMulticlass
- from modshogun import Statistics, MSG_DEBUG, Math
+ from shogun import CrossValidation, CrossValidationResult
+ from shogun import CrossValidationPrintOutput
+ from shogun import CrossValidationMKLStorage, CrossValidationMulticlassStorage
+ from shogun import MulticlassAccuracy, F1Measure
+ from shogun import StratifiedCrossValidationSplitting
+ from shogun import MulticlassLabels
+ from shogun import RealFeatures, CombinedFeatures
+ from shogun import GaussianKernel, CombinedKernel
+ from shogun import MKLMulticlass
+ from shogun import Statistics, MSG_DEBUG, Math
Math.init_random(1)
diff --git a/examples/undocumented/python/evaluation_cross_validation_regression.py b/examples/undocumented/python/evaluation_cross_validation_regression.py
index 45ce5bd5c4b..f124809b6ca 100644
--- a/examples/undocumented/python/evaluation_cross_validation_regression.py
+++ b/examples/undocumented/python/evaluation_cross_validation_regression.py
@@ -14,10 +14,10 @@
parameter_list = [[traindat,label_traindat,0.8,1e-6],[traindat,label_traindat,0.9,1e-7]]
def evaluation_cross_validation_regression (train_fname=traindat,label_fname=label_traindat,width=0.8,tau=1e-6):
- from modshogun import CrossValidation, CrossValidationResult
- from modshogun import MeanSquaredError, CrossValidationSplitting
- from modshogun import RegressionLabels, RealFeatures
- from modshogun import GaussianKernel, KernelRidgeRegression, CSVFile
+ from shogun import CrossValidation, CrossValidationResult
+ from shogun import MeanSquaredError, CrossValidationSplitting
+ from shogun import RegressionLabels, RealFeatures
+ from shogun import GaussianKernel, KernelRidgeRegression, CSVFile
# training data
features=RealFeatures(CSVFile(train_fname))
diff --git a/examples/undocumented/python/evaluation_director_contingencytableevaluation.py b/examples/undocumented/python/evaluation_director_contingencytableevaluation.py
index f8581683a2f..2f839bd3b54 100644
--- a/examples/undocumented/python/evaluation_director_contingencytableevaluation.py
+++ b/examples/undocumented/python/evaluation_director_contingencytableevaluation.py
@@ -11,7 +11,7 @@
def evaluation_director_contingencytableevaluation (ground_truth, predicted):
try:
- from modshogun import DirectorContingencyTableEvaluation, ED_MAXIMIZE
+ from shogun import DirectorContingencyTableEvaluation, ED_MAXIMIZE
except ImportError:
print("recompile shogun with --enable-swig-directors")
return
@@ -24,7 +24,7 @@ def get_custom_direction(self):
def get_custom_score(self):
return self.get_WRACC()+self.get_BAL()
- from modshogun import BinaryLabels
+ from shogun import BinaryLabels
evaluator = SimpleWeightedBinaryEvaluator()
r = evaluator.evaluate(BinaryLabels(ground_truth), BinaryLabels(predicted))
diff --git a/examples/undocumented/python/evaluation_meansquarederror.py b/examples/undocumented/python/evaluation_meansquarederror.py
index b0c3b87d665..141b281f845 100644
--- a/examples/undocumented/python/evaluation_meansquarederror.py
+++ b/examples/undocumented/python/evaluation_meansquarederror.py
@@ -12,8 +12,8 @@
parameter_list = [[ground_truth,predicted]]
def evaluation_meansquarederror (ground_truth, predicted):
- from modshogun import RegressionLabels
- from modshogun import MeanSquaredError
+ from shogun import RegressionLabels
+ from shogun import MeanSquaredError
ground_truth_labels = RegressionLabels(ground_truth)
predicted_labels = RegressionLabels(predicted)
diff --git a/examples/undocumented/python/evaluation_meansquaredlogerror.py b/examples/undocumented/python/evaluation_meansquaredlogerror.py
index ec4946eadc3..b15c4a00b2d 100644
--- a/examples/undocumented/python/evaluation_meansquaredlogerror.py
+++ b/examples/undocumented/python/evaluation_meansquaredlogerror.py
@@ -12,8 +12,8 @@
parameter_list = [[ground_truth,predicted]]
def evaluation_meansquaredlogerror (ground_truth, predicted):
- from modshogun import RegressionLabels
- from modshogun import MeanSquaredLogError
+ from shogun import RegressionLabels
+ from shogun import MeanSquaredLogError
ground_truth_labels = RegressionLabels(ground_truth)
predicted_labels = RegressionLabels(predicted)
diff --git a/examples/undocumented/python/evaluation_multiclassaccuracy.py b/examples/undocumented/python/evaluation_multiclassaccuracy.py
index 747d8a0f02b..3c38486fed9 100644
--- a/examples/undocumented/python/evaluation_multiclassaccuracy.py
+++ b/examples/undocumented/python/evaluation_multiclassaccuracy.py
@@ -10,8 +10,8 @@
parameter_list = [[ground_truth,predicted]]
def evaluation_multiclassaccuracy (ground_truth, predicted):
- from modshogun import MulticlassLabels
- from modshogun import MulticlassAccuracy
+ from shogun import MulticlassLabels
+ from shogun import MulticlassAccuracy
ground_truth_labels = MulticlassLabels(ground_truth)
predicted_labels = MulticlassLabels(predicted)
diff --git a/examples/undocumented/python/evaluation_multiclassovrevaluation.py b/examples/undocumented/python/evaluation_multiclassovrevaluation.py
index 572c44dbe81..0e2f8d6a959 100644
--- a/examples/undocumented/python/evaluation_multiclassovrevaluation.py
+++ b/examples/undocumented/python/evaluation_multiclassovrevaluation.py
@@ -5,9 +5,9 @@
parameter_list = [[traindat, label_traindat]]
def evaluation_multiclassovrevaluation(train_fname=traindat, label_fname=label_traindat):
- from modshogun import MulticlassOVREvaluation,ROCEvaluation
- from modshogun import MulticlassLibLinear,RealFeatures,ContingencyTableEvaluation,ACCURACY
- from modshogun import MulticlassLabels, Math, CSVFile
+ from shogun import MulticlassOVREvaluation,ROCEvaluation
+ from shogun import MulticlassLibLinear,RealFeatures,ContingencyTableEvaluation,ACCURACY
+ from shogun import MulticlassLabels, Math, CSVFile
Math.init_random(1)
ground_truth_labels = MulticlassLabels(CSVFile(label_fname))
diff --git a/examples/undocumented/python/evaluation_prcevaluation.py b/examples/undocumented/python/evaluation_prcevaluation.py
index 1dc94cffe26..6c5325ac66d 100644
--- a/examples/undocumented/python/evaluation_prcevaluation.py
+++ b/examples/undocumented/python/evaluation_prcevaluation.py
@@ -10,8 +10,8 @@
parameter_list = [[ground_truth,predicted]]
def evaluation_prcevaluation (ground_truth, predicted):
- from modshogun import BinaryLabels
- from modshogun import PRCEvaluation
+ from shogun import BinaryLabels
+ from shogun import PRCEvaluation
ground_truth_labels = BinaryLabels(ground_truth)
predicted_labels = BinaryLabels(predicted)
diff --git a/examples/undocumented/python/evaluation_rocevaluation.py b/examples/undocumented/python/evaluation_rocevaluation.py
index 19c1493e078..932c2e009f5 100644
--- a/examples/undocumented/python/evaluation_rocevaluation.py
+++ b/examples/undocumented/python/evaluation_rocevaluation.py
@@ -10,8 +10,8 @@
parameter_list = [[ground_truth,predicted]]
def evaluation_rocevaluation (ground_truth, predicted):
- from modshogun import BinaryLabels
- from modshogun import ROCEvaluation
+ from shogun import BinaryLabels
+ from shogun import ROCEvaluation
ground_truth_labels = BinaryLabels(ground_truth)
predicted_labels = BinaryLabels(predicted)
diff --git a/examples/undocumented/python/evaluation_thresholds.py b/examples/undocumented/python/evaluation_thresholds.py
index 97f2f7e72c2..cd6f1e516b6 100644
--- a/examples/undocumented/python/evaluation_thresholds.py
+++ b/examples/undocumented/python/evaluation_thresholds.py
@@ -2,7 +2,7 @@
parameter_list = [[1000]]
def evaluation_thresholds (index):
- from modshogun import BinaryLabels, ROCEvaluation
+ from shogun import BinaryLabels, ROCEvaluation
import numpy
numpy.random.seed(17)
output=numpy.arange(-1,1,0.001)
diff --git a/examples/undocumented/python/features_binned_dot.py b/examples/undocumented/python/features_binned_dot.py
index f3a8192ffda..a4ef9b18141 100644
--- a/examples/undocumented/python/features_binned_dot.py
+++ b/examples/undocumented/python/features_binned_dot.py
@@ -8,7 +8,7 @@
parameter_list = [(matrix,bins)]
def features_binned_dot (matrix, bins):
- from modshogun import RealFeatures, BinnedDotFeatures
+ from shogun import RealFeatures, BinnedDotFeatures
rf=RealFeatures(matrix)
#print(rf.get_feature_matrix())
diff --git a/examples/undocumented/python/features_dense.py b/examples/undocumented/python/features_dense.py
index 6ffddd83928..136bc216d4a 100644
--- a/examples/undocumented/python/features_dense.py
+++ b/examples/undocumented/python/features_dense.py
@@ -1,5 +1,5 @@
#!/usr/bin/env python
-from modshogun import RealFeatures, LongIntFeatures, ByteFeatures
+from shogun import RealFeatures, LongIntFeatures, ByteFeatures
from numpy import array, float64, int64, uint8, all
# create dense matrices A,B,C
diff --git a/examples/undocumented/python/features_dense_byte.py b/examples/undocumented/python/features_dense_byte.py
index 082b06c4206..f85645529c9 100644
--- a/examples/undocumented/python/features_dense_byte.py
+++ b/examples/undocumented/python/features_dense_byte.py
@@ -7,7 +7,7 @@
parameter_list=[[A]]
def features_dense_byte (A):
- from modshogun import ByteFeatures
+ from shogun import ByteFeatures
# create dense features a
# ... of type Byte
diff --git a/examples/undocumented/python/features_dense_io.py b/examples/undocumented/python/features_dense_io.py
index aa83a4cfd4e..630468c0d3c 100644
--- a/examples/undocumented/python/features_dense_io.py
+++ b/examples/undocumented/python/features_dense_io.py
@@ -2,7 +2,7 @@
parameter_list=[[]]
def features_dense_io():
- from modshogun import RealFeatures, CSVFile
+ from shogun import RealFeatures, CSVFile
feats=RealFeatures()
f=CSVFile("../data/fm_train_real.dat","r")
f.set_delimiter(" ")
diff --git a/examples/undocumented/python/features_dense_longint.py b/examples/undocumented/python/features_dense_longint.py
index 8d5435f4f56..5a21f5b1f42 100644
--- a/examples/undocumented/python/features_dense_longint.py
+++ b/examples/undocumented/python/features_dense_longint.py
@@ -1,5 +1,5 @@
#!/usr/bin/env python
-from modshogun import LongIntFeatures
+from shogun import LongIntFeatures
from numpy import array, int64, all
# create dense matrix A
diff --git a/examples/undocumented/python/features_dense_protocols.py b/examples/undocumented/python/features_dense_protocols.py
index 6867c208463..a11a8639fc3 100644
--- a/examples/undocumented/python/features_dense_protocols.py
+++ b/examples/undocumented/python/features_dense_protocols.py
@@ -1,7 +1,7 @@
#!/usr/bin/env python
import numpy
-from modshogun import RealFeatures
-from modshogun import LongIntFeatures
+from shogun import RealFeatures
+from shogun import LongIntFeatures
from numpy import array, float64, int64
diff --git a/examples/undocumented/python/features_dense_real.py b/examples/undocumented/python/features_dense_real.py
index 4e397fdeecd..169f8011abd 100644
--- a/examples/undocumented/python/features_dense_real.py
+++ b/examples/undocumented/python/features_dense_real.py
@@ -1,5 +1,5 @@
#!/usr/bin/env python
-from modshogun import RealFeatures
+from shogun import RealFeatures
from numpy import array, float64, all
# create dense matrices A,B,C
diff --git a/examples/undocumented/python/features_dense_zero_copy.py b/examples/undocumented/python/features_dense_zero_copy.py
index 24e213beab6..9e7e0c5a40c 100644
--- a/examples/undocumented/python/features_dense_zero_copy.py
+++ b/examples/undocumented/python/features_dense_zero_copy.py
@@ -1,6 +1,6 @@
#!/usr/bin/env python
import numpy
-from modshogun import RealFeatures
+from shogun import RealFeatures
from numpy import array, float64, int64
# create dense matrice
diff --git a/examples/undocumented/python/features_director_dot.py b/examples/undocumented/python/features_director_dot.py
index 70e4b80bd66..047e2c2fda3 100644
--- a/examples/undocumented/python/features_director_dot.py
+++ b/examples/undocumented/python/features_director_dot.py
@@ -12,8 +12,8 @@
def features_director_dot (fm_train_real, fm_test_real,
label_train_twoclass, C, epsilon):
try:
- from modshogun import DirectorDotFeatures
- from modshogun import RealVector
+ from shogun import DirectorDotFeatures
+ from shogun import RealVector
except ImportError:
print("recompile shogun with --enable-swig-directors")
return
@@ -61,9 +61,9 @@ def get_dim_feature_space(self):
# return NumpyFeatures(self.data-other.data)
- #from modshogun import RealFeatures, SparseRealFeatures, BinaryLabels
- #from modshogun import LibLinear, L2R_L2LOSS_SVC_DUAL
- #from modshogun import Math_init_random
+ #from shogun import RealFeatures, SparseRealFeatures, BinaryLabels
+ #from shogun import LibLinear, L2R_L2LOSS_SVC_DUAL
+ #from shogun import Math_init_random
#Math_init_random(17)
#feats_train=RealFeatures(fm_train_real)
diff --git a/examples/undocumented/python/features_hasheddocdot.py b/examples/undocumented/python/features_hasheddocdot.py
index 8bb00b4049a..06b4bc78695 100644
--- a/examples/undocumented/python/features_hasheddocdot.py
+++ b/examples/undocumented/python/features_hasheddocdot.py
@@ -4,9 +4,9 @@
parameter_list=[[strings]]
def features_hasheddocdot(strings):
- from modshogun import StringCharFeatures, RAWBYTE
- from modshogun import HashedDocDotFeatures
- from modshogun import NGramTokenizer
+ from shogun import StringCharFeatures, RAWBYTE
+ from shogun import HashedDocDotFeatures
+ from shogun import NGramTokenizer
from numpy import array
#create string features
diff --git a/examples/undocumented/python/features_io.py b/examples/undocumented/python/features_io.py
index 8d1069e41df..ba41c42d3d0 100644
--- a/examples/undocumented/python/features_io.py
+++ b/examples/undocumented/python/features_io.py
@@ -8,9 +8,9 @@
def features_io (fm_train_real, label_train_twoclass):
import numpy
- from modshogun import SparseRealFeatures, RealFeatures, MulticlassLabels
- from modshogun import GaussianKernel
- from modshogun import LibSVMFile, CSVFile, BinaryFile, HDF5File
+ from shogun import SparseRealFeatures, RealFeatures, MulticlassLabels
+ from shogun import GaussianKernel
+ from shogun import LibSVMFile, CSVFile, BinaryFile, HDF5File
from tempfile import NamedTemporaryFile
feats=SparseRealFeatures(fm_train_real)
diff --git a/examples/undocumented/python/features_read_svmlight_format.py b/examples/undocumented/python/features_read_svmlight_format.py
index 3551b6bd536..741fd59571b 100644
--- a/examples/undocumented/python/features_read_svmlight_format.py
+++ b/examples/undocumented/python/features_read_svmlight_format.py
@@ -3,8 +3,8 @@
def features_read_svmlight_format (fname):
from tempfile import NamedTemporaryFile
- from modshogun import SparseRealFeatures
- from modshogun import LibSVMFile
+ from shogun import SparseRealFeatures
+ from shogun import LibSVMFile
f=SparseRealFeatures()
lab=f.load_with_labels(LibSVMFile(fname))
diff --git a/examples/undocumented/python/features_snp.py b/examples/undocumented/python/features_snp.py
index 4bc1ff0ad01..afaa31aecf3 100644
--- a/examples/undocumented/python/features_snp.py
+++ b/examples/undocumented/python/features_snp.py
@@ -2,7 +2,7 @@
parameter_list=[['../data/snps.dat']]
def features_snp (fname):
- from modshogun import StringByteFeatures, SNPFeatures, SNP
+ from shogun import StringByteFeatures, SNPFeatures, SNP
sf=StringByteFeatures(SNP)
sf.load_ascii_file(fname, False, SNP, SNP)
diff --git a/examples/undocumented/python/features_sparse.py b/examples/undocumented/python/features_sparse.py
index 02ad70eaf61..8c80f69f461 100644
--- a/examples/undocumented/python/features_sparse.py
+++ b/examples/undocumented/python/features_sparse.py
@@ -6,7 +6,7 @@
parameter_list=[[A]]
def features_sparse (A):
from scipy.sparse import csc_matrix
- from modshogun import SparseRealFeatures
+ from shogun import SparseRealFeatures
from numpy import array, float64, all
# sparse representation X of dense matrix A
diff --git a/examples/undocumented/python/features_string_char.py b/examples/undocumented/python/features_string_char.py
index 9be1c8f4dee..cab77c02af4 100644
--- a/examples/undocumented/python/features_string_char.py
+++ b/examples/undocumented/python/features_string_char.py
@@ -4,7 +4,7 @@
parameter_list=[[strings]]
def features_string_char (strings):
- from modshogun import StringCharFeatures, RAWBYTE
+ from shogun import StringCharFeatures, RAWBYTE
from numpy import array
#create string features
diff --git a/examples/undocumented/python/features_string_char_compressed.py b/examples/undocumented/python/features_string_char_compressed.py
index 7922f67e7bb..462809c66bc 100644
--- a/examples/undocumented/python/features_string_char_compressed.py
+++ b/examples/undocumented/python/features_string_char_compressed.py
@@ -2,9 +2,9 @@
parameter_list = [['features_string_char_compressed.py']]
def features_string_char_compressed (fname):
- from modshogun import StringCharFeatures, StringFileCharFeatures, RAWBYTE
- from modshogun import UNCOMPRESSED,SNAPPY,LZO,GZIP,BZIP2,LZMA, MSG_DEBUG
- from modshogun import DecompressCharString
+ from shogun import StringCharFeatures, StringFileCharFeatures, RAWBYTE
+ from shogun import UNCOMPRESSED,SNAPPY,LZO,GZIP,BZIP2,LZMA, MSG_DEBUG
+ from shogun import DecompressCharString
f=StringFileCharFeatures(fname, RAWBYTE)
diff --git a/examples/undocumented/python/features_string_file.py b/examples/undocumented/python/features_string_file.py
index cc05ea8dd69..d0e633d1226 100644
--- a/examples/undocumented/python/features_string_file.py
+++ b/examples/undocumented/python/features_string_file.py
@@ -2,8 +2,8 @@
parameter_list=[[".", "features_string_char.py"]]
def features_string_file (directory, fname):
- from modshogun import StringCharFeatures, RAWBYTE
- from modshogun import CSVFile
+ from shogun import StringCharFeatures, RAWBYTE
+ from shogun import CSVFile
# load features from directory
f=StringCharFeatures(RAWBYTE)
diff --git a/examples/undocumented/python/features_string_file_char.py b/examples/undocumented/python/features_string_file_char.py
index 5de183de0a0..3335b668535 100644
--- a/examples/undocumented/python/features_string_file_char.py
+++ b/examples/undocumented/python/features_string_file_char.py
@@ -2,7 +2,7 @@
parameter_list = [['features_string_file_char.py']]
def features_string_file_char (fname):
- from modshogun import StringFileCharFeatures, RAWBYTE
+ from shogun import StringFileCharFeatures, RAWBYTE
f = StringFileCharFeatures(fname, RAWBYTE)
#print("strings", f.get_features())
return f
diff --git a/examples/undocumented/python/features_string_hashed_wd.py b/examples/undocumented/python/features_string_hashed_wd.py
index 734d5c2cacf..7ee0679fcee 100644
--- a/examples/undocumented/python/features_string_hashed_wd.py
+++ b/examples/undocumented/python/features_string_hashed_wd.py
@@ -1,5 +1,5 @@
#!/usr/bin/env python
-from modshogun import LongIntFeatures
+from shogun import LongIntFeatures
from numpy import array, int64, all
# create dense matrix A
@@ -12,8 +12,8 @@ def features_string_hashed_wd (A=matrix,order=3,start_order=1,hash_bits=2):
a=LongIntFeatures(A)
from numpy import array, uint8
- from modshogun import HashedWDFeatures, StringByteFeatures, RAWDNA
- from modshogun import MSG_DEBUG
+ from shogun import HashedWDFeatures, StringByteFeatures, RAWDNA
+ from shogun import MSG_DEBUG
x=[array([0,1,2,3,0,1,2,3,3,2,2,1,1],dtype=uint8)]
from_order=order
diff --git a/examples/undocumented/python/features_string_sliding_window.py b/examples/undocumented/python/features_string_sliding_window.py
index 29f29cd3da1..05ac8de69c7 100644
--- a/examples/undocumented/python/features_string_sliding_window.py
+++ b/examples/undocumented/python/features_string_sliding_window.py
@@ -5,8 +5,8 @@
parameter_list=[[s]]
def features_string_sliding_window (strings):
- from modshogun import StringCharFeatures, DNA
- from modshogun import DynamicIntArray
+ from shogun import StringCharFeatures, DNA
+ from shogun import DynamicIntArray
f=StringCharFeatures([strings], DNA)
diff --git a/examples/undocumented/python/features_string_ulong.py b/examples/undocumented/python/features_string_ulong.py
index e6e0733016f..ca0808299fa 100644
--- a/examples/undocumented/python/features_string_ulong.py
+++ b/examples/undocumented/python/features_string_ulong.py
@@ -4,7 +4,7 @@
def features_string_ulong (start=0,order=2,gap=0,rev=False):
- from modshogun import StringCharFeatures, StringUlongFeatures, RAWBYTE
+ from shogun import StringCharFeatures, StringUlongFeatures, RAWBYTE
from numpy import array, uint64
#create string features
diff --git a/examples/undocumented/python/features_string_word.py b/examples/undocumented/python/features_string_word.py
index 09865a65c5e..0bba571d6a8 100644
--- a/examples/undocumented/python/features_string_word.py
+++ b/examples/undocumented/python/features_string_word.py
@@ -4,7 +4,7 @@
parameter_list=[[strings,0,2,0,False]]
def features_string_word (strings, start, order, gap, rev):
- from modshogun import StringCharFeatures, StringWordFeatures, RAWBYTE
+ from shogun import StringCharFeatures, StringWordFeatures, RAWBYTE
from numpy import array, uint16
#create string features
diff --git a/examples/undocumented/python/graphical/classifier_gaussian_process_binary_classification.py b/examples/undocumented/python/graphical/classifier_gaussian_process_binary_classification.py
index ce8e06feb56..2d88b9693c6 100644
--- a/examples/undocumented/python/graphical/classifier_gaussian_process_binary_classification.py
+++ b/examples/undocumented/python/graphical/classifier_gaussian_process_binary_classification.py
@@ -35,7 +35,7 @@ def gaussian_process_binary_classification_laplace(X_train, y_train, n_test=50):
# import all necessary modules from Shogun (some of them require Eigen3)
try:
- from modshogun import RealFeatures, BinaryLabels, GaussianKernel, \
+ from shogun import RealFeatures, BinaryLabels, GaussianKernel, \
LogitLikelihood, ProbitLikelihood, ZeroMean, SingleLaplacianInferenceMethod, \
EPInferenceMethod, GaussianProcessClassification
except ImportError:
diff --git a/examples/undocumented/python/graphical/classifier_perceptron_graphical.py b/examples/undocumented/python/graphical/classifier_perceptron_graphical.py
index d7efc844432..2064921b835 100644
--- a/examples/undocumented/python/graphical/classifier_perceptron_graphical.py
+++ b/examples/undocumented/python/graphical/classifier_perceptron_graphical.py
@@ -7,9 +7,9 @@
parameter_list = [[20, 5, 1., 1000, 1, None, 5], [100, 5, 1., 1000, 1, None, 10]]
def classifier_perceptron_graphical(n=100, distance=5, learn_rate=1., max_iter=1000, num_threads=1, seed=None, nperceptrons=5):
- from modshogun import RealFeatures, BinaryLabels
- from modshogun import Perceptron
- from modshogun import MSG_INFO
+ from shogun import RealFeatures, BinaryLabels
+ from shogun import Perceptron
+ from shogun import MSG_INFO
# 2D data
_DIM = 2
diff --git a/examples/undocumented/python/graphical/cluster_kmeans.py b/examples/undocumented/python/graphical/cluster_kmeans.py
index e549ab3bbbf..63a82911954 100644
--- a/examples/undocumented/python/graphical/cluster_kmeans.py
+++ b/examples/undocumented/python/graphical/cluster_kmeans.py
@@ -2,7 +2,7 @@
from numpy import ones,zeros,cos,sin,concatenate
from numpy.random import randn
-from modshogun import *
+from shogun import *
k=4
num=1000
diff --git a/examples/undocumented/python/graphical/cluster_kpp.py b/examples/undocumented/python/graphical/cluster_kpp.py
index c0c0f6f9b9a..70b76cdeeeb 100644
--- a/examples/undocumented/python/graphical/cluster_kpp.py
+++ b/examples/undocumented/python/graphical/cluster_kpp.py
@@ -11,7 +11,7 @@
from numpy import array,ones,zeros,cos,sin,concatenate
from numpy.random import randn
-from modshogun import *
+from shogun import *
k=2
num=500
diff --git a/examples/undocumented/python/graphical/converter_ffsep_bss.py b/examples/undocumented/python/graphical/converter_ffsep_bss.py
index 64e01a94a3a..cfb4527e568 100644
--- a/examples/undocumented/python/graphical/converter_ffsep_bss.py
+++ b/examples/undocumented/python/graphical/converter_ffsep_bss.py
@@ -11,8 +11,8 @@
import numpy as np
import pylab as pl
-from modshogun import RealFeatures
-from modshogun import FFSep
+from shogun import RealFeatures
+from shogun import FFSep
# Generate sample data
np.random.seed(0)
diff --git a/examples/undocumented/python/graphical/converter_jade_bss.py b/examples/undocumented/python/graphical/converter_jade_bss.py
index 973508a27c0..f06b2efe033 100644
--- a/examples/undocumented/python/graphical/converter_jade_bss.py
+++ b/examples/undocumented/python/graphical/converter_jade_bss.py
@@ -11,8 +11,8 @@
import numpy as np
import pylab as pl
-from modshogun import RealFeatures
-from modshogun import Jade
+from shogun import RealFeatures
+from shogun import Jade
# Generate sample data
np.random.seed(0)
diff --git a/examples/undocumented/python/graphical/converter_jedi_bss.py b/examples/undocumented/python/graphical/converter_jedi_bss.py
index 184f86f5a33..b110ccc493b 100644
--- a/examples/undocumented/python/graphical/converter_jedi_bss.py
+++ b/examples/undocumented/python/graphical/converter_jedi_bss.py
@@ -11,8 +11,8 @@
import numpy as np
import pylab as pl
-from modshogun import RealFeatures
-from modshogun import JediSep
+from shogun import RealFeatures
+from shogun import JediSep
# Generate sample data
np.random.seed(0)
diff --git a/examples/undocumented/python/graphical/converter_sobi_bss.py b/examples/undocumented/python/graphical/converter_sobi_bss.py
index 808f0880ad8..67b11eaab4e 100644
--- a/examples/undocumented/python/graphical/converter_sobi_bss.py
+++ b/examples/undocumented/python/graphical/converter_sobi_bss.py
@@ -11,8 +11,8 @@
import numpy as np
import pylab as pl
-from modshogun import RealFeatures
-from modshogun import SOBI
+from shogun import RealFeatures
+from shogun import SOBI
# Generate sample data
np.random.seed(0)
diff --git a/examples/undocumented/python/graphical/converter_spe_helix.py b/examples/undocumented/python/graphical/converter_spe_helix.py
index c3c79aaf6d3..08164b1d2d0 100644
--- a/examples/undocumented/python/graphical/converter_spe_helix.py
+++ b/examples/undocumented/python/graphical/converter_spe_helix.py
@@ -19,9 +19,9 @@
import pylab
import util
-from modshogun import RealFeatures
-from modshogun import StochasticProximityEmbedding, SPE_GLOBAL
-from modshogun import SPE_LOCAL, Isomap
+from shogun import RealFeatures
+from shogun import StochasticProximityEmbedding, SPE_GLOBAL
+from shogun import SPE_LOCAL, Isomap
# Number of data points
N = 500
diff --git a/examples/undocumented/python/graphical/converter_uwedge_bss.py b/examples/undocumented/python/graphical/converter_uwedge_bss.py
index 0450d7fee1f..bbab4c6ccaa 100644
--- a/examples/undocumented/python/graphical/converter_uwedge_bss.py
+++ b/examples/undocumented/python/graphical/converter_uwedge_bss.py
@@ -8,8 +8,8 @@
import numpy as np
import pylab as pl
-from modshogun import RealFeatures
-from modshogun import UWedgeSep
+from shogun import RealFeatures
+from shogun import UWedgeSep
# Generate sample data
np.random.seed(0)
diff --git a/examples/undocumented/python/graphical/eigenfaces.py b/examples/undocumented/python/graphical/eigenfaces.py
index d4a888f4627..60253957680 100644
--- a/examples/undocumented/python/graphical/eigenfaces.py
+++ b/examples/undocumented/python/graphical/eigenfaces.py
@@ -41,9 +41,9 @@
import numpy as np
from numpy import random
-from modshogun import RealFeatures
-from modshogun import PCA
-from modshogun import EuclideanDistance
+from shogun import RealFeatures
+from shogun import PCA
+from shogun import EuclideanDistance
import math
import os
import pylab as pl
diff --git a/examples/undocumented/python/graphical/em_1d_gmm.py b/examples/undocumented/python/graphical/em_1d_gmm.py
index cb00495acb2..1e826e3a514 100644
--- a/examples/undocumented/python/graphical/em_1d_gmm.py
+++ b/examples/undocumented/python/graphical/em_1d_gmm.py
@@ -1,7 +1,7 @@
from pylab import figure,show,connect,hist,plot,legend
from numpy import array, append, arange, empty, exp
-from modshogun import Gaussian, GMM
-from modshogun import RealFeatures
+from shogun import Gaussian, GMM
+from shogun import RealFeatures
import util
util.set_title('EM for 1d GMM example')
diff --git a/examples/undocumented/python/graphical/em_2d_gmm.py b/examples/undocumented/python/graphical/em_2d_gmm.py
index 92cb298de37..90551c7b440 100644
--- a/examples/undocumented/python/graphical/em_2d_gmm.py
+++ b/examples/undocumented/python/graphical/em_2d_gmm.py
@@ -1,7 +1,7 @@
from pylab import figure,scatter,contour,show,legend,connect
from numpy import array, append, arange, reshape, empty, exp
-from modshogun import Gaussian, GMM
-from modshogun import RealFeatures
+from shogun import Gaussian, GMM
+from shogun import RealFeatures
import util
util.set_title('EM for 2d GMM example')
diff --git a/examples/undocumented/python/graphical/group_lasso.py b/examples/undocumented/python/graphical/group_lasso.py
index fb27e2361c9..1de54d418b6 100644
--- a/examples/undocumented/python/graphical/group_lasso.py
+++ b/examples/undocumented/python/graphical/group_lasso.py
@@ -4,7 +4,7 @@
import matplotlib.pyplot as plt
from numpy.random import rand, randn, permutation, multivariate_normal
-from modshogun import BinaryLabels, RealFeatures, IndexBlock, IndexBlockGroup, FeatureBlockLogisticRegression
+from shogun import BinaryLabels, RealFeatures, IndexBlock, IndexBlockGroup, FeatureBlockLogisticRegression
def generate_synthetic_logistic_data(n, p, L, blk_nnz, gcov, nstd):
diff --git a/examples/undocumented/python/graphical/interactive_clustering_demo.py b/examples/undocumented/python/graphical/interactive_clustering_demo.py
index ad434521619..38c14597cae 100644
--- a/examples/undocumented/python/graphical/interactive_clustering_demo.py
+++ b/examples/undocumented/python/graphical/interactive_clustering_demo.py
@@ -16,9 +16,9 @@
from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar
from matplotlib.figure import Figure
-from modshogun import *
-from modshogun import *
-from modshogun import *
+from shogun import *
+from shogun import *
+from shogun import *
import util
class Form(QMainWindow):
diff --git a/examples/undocumented/python/graphical/interactive_gp_demo.py b/examples/undocumented/python/graphical/interactive_gp_demo.py
index ebbff94d0e2..9e1846eb52c 100644
--- a/examples/undocumented/python/graphical/interactive_gp_demo.py
+++ b/examples/undocumented/python/graphical/interactive_gp_demo.py
@@ -29,9 +29,9 @@
from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar
from matplotlib.figure import Figure
-from modshogun import *
-from modshogun import *
-from modshogun import *
+from shogun import *
+from shogun import *
+from shogun import *
import util
class Form(QMainWindow):
diff --git a/examples/undocumented/python/graphical/interactive_kmm_demo.py b/examples/undocumented/python/graphical/interactive_kmm_demo.py
index 80f957201b7..9eae0833371 100644
--- a/examples/undocumented/python/graphical/interactive_kmm_demo.py
+++ b/examples/undocumented/python/graphical/interactive_kmm_demo.py
@@ -25,9 +25,9 @@
from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar
from matplotlib.figure import Figure
-from modshogun import *
-from modshogun import KernelMeanMatching
-from modshogun import Math
+from shogun import *
+from shogun import KernelMeanMatching
+from shogun import Math
import util
class Form(QMainWindow):
diff --git a/examples/undocumented/python/graphical/interactive_svm_demo.py b/examples/undocumented/python/graphical/interactive_svm_demo.py
index 14b00292d29..1732f89394d 100644
--- a/examples/undocumented/python/graphical/interactive_svm_demo.py
+++ b/examples/undocumented/python/graphical/interactive_svm_demo.py
@@ -16,7 +16,7 @@
from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar
from matplotlib.figure import Figure
-from modshogun import *
+from shogun import *
import util
class Form(QMainWindow):
diff --git a/examples/undocumented/python/graphical/interactive_svr_demo.py b/examples/undocumented/python/graphical/interactive_svr_demo.py
index b0276ae3765..302e9e86c82 100644
--- a/examples/undocumented/python/graphical/interactive_svr_demo.py
+++ b/examples/undocumented/python/graphical/interactive_svr_demo.py
@@ -16,7 +16,7 @@
from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar
from matplotlib.figure import Figure
-from modshogun import *
+from shogun import *
class Form(QMainWindow):
def __init__(self, parent=None):
diff --git a/examples/undocumented/python/graphical/inverse_covariance_estimation_demo.py b/examples/undocumented/python/graphical/inverse_covariance_estimation_demo.py
index 09faf32ac6f..91848758b03 100755
--- a/examples/undocumented/python/graphical/inverse_covariance_estimation_demo.py
+++ b/examples/undocumented/python/graphical/inverse_covariance_estimation_demo.py
@@ -5,7 +5,7 @@
from pylab import show, imshow
def simulate_data (n,p):
- from modshogun import SparseInverseCovariance
+ from shogun import SparseInverseCovariance
import numpy as np
#create a random pxp covariance matrix
@@ -18,7 +18,7 @@ def simulate_data (n,p):
return data
def inverse_covariance (data,lc):
- from modshogun import SparseInverseCovariance
+ from shogun import SparseInverseCovariance
from numpy import dot
sic = SparseInverseCovariance()
diff --git a/examples/undocumented/python/graphical/kernel_ridge_regression.py b/examples/undocumented/python/graphical/kernel_ridge_regression.py
index 7d9a9227b32..be72f275076 100644
--- a/examples/undocumented/python/graphical/kernel_ridge_regression.py
+++ b/examples/undocumented/python/graphical/kernel_ridge_regression.py
@@ -1,7 +1,7 @@
from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,connect
from numpy import array,meshgrid,reshape,linspace,min,max
from numpy import concatenate,transpose,ravel
-from modshogun import *
+from shogun import *
import util
util.set_title('KernelRidgeRegression')
diff --git a/examples/undocumented/python/graphical/kernel_ridge_regression_sinc.py b/examples/undocumented/python/graphical/kernel_ridge_regression_sinc.py
index 2ea9a0aa311..7f9d68d8284 100644
--- a/examples/undocumented/python/graphical/kernel_ridge_regression_sinc.py
+++ b/examples/undocumented/python/graphical/kernel_ridge_regression_sinc.py
@@ -1,5 +1,5 @@
from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,legend,connect
-from modshogun import *
+from shogun import *
import util
util.set_title('KernelRidgeRegression on Sine')
diff --git a/examples/undocumented/python/graphical/lda.py b/examples/undocumented/python/graphical/lda.py
index 14b4ccf9c47..9eef3796f2f 100644
--- a/examples/undocumented/python/graphical/lda.py
+++ b/examples/undocumented/python/graphical/lda.py
@@ -1,5 +1,5 @@
from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,connect
-from modshogun import *
+from shogun import *
import util
util.set_title('LDA')
diff --git a/examples/undocumented/python/graphical/mclda.py b/examples/undocumented/python/graphical/mclda.py
index 961b93dfe7d..eae10a4ec65 100644
--- a/examples/undocumented/python/graphical/mclda.py
+++ b/examples/undocumented/python/graphical/mclda.py
@@ -1,6 +1,6 @@
-from modshogun import RealFeatures
-from modshogun import MulticlassLabels
-from modshogun import MCLDA
+from shogun import RealFeatures
+from shogun import MulticlassLabels
+from shogun import MCLDA
from pylab import pcolor, contour, colorbar, connect, show, plot, axis
import numpy as np
diff --git a/examples/undocumented/python/graphical/metric_lmnn_objective.py b/examples/undocumented/python/graphical/metric_lmnn_objective.py
index 1fc83932eb0..bae796fd351 100644
--- a/examples/undocumented/python/graphical/metric_lmnn_objective.py
+++ b/examples/undocumented/python/graphical/metric_lmnn_objective.py
@@ -36,10 +36,10 @@ def load_compressed_features(fname_features):
def metric_lmnn_statistics(k=3, fname_features='../../data/fm_train_multiclass_digits.dat.gz', fname_labels='../../data/label_train_multiclass_digits.dat'):
try:
- from modshogun import LMNN, CSVFile, RealFeatures, MulticlassLabels, MSG_DEBUG
+ from shogun import LMNN, CSVFile, RealFeatures, MulticlassLabels, MSG_DEBUG
import matplotlib.pyplot as pyplot
except ImportError:
- print 'Error importing modshogun or other required modules. Please, verify their installation.'
+ print 'Error importing shogun or other required modules. Please, verify their installation.'
return
features = RealFeatures(load_compressed_features(fname_features).T)
diff --git a/examples/undocumented/python/graphical/multiclass_qda.py b/examples/undocumented/python/graphical/multiclass_qda.py
index 91afb419078..53419f45e57 100644
--- a/examples/undocumented/python/graphical/multiclass_qda.py
+++ b/examples/undocumented/python/graphical/multiclass_qda.py
@@ -10,8 +10,8 @@
import util
from scipy import linalg
-from modshogun import QDA
-from modshogun import RealFeatures, MulticlassLabels
+from shogun import QDA
+from shogun import RealFeatures, MulticlassLabels
# colormap
cmap = mpl.colors.LinearSegmentedColormap('color_classes',
diff --git a/examples/undocumented/python/graphical/multiple_smvs.py b/examples/undocumented/python/graphical/multiple_smvs.py
index 21ad567584e..a728ba5b03a 100644
--- a/examples/undocumented/python/graphical/multiple_smvs.py
+++ b/examples/undocumented/python/graphical/multiple_smvs.py
@@ -4,7 +4,7 @@
from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,connect,axis
from numpy import concatenate
from numpy.random import randn
-from modshogun import *
+from shogun import *
import util
util.set_title('Multiple SVMS')
diff --git a/examples/undocumented/python/graphical/prc.py b/examples/undocumented/python/graphical/prc.py
index d13eda7ada1..79ef6c0c942 100644
--- a/examples/undocumented/python/graphical/prc.py
+++ b/examples/undocumented/python/graphical/prc.py
@@ -1,7 +1,7 @@
from pylab import plot,grid,title,subplot,xlabel,ylabel,text,subplots_adjust,fill_between,mean,connect,show
-from modshogun import GaussianKernel
-from modshogun import LibSVM, LDA
-from modshogun import PRCEvaluation
+from shogun import GaussianKernel
+from shogun import LibSVM, LDA
+from shogun import PRCEvaluation
import util
util.set_title('PRC example')
diff --git a/examples/undocumented/python/graphical/preprocessor_kpca_graphical.py b/examples/undocumented/python/graphical/preprocessor_kpca_graphical.py
index a2344ef8629..c6ef1437235 100644
--- a/examples/undocumented/python/graphical/preprocessor_kpca_graphical.py
+++ b/examples/undocumented/python/graphical/preprocessor_kpca_graphical.py
@@ -25,9 +25,9 @@
parameter_list = [[data,0.01,1.0], [data,0.05,2.0]]
def preprocessor_kernelpca_modular (data, threshold, width):
- from modshogun import RealFeatures
- from modshogun import KernelPCA
- from modshogun import GaussianKernel
+ from shogun import RealFeatures
+ from shogun import KernelPCA
+ from shogun import GaussianKernel
features = RealFeatures(data)
kernel=GaussianKernel(features,features,width)
preprocessor=KernelPCA(kernel)
diff --git a/examples/undocumented/python/graphical/qda.py b/examples/undocumented/python/graphical/qda.py
index 3361cf12378..e8486410e5d 100644
--- a/examples/undocumented/python/graphical/qda.py
+++ b/examples/undocumented/python/graphical/qda.py
@@ -1,6 +1,6 @@
-from modshogun import RealFeatures
-from modshogun import MulticlassLabels
-from modshogun import QDA
+from shogun import RealFeatures
+from shogun import MulticlassLabels
+from shogun import QDA
from pylab import pcolor, contour, colorbar, connect, show, plot, axis
import numpy as np
diff --git a/examples/undocumented/python/graphical/regression_gaussian_process_demo.py b/examples/undocumented/python/graphical/regression_gaussian_process_demo.py
index eb24bf4f1b9..132d6a71ebc 100644
--- a/examples/undocumented/python/graphical/regression_gaussian_process_demo.py
+++ b/examples/undocumented/python/graphical/regression_gaussian_process_demo.py
@@ -6,14 +6,14 @@
###########################################################################
from numpy import *
from numpy.random import randn
-from modshogun import *
+from shogun import *
import pylab as PL
import matplotlib
import logging as LG
import scipy as SP
-from modshogun import GradientModelSelection
-from modshogun import ModelSelectionParameters, R_EXP, R_LINEAR
-from modshogun import ParameterCombination
+from shogun import GradientModelSelection
+from shogun import ModelSelectionParameters, R_EXP, R_LINEAR
+from shogun import ParameterCombination
def plot_training_data(x, y,
shift=None,
diff --git a/examples/undocumented/python/graphical/regression_gaussian_process_modelselection.py b/examples/undocumented/python/graphical/regression_gaussian_process_modelselection.py
index d9621f259cc..f916e7a4ea8 100644
--- a/examples/undocumented/python/graphical/regression_gaussian_process_modelselection.py
+++ b/examples/undocumented/python/graphical/regression_gaussian_process_modelselection.py
@@ -6,10 +6,10 @@
def regression_gaussian_process_modelselection (n=100, n_test=100, \
x_range=5, x_range_test=10, noise_var=0.4):
- from modshogun import RealFeatures, RegressionLabels
- from modshogun import GaussianKernel
- from modshogun import GradientModelSelection, ModelSelectionParameters
- from modshogun import GaussianLikelihood, ZeroMean, \
+ from shogun import RealFeatures, RegressionLabels
+ from shogun import GaussianKernel
+ from shogun import GradientModelSelection, ModelSelectionParameters
+ from shogun import GaussianLikelihood, ZeroMean, \
ExactInferenceMethod, GaussianProcessRegression, GradientCriterion, \
GradientEvaluation
diff --git a/examples/undocumented/python/graphical/regression_lars.py b/examples/undocumented/python/graphical/regression_lars.py
index d78aef010c0..bab8189130a 100644
--- a/examples/undocumented/python/graphical/regression_lars.py
+++ b/examples/undocumented/python/graphical/regression_lars.py
@@ -3,9 +3,9 @@
import numpy as np
import matplotlib.pyplot as plt
-from modshogun import RegressionLabels, RealFeatures
-from modshogun import LeastAngleRegression, LinearRidgeRegression, LeastSquaresRegression
-from modshogun import MeanSquaredError
+from shogun import RegressionLabels, RealFeatures
+from shogun import LeastAngleRegression, LinearRidgeRegression, LeastSquaresRegression
+from shogun import MeanSquaredError
# we compare LASSO with ordinary least-squares (OLE)
# in the ideal case, the MSE of OLE should coincide
diff --git a/examples/undocumented/python/graphical/roc.py b/examples/undocumented/python/graphical/roc.py
index aeb16c4275e..0c0b6c60438 100644
--- a/examples/undocumented/python/graphical/roc.py
+++ b/examples/undocumented/python/graphical/roc.py
@@ -1,7 +1,7 @@
from pylab import plot,grid,title,subplot,xlabel,ylabel,text,subplots_adjust,fill_between,mean,connect,show
-from modshogun import GaussianKernel
-from modshogun import LibSVM, LDA
-from modshogun import ROCEvaluation
+from shogun import GaussianKernel
+from shogun import LibSVM, LDA
+from shogun import ROCEvaluation
import util
util.set_title('ROC example')
diff --git a/examples/undocumented/python/graphical/smem_1d_gmm.py b/examples/undocumented/python/graphical/smem_1d_gmm.py
index 46daf184344..256404c93f7 100644
--- a/examples/undocumented/python/graphical/smem_1d_gmm.py
+++ b/examples/undocumented/python/graphical/smem_1d_gmm.py
@@ -1,7 +1,7 @@
from pylab import figure,show,connect,hist,plot,legend
from numpy import array, append, arange, empty, exp
-from modshogun import Gaussian, GMM
-from modshogun import RealFeatures
+from shogun import Gaussian, GMM
+from shogun import RealFeatures
import util
util.set_title('SMEM for 1d GMM example')
diff --git a/examples/undocumented/python/graphical/smem_2d_gmm.py b/examples/undocumented/python/graphical/smem_2d_gmm.py
index 1b113a96989..6655aceee30 100644
--- a/examples/undocumented/python/graphical/smem_2d_gmm.py
+++ b/examples/undocumented/python/graphical/smem_2d_gmm.py
@@ -1,7 +1,7 @@
from pylab import figure,scatter,contour,show,legend,connect
from numpy import array, append, arange, reshape, empty, exp
-from modshogun import Gaussian, GMM
-from modshogun import RealFeatures
+from shogun import Gaussian, GMM
+from shogun import RealFeatures
import util
util.set_title('SMEM for 2d GMM example')
diff --git a/examples/undocumented/python/graphical/so_multiclass_BMRM.py b/examples/undocumented/python/graphical/so_multiclass_BMRM.py
index 64295040669..8c59826e060 100644
--- a/examples/undocumented/python/graphical/so_multiclass_BMRM.py
+++ b/examples/undocumented/python/graphical/so_multiclass_BMRM.py
@@ -3,10 +3,10 @@
import numpy as np
import matplotlib.pyplot as plt
-from modshogun import RealFeatures
-from modshogun import MulticlassModel, MulticlassSOLabels, RealNumber, DualLibQPBMSOSVM
-from modshogun import BMRM, PPBMRM, P3BMRM
-from modshogun import StructuredAccuracy
+from shogun import RealFeatures
+from shogun import MulticlassModel, MulticlassSOLabels, RealNumber, DualLibQPBMSOSVM
+from shogun import BMRM, PPBMRM, P3BMRM
+from shogun import StructuredAccuracy
def fill_data(cnt, minv, maxv):
x1 = np.linspace(minv, maxv, cnt)
diff --git a/examples/undocumented/python/graphical/so_multiclass_director_BMRM.py b/examples/undocumented/python/graphical/so_multiclass_director_BMRM.py
index e49e28987eb..0edbe80b833 100644
--- a/examples/undocumented/python/graphical/so_multiclass_director_BMRM.py
+++ b/examples/undocumented/python/graphical/so_multiclass_director_BMRM.py
@@ -3,10 +3,10 @@
import numpy as np
import matplotlib.pyplot as plt
-from modshogun import RealFeatures
-from modshogun import MulticlassModel, MulticlassSOLabels, RealNumber, DualLibQPBMSOSVM, DirectorStructuredModel
-from modshogun import BMRM, PPBMRM, P3BMRM, ResultSet, RealVector
-from modshogun import StructuredAccuracy
+from shogun import RealFeatures
+from shogun import MulticlassModel, MulticlassSOLabels, RealNumber, DualLibQPBMSOSVM, DirectorStructuredModel
+from shogun import BMRM, PPBMRM, P3BMRM, ResultSet, RealVector
+from shogun import StructuredAccuracy
class MulticlassStructuredModel(DirectorStructuredModel):
def __init__(self,features,labels):
diff --git a/examples/undocumented/python/graphical/statistics_hsic.py b/examples/undocumented/python/graphical/statistics_hsic.py
index c5b76658179..a0e44f24d3b 100644
--- a/examples/undocumented/python/graphical/statistics_hsic.py
+++ b/examples/undocumented/python/graphical/statistics_hsic.py
@@ -10,13 +10,13 @@
from pylab import *
from scipy import *
-from modshogun import RealFeatures
-from modshogun import DataGenerator
-from modshogun import GaussianKernel
-from modshogun import HSIC
-from modshogun import PERMUTATION, HSIC_GAMMA
-from modshogun import EuclideanDistance
-from modshogun import Statistics, Math
+from shogun import RealFeatures
+from shogun import DataGenerator
+from shogun import GaussianKernel
+from shogun import HSIC
+from shogun import PERMUTATION, HSIC_GAMMA
+from shogun import EuclideanDistance
+from shogun import Statistics, Math
# for nice plotting that fits into our shogun tutorial
import latex_plot_inits
diff --git a/examples/undocumented/python/graphical/statistics_linear_time_mmd.py b/examples/undocumented/python/graphical/statistics_linear_time_mmd.py
index f57ef905866..c2e001dc399 100644
--- a/examples/undocumented/python/graphical/statistics_linear_time_mmd.py
+++ b/examples/undocumented/python/graphical/statistics_linear_time_mmd.py
@@ -10,13 +10,13 @@
from pylab import *
from scipy import *
-from modshogun import RealFeatures
-from modshogun import MeanShiftDataGenerator
-from modshogun import GaussianKernel, CombinedKernel
-from modshogun import LinearTimeMMD, MMDKernelSelectionOpt
-from modshogun import PERMUTATION, MMD1_GAUSSIAN
-from modshogun import EuclideanDistance
-from modshogun import Statistics, Math
+from shogun import RealFeatures
+from shogun import MeanShiftDataGenerator
+from shogun import GaussianKernel, CombinedKernel
+from shogun import LinearTimeMMD, MMDKernelSelectionOpt
+from shogun import PERMUTATION, MMD1_GAUSSIAN
+from shogun import EuclideanDistance
+from shogun import Statistics, Math
# for nice plotting that fits into our shogun tutorial
import latex_plot_inits
diff --git a/examples/undocumented/python/graphical/statistics_quadratic_time_mmd.py b/examples/undocumented/python/graphical/statistics_quadratic_time_mmd.py
index f70459583fe..1fc33ff41f6 100644
--- a/examples/undocumented/python/graphical/statistics_quadratic_time_mmd.py
+++ b/examples/undocumented/python/graphical/statistics_quadratic_time_mmd.py
@@ -10,13 +10,13 @@
from pylab import *
from scipy import *
-from modshogun import RealFeatures
-from modshogun import MeanShiftDataGenerator
-from modshogun import GaussianKernel, CombinedKernel
-from modshogun import QuadraticTimeMMD, MMDKernelSelectionMax
-from modshogun import PERMUTATION, MMD2_SPECTRUM, MMD2_GAMMA, BIASED, UNBIASED
-from modshogun import EuclideanDistance
-from modshogun import Statistics, Math
+from shogun import RealFeatures
+from shogun import MeanShiftDataGenerator
+from shogun import GaussianKernel, CombinedKernel
+from shogun import QuadraticTimeMMD, MMDKernelSelectionMax
+from shogun import PERMUTATION, MMD2_SPECTRUM, MMD2_GAMMA, BIASED, UNBIASED
+from shogun import EuclideanDistance
+from shogun import Statistics, Math
# for nice plotting that fits into our shogun tutorial
import latex_plot_inits
diff --git a/examples/undocumented/python/graphical/svm.py b/examples/undocumented/python/graphical/svm.py
index 688aeea0637..9f6146fa259 100644
--- a/examples/undocumented/python/graphical/svm.py
+++ b/examples/undocumented/python/graphical/svm.py
@@ -1,6 +1,6 @@
from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,connect,axis
from numpy.random import randn
-from modshogun import *
+from shogun import *
import util
util.set_title('SVM')
diff --git a/examples/undocumented/python/graphical/svmlin.py b/examples/undocumented/python/graphical/svmlin.py
index 402edcdf85d..b380b7cf4ae 100644
--- a/examples/undocumented/python/graphical/svmlin.py
+++ b/examples/undocumented/python/graphical/svmlin.py
@@ -1,5 +1,5 @@
from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,axis, connect
-from modshogun import *
+from shogun import *
import util
util.set_title('SVM Linear 1')
diff --git a/examples/undocumented/python/graphical/svr_sinc.py b/examples/undocumented/python/graphical/svr_sinc.py
index d1245f71aee..54ff5b96b3b 100644
--- a/examples/undocumented/python/graphical/svr_sinc.py
+++ b/examples/undocumented/python/graphical/svr_sinc.py
@@ -1,5 +1,5 @@
from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,legend, connect
-from modshogun import *
+from shogun import *
import util
util.set_title('SVR on Sinus')
diff --git a/examples/undocumented/python/graphical/util.py b/examples/undocumented/python/graphical/util.py
index e4b749e014c..00bf51f917d 100644
--- a/examples/undocumented/python/graphical/util.py
+++ b/examples/undocumented/python/graphical/util.py
@@ -4,7 +4,7 @@
from numpy import ones, array, double, meshgrid, reshape, linspace, \
concatenate, ravel, pi, sinc
from numpy.random import randn, rand
-from modshogun import BinaryLabels, RegressionLabels, RealFeatures, SparseRealFeatures
+from shogun import BinaryLabels, RegressionLabels, RealFeatures, SparseRealFeatures
QUITKEY='q'
NUM_EXAMPLES=100
diff --git a/examples/undocumented/python/kernel_anova.py b/examples/undocumented/python/kernel_anova.py
index 8336fc2bc3d..e1ab024add1 100644
--- a/examples/undocumented/python/kernel_anova.py
+++ b/examples/undocumented/python/kernel_anova.py
@@ -4,7 +4,7 @@
parameter_list = [[traindat,testdat,2,10], [traindat,testdat,5,10]]
def kernel_anova (train_fname=traindat,test_fname=testdat,cardinality=2, size_cache=10):
- from modshogun import ANOVAKernel,RealFeatures,CSVFile
+ from shogun import ANOVAKernel,RealFeatures,CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/kernel_auc.py b/examples/undocumented/python/kernel_auc.py
index 8594419bc4a..eb62666c13b 100644
--- a/examples/undocumented/python/kernel_auc.py
+++ b/examples/undocumented/python/kernel_auc.py
@@ -4,8 +4,8 @@
parameter_list = [[traindat,label_traindat,1.7], [traindat,label_traindat,1.6]]
def kernel_auc (train_fname=traindat,label_fname=label_traindat,width=1.7):
- from modshogun import GaussianKernel, AUCKernel, RealFeatures
- from modshogun import BinaryLabels, CSVFile
+ from shogun import GaussianKernel, AUCKernel, RealFeatures
+ from shogun import BinaryLabels, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
subkernel=GaussianKernel(feats_train, feats_train, width)
diff --git a/examples/undocumented/python/kernel_cauchy.py b/examples/undocumented/python/kernel_cauchy.py
index 272bc041db4..10814ebf1eb 100644
--- a/examples/undocumented/python/kernel_cauchy.py
+++ b/examples/undocumented/python/kernel_cauchy.py
@@ -5,7 +5,7 @@
parameter_list=[[traindat,testdat, 1.0],[traindat,testdat, 10.0]]
def kernel_cauchy (train_fname=traindat,test_fname=testdat, sigma=1.0):
- from modshogun import RealFeatures, CauchyKernel, CSVFile, EuclideanDistance
+ from shogun import RealFeatures, CauchyKernel, CSVFile, EuclideanDistance
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/kernel_chi2.py b/examples/undocumented/python/kernel_chi2.py
index 11f4eedee5d..95c4b4a62b8 100644
--- a/examples/undocumented/python/kernel_chi2.py
+++ b/examples/undocumented/python/kernel_chi2.py
@@ -5,7 +5,7 @@
parameter_list = [[traindat,testdat,1.4,10], [traindat,testdat,1.5,10]]
def kernel_chi2 (train_fname=traindat,test_fname=testdat,width=1.4, size_cache=10):
- from modshogun import RealFeatures, Chi2Kernel, CSVFile, NormOne
+ from shogun import RealFeatures, Chi2Kernel, CSVFile, NormOne
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/kernel_circular.py b/examples/undocumented/python/kernel_circular.py
index cddc9452c2f..872a523c365 100644
--- a/examples/undocumented/python/kernel_circular.py
+++ b/examples/undocumented/python/kernel_circular.py
@@ -5,7 +5,7 @@
parameter_list=[[traindat,testdat, 1.0],[traindat,testdat, 5.0]]
def kernel_circular(train_fname=traindat,test_fname=testdat, sigma=1.0):
- from modshogun import RealFeatures, CircularKernel, EuclideanDistance, CSVFile
+ from shogun import RealFeatures, CircularKernel, EuclideanDistance, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/kernel_combined.py b/examples/undocumented/python/kernel_combined.py
index a691e2f1011..50227811a12 100644
--- a/examples/undocumented/python/kernel_combined.py
+++ b/examples/undocumented/python/kernel_combined.py
@@ -10,8 +10,8 @@
parameter_list = [[traindat,testdat,traindna,testdna],[traindat,testdat,traindna,testdna]]
def kernel_combined (fm_train_real=traindat,fm_test_real=testdat,fm_train_dna=traindna,fm_test_dna=testdna ):
- from modshogun import CombinedKernel, GaussianKernel, FixedDegreeStringKernel, LocalAlignmentStringKernel
- from modshogun import RealFeatures, StringCharFeatures, CombinedFeatures, DNA
+ from shogun import CombinedKernel, GaussianKernel, FixedDegreeStringKernel, LocalAlignmentStringKernel
+ from shogun import RealFeatures, StringCharFeatures, CombinedFeatures, DNA
kernel=CombinedKernel()
feats_train=CombinedFeatures()
diff --git a/examples/undocumented/python/kernel_combined_custom_poly.py b/examples/undocumented/python/kernel_combined_custom_poly.py
index c57f593858c..490f7fecac9 100644
--- a/examples/undocumented/python/kernel_combined_custom_poly.py
+++ b/examples/undocumented/python/kernel_combined_custom_poly.py
@@ -8,9 +8,9 @@
def kernel_combined_custom_poly (train_fname = traindat,test_fname = testdat,train_label_fname=label_traindat):
- from modshogun import CombinedFeatures, RealFeatures, BinaryLabels
- from modshogun import CombinedKernel, PolyKernel, CustomKernel
- from modshogun import LibSVM, CSVFile
+ from shogun import CombinedFeatures, RealFeatures, BinaryLabels
+ from shogun import CombinedKernel, PolyKernel, CustomKernel
+ from shogun import LibSVM, CSVFile
kernel = CombinedKernel()
feats_train = CombinedFeatures()
diff --git a/examples/undocumented/python/kernel_comm_ulong_string.py b/examples/undocumented/python/kernel_comm_ulong_string.py
index 84db703ae5c..f6f04e45521 100644
--- a/examples/undocumented/python/kernel_comm_ulong_string.py
+++ b/examples/undocumented/python/kernel_comm_ulong_string.py
@@ -8,9 +8,9 @@
def kernel_comm_ulong_string (fm_train_dna=traindat,fm_test_dna=testdat, order=3, gap=0, reverse = False):
- from modshogun import CommUlongStringKernel
- from modshogun import StringUlongFeatures, StringCharFeatures, DNA
- from modshogun import SortUlongString
+ from shogun import CommUlongStringKernel
+ from shogun import StringUlongFeatures, StringCharFeatures, DNA
+ from shogun import SortUlongString
charfeat=StringCharFeatures(DNA)
charfeat.set_features(fm_train_dna)
diff --git a/examples/undocumented/python/kernel_comm_word_string.py b/examples/undocumented/python/kernel_comm_word_string.py
index 683e6525f9e..8054abb9081 100644
--- a/examples/undocumented/python/kernel_comm_word_string.py
+++ b/examples/undocumented/python/kernel_comm_word_string.py
@@ -8,9 +8,9 @@
def kernel_comm_word_string (fm_train_dna=traindat, fm_test_dna=testdat, order=3, gap=0, reverse = False, use_sign = False):
- from modshogun import CommWordStringKernel
- from modshogun import StringWordFeatures, StringCharFeatures, DNA
- from modshogun import SortWordString
+ from shogun import CommWordStringKernel
+ from shogun import StringWordFeatures, StringCharFeatures, DNA
+ from shogun import SortWordString
charfeat=StringCharFeatures(DNA)
charfeat.set_features(fm_train_dna)
diff --git a/examples/undocumented/python/kernel_const.py b/examples/undocumented/python/kernel_const.py
index 06e185204b0..5c3f0658df2 100644
--- a/examples/undocumented/python/kernel_const.py
+++ b/examples/undocumented/python/kernel_const.py
@@ -2,8 +2,8 @@
parameter_list =[[23],[24]]
def kernel_const (c=23):
- from modshogun import DummyFeatures
- from modshogun import ConstKernel
+ from shogun import DummyFeatures
+ from shogun import ConstKernel
feats_train=DummyFeatures(10)
feats_test=DummyFeatures(17)
diff --git a/examples/undocumented/python/kernel_custom.py b/examples/undocumented/python/kernel_custom.py
index 1b1276424b1..c77d1017166 100644
--- a/examples/undocumented/python/kernel_custom.py
+++ b/examples/undocumented/python/kernel_custom.py
@@ -7,9 +7,9 @@
def kernel_custom (dim=7):
from numpy.random import rand, seed
from numpy import array, float32, int32
- from modshogun import RealFeatures
- from modshogun import CustomKernel
- from modshogun import IndexFeatures
+ from shogun import RealFeatures
+ from shogun import CustomKernel
+ from shogun import IndexFeatures
seed(17)
data=rand(dim, dim)
diff --git a/examples/undocumented/python/kernel_diag.py b/examples/undocumented/python/kernel_diag.py
index 8c038bc6215..6c24a4e9614 100644
--- a/examples/undocumented/python/kernel_diag.py
+++ b/examples/undocumented/python/kernel_diag.py
@@ -1,8 +1,8 @@
#!/usr/bin/env python
parameter_list =[[23],[24]]
def kernel_diag (diag=23):
- from modshogun import DummyFeatures
- from modshogun import DiagKernel
+ from shogun import DummyFeatures
+ from shogun import DiagKernel
feats_train=DummyFeatures(10)
feats_test=DummyFeatures(17)
diff --git a/examples/undocumented/python/kernel_director_linear.py b/examples/undocumented/python/kernel_director_linear.py
index b36fa5487bd..6892d7e9d24 100644
--- a/examples/undocumented/python/kernel_director_linear.py
+++ b/examples/undocumented/python/kernel_director_linear.py
@@ -1,13 +1,13 @@
#!/usr/bin/env python
import numpy
-from modshogun import RealFeatures, MSG_DEBUG
+from shogun import RealFeatures, MSG_DEBUG
traindat = numpy.random.random_sample((10,10))
testdat = numpy.random.random_sample((10,10))
parameter_list=[[traindat,testdat,1.2],[traindat,testdat,1.4]]
def kernel_director_linear (fm_train_real=traindat,fm_test_real=testdat,scale=1.2):
try:
- from modshogun import DirectorKernel
+ from shogun import DirectorKernel
except ImportError:
print("recompile shogun with --enable-swig-directors")
return
@@ -21,8 +21,8 @@ def kernel_function(self, idx_a, idx_b):
return numpy.dot(seq1, seq2)
- from modshogun import LinearKernel, AvgDiagKernelNormalizer
- from modshogun import Time
+ from shogun import LinearKernel, AvgDiagKernelNormalizer
+ from shogun import Time
feats_train=RealFeatures(fm_train_real)
#feats_train.io.set_loglevel(MSG_DEBUG)
diff --git a/examples/undocumented/python/kernel_distance.py b/examples/undocumented/python/kernel_distance.py
index e54efd4201e..29ce767eefa 100644
--- a/examples/undocumented/python/kernel_distance.py
+++ b/examples/undocumented/python/kernel_distance.py
@@ -5,7 +5,7 @@
parameter_list=[[traindat,testdat,1.7],[traindat,testdat,1.8]]
def kernel_distance (train_fname=traindat,test_fname=testdat,width=1.7):
- from modshogun import RealFeatures, DistanceKernel, EuclideanDistance, CSVFile
+ from shogun import RealFeatures, DistanceKernel, EuclideanDistance, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/kernel_distantsegments.py b/examples/undocumented/python/kernel_distantsegments.py
index 7e50d76a363..082ec2f7fde 100644
--- a/examples/undocumented/python/kernel_distantsegments.py
+++ b/examples/undocumented/python/kernel_distantsegments.py
@@ -7,8 +7,8 @@
parameter_list = [[traindat,testdat,5,5],[traindat,testdat,6,6]]
def kernel_distantsegments (fm_train_dna=traindat,fm_test_dna=testdat,delta=5, theta=5):
- from modshogun import StringCharFeatures, DNA
- from modshogun import DistantSegmentsKernel
+ from shogun import StringCharFeatures, DNA
+ from shogun import DistantSegmentsKernel
feats_train=StringCharFeatures(fm_train_dna, DNA)
feats_test=StringCharFeatures(fm_test_dna, DNA)
diff --git a/examples/undocumented/python/kernel_exponential.py b/examples/undocumented/python/kernel_exponential.py
index 0726f339f59..4bf809a8642 100644
--- a/examples/undocumented/python/kernel_exponential.py
+++ b/examples/undocumented/python/kernel_exponential.py
@@ -5,7 +5,7 @@
parameter_list=[[traindat,testdat, 1.0],[traindat,testdat, 5.0]]
def kernel_exponential (train_fname=traindat,test_fname=testdat, tau_coef=1.0):
- from modshogun import RealFeatures, ExponentialKernel, EuclideanDistance, CSVFile
+ from shogun import RealFeatures, ExponentialKernel, EuclideanDistance, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/kernel_fisher.py b/examples/undocumented/python/kernel_fisher.py
index 10abaf60ee2..99a452e07ea 100644
--- a/examples/undocumented/python/kernel_fisher.py
+++ b/examples/undocumented/python/kernel_fisher.py
@@ -16,9 +16,9 @@ def kernel_fisher (fm_train_dna=traindat, fm_test_dna=testdat,
N=1,M=4,pseudo=1e-1,order=1,gap=0,reverse=False,
kargs=[1,False,True]):
- from modshogun import StringCharFeatures, StringWordFeatures, FKFeatures, DNA
- from modshogun import PolyKernel
- from modshogun import HMM, BW_NORMAL#, MSG_DEBUG
+ from shogun import StringCharFeatures, StringWordFeatures, FKFeatures, DNA
+ from shogun import PolyKernel
+ from shogun import HMM, BW_NORMAL#, MSG_DEBUG
# train HMM for positive class
charfeat=StringCharFeatures(fm_hmm_pos, DNA)
diff --git a/examples/undocumented/python/kernel_fixed_degree_string.py b/examples/undocumented/python/kernel_fixed_degree_string.py
index 47cbad80399..51c8a5d42f0 100644
--- a/examples/undocumented/python/kernel_fixed_degree_string.py
+++ b/examples/undocumented/python/kernel_fixed_degree_string.py
@@ -7,8 +7,8 @@
parameter_list=[[traindat, testdat,3],[traindat,testdat,4]]
def kernel_fixed_degree_string (fm_train_dna=traindat, fm_test_dna=testdat,degree=3):
- from modshogun import StringCharFeatures, DNA
- from modshogun import FixedDegreeStringKernel
+ from shogun import StringCharFeatures, DNA
+ from shogun import FixedDegreeStringKernel
feats_train=StringCharFeatures(fm_train_dna, DNA)
feats_test=StringCharFeatures(fm_test_dna, DNA)
diff --git a/examples/undocumented/python/kernel_gaussian.py b/examples/undocumented/python/kernel_gaussian.py
index c351ba9f389..181465ffe2d 100644
--- a/examples/undocumented/python/kernel_gaussian.py
+++ b/examples/undocumented/python/kernel_gaussian.py
@@ -5,7 +5,7 @@
parameter_list=[[traindat,testdat, 1.3],[traindat,testdat, 1.4]]
def kernel_gaussian (train_fname=traindat,test_fname=testdat, width=1.3):
- from modshogun import RealFeatures, GaussianKernel, CSVFile
+ from shogun import RealFeatures, GaussianKernel, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/kernel_gaussian_shift.py b/examples/undocumented/python/kernel_gaussian_shift.py
index b848f29d0ac..460dd43bedb 100644
--- a/examples/undocumented/python/kernel_gaussian_shift.py
+++ b/examples/undocumented/python/kernel_gaussian_shift.py
@@ -8,7 +8,7 @@
parameter_list=[[traindat,testdat,1.8,2,1],[traindat,testdat,1.9,2,1]]
def kernel_gaussian_shift (train_fname=traindat,test_fname=testdat,width=1.8,max_shift=2,shift_step=1):
- from modshogun import RealFeatures, GaussianShiftKernel, CSVFile
+ from shogun import RealFeatures, GaussianShiftKernel, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/kernel_histogram_word_string.py b/examples/undocumented/python/kernel_histogram_word_string.py
index 42644e6d14a..92b955b63e2 100644
--- a/examples/undocumented/python/kernel_histogram_word_string.py
+++ b/examples/undocumented/python/kernel_histogram_word_string.py
@@ -9,9 +9,9 @@
def kernel_histogram_word_string (fm_train_dna=traindat,fm_test_dna=testdat,label_train_dna=label_traindat,order=3,ppseudo_count=1,npseudo_count=1):
- from modshogun import StringCharFeatures, StringWordFeatures, DNA, BinaryLabels
- from modshogun import HistogramWordStringKernel, AvgDiagKernelNormalizer
- from modshogun import PluginEstimate#, MSG_DEBUG
+ from shogun import StringCharFeatures, StringWordFeatures, DNA, BinaryLabels
+ from shogun import HistogramWordStringKernel, AvgDiagKernelNormalizer
+ from shogun import PluginEstimate#, MSG_DEBUG
charfeat=StringCharFeatures(DNA)
#charfeat.io.set_loglevel(MSG_DEBUG)
diff --git a/examples/undocumented/python/kernel_inversemultiquadric.py b/examples/undocumented/python/kernel_inversemultiquadric.py
index b45bad8292b..4efefd7f4e1 100644
--- a/examples/undocumented/python/kernel_inversemultiquadric.py
+++ b/examples/undocumented/python/kernel_inversemultiquadric.py
@@ -5,7 +5,7 @@
parameter_list=[[traindat,testdat, 1.0],[traindat,testdat, 5.0]]
def kernel_inversemultiquadric (train_fname=traindat,test_fname=testdat, shift_coef=1.0):
- from modshogun import RealFeatures, InverseMultiQuadricKernel, EuclideanDistance, CSVFile
+ from shogun import RealFeatures, InverseMultiQuadricKernel, EuclideanDistance, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/kernel_io.py b/examples/undocumented/python/kernel_io.py
index 9aa9863e2ce..720cb738763 100644
--- a/examples/undocumented/python/kernel_io.py
+++ b/examples/undocumented/python/kernel_io.py
@@ -5,7 +5,7 @@
parameter_list=[[traindat,testdat,1.9],[traindat,testdat,1.7]]
def kernel_io (train_fname=traindat,test_fname=testdat,width=1.9):
- from modshogun import RealFeatures, GaussianKernel, CSVFile
+ from shogun import RealFeatures, GaussianKernel, CSVFile
from tempfile import NamedTemporaryFile
feats_train=RealFeatures(CSVFile(train_fname))
diff --git a/examples/undocumented/python/kernel_linear.py b/examples/undocumented/python/kernel_linear.py
index 79cbbb6a5dd..2f668b453b4 100644
--- a/examples/undocumented/python/kernel_linear.py
+++ b/examples/undocumented/python/kernel_linear.py
@@ -6,7 +6,7 @@
def kernel_linear (train_fname=traindat,test_fname=testdat,scale=1.2):
- from modshogun import RealFeatures, LinearKernel, AvgDiagKernelNormalizer, CSVFile
+ from shogun import RealFeatures, LinearKernel, AvgDiagKernelNormalizer, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/kernel_linear_byte.py b/examples/undocumented/python/kernel_linear_byte.py
index 798872cb755..41ebbee28a9 100644
--- a/examples/undocumented/python/kernel_linear_byte.py
+++ b/examples/undocumented/python/kernel_linear_byte.py
@@ -5,7 +5,7 @@
parameter_list=[[traindat,testdat],[traindat,testdat]]
def kernel_linear_byte (train_fname=traindat,test_fname=testdat):
- from modshogun import LinearKernel, ByteFeatures, CSVFile
+ from shogun import LinearKernel, ByteFeatures, CSVFile
feats_train=ByteFeatures(CSVFile(train_fname))
feats_test=ByteFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/kernel_linear_string.py b/examples/undocumented/python/kernel_linear_string.py
index 9b9f143cf60..22eeb1d63ef 100644
--- a/examples/undocumented/python/kernel_linear_string.py
+++ b/examples/undocumented/python/kernel_linear_string.py
@@ -7,8 +7,8 @@
parameter_list=[[traindat,testdat],[traindat,testdat]]
def kernel_linear_string (fm_train_dna=traindat,fm_test_dna=testdat):
- from modshogun import StringCharFeatures, DNA
- from modshogun import LinearStringKernel
+ from shogun import StringCharFeatures, DNA
+ from shogun import LinearStringKernel
feats_train=StringCharFeatures(fm_train_dna, DNA)
feats_test=StringCharFeatures(fm_test_dna, DNA)
diff --git a/examples/undocumented/python/kernel_linear_word.py b/examples/undocumented/python/kernel_linear_word.py
index bc1e2c917e8..1ac78017dd0 100644
--- a/examples/undocumented/python/kernel_linear_word.py
+++ b/examples/undocumented/python/kernel_linear_word.py
@@ -10,8 +10,8 @@
def kernel_linear_word (fm_train_word=traindat,fm_test_word=testdat,scale=1.2):
- from modshogun import LinearKernel, AvgDiagKernelNormalizer
- from modshogun import WordFeatures
+ from shogun import LinearKernel, AvgDiagKernelNormalizer
+ from shogun import WordFeatures
feats_train=WordFeatures(fm_train_word)
feats_test=WordFeatures(fm_test_word)
diff --git a/examples/undocumented/python/kernel_local_alignment_string.py b/examples/undocumented/python/kernel_local_alignment_string.py
index da0da5e5f1c..e2ab9aeb245 100644
--- a/examples/undocumented/python/kernel_local_alignment_string.py
+++ b/examples/undocumented/python/kernel_local_alignment_string.py
@@ -8,8 +8,8 @@
def kernel_local_alignment_string (fm_train_dna=traindat,fm_test_dna=testdat):
- from modshogun import StringCharFeatures, DNA
- from modshogun import LocalAlignmentStringKernel
+ from shogun import StringCharFeatures, DNA
+ from shogun import LocalAlignmentStringKernel
feats_train=StringCharFeatures(fm_train_dna, DNA)
feats_test=StringCharFeatures(fm_test_dna, DNA)
diff --git a/examples/undocumented/python/kernel_locality_improved_string.py b/examples/undocumented/python/kernel_locality_improved_string.py
index f885cabc2aa..95d01f499b0 100644
--- a/examples/undocumented/python/kernel_locality_improved_string.py
+++ b/examples/undocumented/python/kernel_locality_improved_string.py
@@ -8,8 +8,8 @@
def kernel_locality_improved_string (fm_train_dna=traindat,fm_test_dna=testdat,length=5,inner_degree=5,outer_degree=7):
- from modshogun import StringCharFeatures, DNA
- from modshogun import LocalityImprovedStringKernel
+ from shogun import StringCharFeatures, DNA
+ from shogun import LocalityImprovedStringKernel
feats_train=StringCharFeatures(fm_train_dna, DNA)
feats_test=StringCharFeatures(fm_test_dna, DNA)
diff --git a/examples/undocumented/python/kernel_log.py b/examples/undocumented/python/kernel_log.py
index 76d037f415f..3c6cc15f84b 100644
--- a/examples/undocumented/python/kernel_log.py
+++ b/examples/undocumented/python/kernel_log.py
@@ -5,7 +5,7 @@
parameter_list=[[traindat,testdat, 2.0],[traindat,testdat, 3.0]]
def kernel_log (train_fname=traindat,test_fname=testdat, degree=2.0):
- from modshogun import RealFeatures, LogKernel, EuclideanDistance, CSVFile
+ from shogun import RealFeatures, LogKernel, EuclideanDistance, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/kernel_match_word_string.py b/examples/undocumented/python/kernel_match_word_string.py
index 9c7630fb18c..f7a81e02162 100644
--- a/examples/undocumented/python/kernel_match_word_string.py
+++ b/examples/undocumented/python/kernel_match_word_string.py
@@ -9,8 +9,8 @@
def kernel_match_word_string (fm_train_dna=traindat,fm_test_dna=testdat,
degree=3,scale=1.4,size_cache=10,order=3,gap=0,reverse=False):
- from modshogun import MatchWordStringKernel, AvgDiagKernelNormalizer
- from modshogun import StringWordFeatures, StringCharFeatures, DNA
+ from shogun import MatchWordStringKernel, AvgDiagKernelNormalizer
+ from shogun import StringWordFeatures, StringCharFeatures, DNA
charfeat=StringCharFeatures(fm_train_dna, DNA)
feats_train=StringWordFeatures(DNA)
diff --git a/examples/undocumented/python/kernel_multiquadric.py b/examples/undocumented/python/kernel_multiquadric.py
index 6b0ffe038bf..f377e35ebc7 100644
--- a/examples/undocumented/python/kernel_multiquadric.py
+++ b/examples/undocumented/python/kernel_multiquadric.py
@@ -6,7 +6,7 @@
parameter_list=[[traindat,testdat, 1.0],[traindat,testdat, 5.0]]
def kernel_multiquadric (train_fname=traindat,test_fname=testdat, shift_coef=1.0):
- from modshogun import RealFeatures, MultiquadricKernel, EuclideanDistance, CSVFile
+ from shogun import RealFeatures, MultiquadricKernel, EuclideanDistance, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/kernel_oligo_string.py b/examples/undocumented/python/kernel_oligo_string.py
index 7b09109bfdb..466a739ef0d 100644
--- a/examples/undocumented/python/kernel_oligo_string.py
+++ b/examples/undocumented/python/kernel_oligo_string.py
@@ -7,8 +7,8 @@
parameter_list = [[traindat,testdat,3,1.2,10],[traindat,testdat,4,1.3,10]]
def kernel_oligo_string (fm_train_dna=traindat,fm_test_dna=testdat,k=3,width=1.2,size_cache=10):
- from modshogun import StringCharFeatures, DNA
- from modshogun import OligoStringKernel
+ from shogun import StringCharFeatures, DNA
+ from shogun import OligoStringKernel
feats_train=StringCharFeatures(fm_train_dna, DNA)
feats_test=StringCharFeatures(fm_test_dna, DNA)
diff --git a/examples/undocumented/python/kernel_poly.py b/examples/undocumented/python/kernel_poly.py
index d83bc583f3b..56b481a59be 100644
--- a/examples/undocumented/python/kernel_poly.py
+++ b/examples/undocumented/python/kernel_poly.py
@@ -6,7 +6,7 @@
def kernel_poly (train_fname=traindat,test_fname=testdat,degree=4,inhomogene=False,
use_normalization=True):
- from modshogun import RealFeatures, PolyKernel, CSVFile
+ from shogun import RealFeatures, PolyKernel, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/kernel_poly_match_string.py b/examples/undocumented/python/kernel_poly_match_string.py
index a3a385e3035..2fc6bb0ce5a 100644
--- a/examples/undocumented/python/kernel_poly_match_string.py
+++ b/examples/undocumented/python/kernel_poly_match_string.py
@@ -6,8 +6,8 @@
parameter_list = [[traindat,testdat,3,False],[traindat,testdat,4,False]]
def kernel_poly_match_string (fm_train_dna=traindat,fm_test_dna=testdat,degree=3,inhomogene=False):
- from modshogun import PolyMatchStringKernel
- from modshogun import StringCharFeatures, DNA
+ from shogun import PolyMatchStringKernel
+ from shogun import StringCharFeatures, DNA
feats_train=StringCharFeatures(fm_train_dna, DNA)
feats_test=StringCharFeatures(fm_train_dna, DNA)
diff --git a/examples/undocumented/python/kernel_poly_match_word_string.py b/examples/undocumented/python/kernel_poly_match_word_string.py
index 85c971f99d9..c2ac3017603 100644
--- a/examples/undocumented/python/kernel_poly_match_word_string.py
+++ b/examples/undocumented/python/kernel_poly_match_word_string.py
@@ -8,8 +8,8 @@
def kernel_poly_match_word_string (fm_train_dna=traindat,fm_test_dna=testdat,
degree=2,inhomogene=True,order=3,gap=0,reverse=False):
- from modshogun import PolyMatchWordStringKernel
- from modshogun import StringWordFeatures, StringCharFeatures, DNA
+ from shogun import PolyMatchWordStringKernel
+ from shogun import StringWordFeatures, StringCharFeatures, DNA
diff --git a/examples/undocumented/python/kernel_power.py b/examples/undocumented/python/kernel_power.py
index de6e40f5cb5..52c7c471808 100644
--- a/examples/undocumented/python/kernel_power.py
+++ b/examples/undocumented/python/kernel_power.py
@@ -5,7 +5,7 @@
parameter_list=[[traindat,testdat, 2.0],[traindat,testdat, 3.0]]
def kernel_power (train_fname=traindat,test_fname=testdat, degree=2.0):
- from modshogun import RealFeatures, PowerKernel, EuclideanDistance, CSVFile
+ from shogun import RealFeatures, PowerKernel, EuclideanDistance, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/kernel_rationalquadratic.py b/examples/undocumented/python/kernel_rationalquadratic.py
index 6c7b5fccd2f..f2e4a991c7a 100644
--- a/examples/undocumented/python/kernel_rationalquadratic.py
+++ b/examples/undocumented/python/kernel_rationalquadratic.py
@@ -5,7 +5,7 @@
parameter_list=[[traindat,testdat, 1.0],[traindat,testdat, 5.0]]
def kernel_rationalquadratic (train_fname=traindat,test_fname=testdat, shift_coef=1.0):
- from modshogun import RealFeatures, RationalQuadraticKernel, EuclideanDistance, CSVFile
+ from shogun import RealFeatures, RationalQuadraticKernel, EuclideanDistance, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/kernel_salzberg_word_string.py b/examples/undocumented/python/kernel_salzberg_word_string.py
index b979b6f0bf8..88a4773896e 100644
--- a/examples/undocumented/python/kernel_salzberg_word_string.py
+++ b/examples/undocumented/python/kernel_salzberg_word_string.py
@@ -8,9 +8,9 @@
parameter_list = [[traindat,testdat,label_traindat,3,0,False],[traindat,testdat,label_traindat,3,0,False]]
def kernel_salzberg_word_string (fm_train_dna=traindat,fm_test_dna=testdat,label_train_dna=label_traindat,
order=3,gap=0,reverse=False):
- from modshogun import StringCharFeatures, StringWordFeatures, DNA, BinaryLabels
- from modshogun import SalzbergWordStringKernel
- from modshogun import PluginEstimate
+ from shogun import StringCharFeatures, StringWordFeatures, DNA, BinaryLabels
+ from shogun import SalzbergWordStringKernel
+ from shogun import PluginEstimate
charfeat=StringCharFeatures(fm_train_dna, DNA)
feats_train=StringWordFeatures(charfeat.get_alphabet())
diff --git a/examples/undocumented/python/kernel_sigmoid.py b/examples/undocumented/python/kernel_sigmoid.py
index 37586303dce..64ad32595ba 100644
--- a/examples/undocumented/python/kernel_sigmoid.py
+++ b/examples/undocumented/python/kernel_sigmoid.py
@@ -5,7 +5,7 @@
parameter_list = [[traindat,testdat,10,1.2,1.3],[traindat,testdat,10,1.2,1.3]]
def kernel_sigmoid (train_fname=traindat,test_fname=testdat,size_cache=10,gamma=1.2,coef0=1.3):
- from modshogun import RealFeatures, SigmoidKernel, CSVFile
+ from shogun import RealFeatures, SigmoidKernel, CSVFile
feats_train=RealFeatures(CSVFile(train_fname))
feats_test=RealFeatures(CSVFile(test_fname))
diff --git a/examples/undocumented/python/kernel_simple_locality_improved_string.py b/examples/undocumented/python/kernel_simple_locality_improved_string.py
index 66285167554..cd9e33053d8 100644
--- a/examples/undocumented/python/kernel_simple_locality_improved_string.py
+++ b/examples/undocumented/python/kernel_simple_locality_improved_string.py
@@ -9,8 +9,8 @@
def kernel_simple_locality_improved_string (fm_train_dna=traindat,fm_test_dna=testdat,
length=5,inner_degree=5,outer_degree=1 ):
- from modshogun import StringCharFeatures, DNA
- from modshogun import SimpleLocalityImprovedStringKernel, MSG_DEBUG
+ from shogun import StringCharFeatures, DNA
+ from shogun import SimpleLocalityImprovedStringKernel, MSG_DEBUG
feats_train=StringCharFeatures(fm_train_dna, DNA)
#feats_train.io.set_loglevel(MSG_DEBUG)
diff --git a/examples/undocumented/python/kernel_sparse_gaussian.py b/examples/undocumented/python/kernel_sparse_gaussian.py
index e1b35f8ff1a..1ad4623d420 100644
--- a/examples/undocumented/python/kernel_sparse_gaussian.py
+++ b/examples/undocumented/python/kernel_sparse_gaussian.py
@@ -7,8 +7,8 @@
parameter_list = [[traindat,testdat,1.1],[traindat,testdat,1.2]]
def kernel_sparse_gaussian (fm_train_real=traindat,fm_test_real=testdat,width=1.1 ):
- from modshogun import SparseRealFeatures
- from modshogun import GaussianKernel
+ from shogun import SparseRealFeatures
+ from shogun import GaussianKernel
feats_train=SparseRealFeatures(fm_train_real)
feats_test=SparseRealFeatures(fm_test_real)
diff --git a/examples/undocumented/python/kernel_sparse_linear.py b/examples/undocumented/python/kernel_sparse_linear.py
index 6989feba921..8ca21aa1831 100644
--- a/examples/undocumented/python/kernel_sparse_linear.py
+++ b/examples/undocumented/python/kernel_sparse_linear.py
@@ -8,8 +8,8 @@
parameter_list = [[traindat,testdat,1.1],[traindat,testdat,1.2]]
def kernel_sparse_linear (fm_train_real=traindat,fm_test_real=testdat,scale=1.1):
- from modshogun import SparseRealFeatures
- from modshogun import LinearKernel, AvgDiagKernelNormalizer
+ from shogun import SparseRealFeatures
+ from shogun import LinearKernel, AvgDiagKernelNormalizer
feats_train=SparseRealFeatures(fm_train_real)
feats_test=SparseRealFeatures(fm_test_real)
diff --git a/examples/undocumented/python/kernel_sparse_poly.py b/examples/undocumented/python/kernel_sparse_poly.py
index 29e3ad2c540..b881012da50 100644
--- a/examples/undocumented/python/kernel_sparse_poly.py
+++ b/examples/undocumented/python/kernel_sparse_poly.py
@@ -9,8 +9,8 @@
def kernel_sparse_poly (fm_train_real=traindat,fm_test_real=testdat,
size_cache=10,degree=3,inhomogene=True ):
- from modshogun import SparseRealFeatures
- from modshogun import PolyKernel
+ from shogun import SparseRealFeatures
+ from shogun import PolyKernel
feats_train=SparseRealFeatures(fm_train_real)
feats_test=SparseRealFeatures(fm_test_real)
diff --git a/examples/undocumented/python/kernel_spherical.py b/examples/undocumented/python/kernel_spherical.py
index e2d9f39161d..52a429e8a99 100644
--- a/examples/undocumented/python/kernel_spherical.py
+++ b/examples/undocumented/python/kernel_spherical.py
@@ -9,9 +9,9 @@
parameter_list=[[traindat,testdat, 1.0],[traindat,testdat, 5.0]]
def kernel_spherical (fm_train_real=traindat,fm_test_real=testdat, sigma=1.0):
- from modshogun import RealFeatures
- from modshogun import MultiquadricKernel
- from modshogun import EuclideanDistance
+ from shogun import RealFeatures
+ from shogun import MultiquadricKernel
+ from shogun import EuclideanDistance
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
diff --git a/examples/undocumented/python/kernel_spline.py b/examples/undocumented/python/kernel_spline.py
index 5c5785920a7..26aae43c802 100644
--- a/examples/undocumented/python/kernel_spline.py
+++ b/examples/undocumented/python/kernel_spline.py
@@ -9,8 +9,8 @@
parameter_list=[[traindat,testdat],[traindat,testdat]]
def kernel_spline (fm_train_real=traindat,fm_test_real=testdat):
- from modshogun import RealFeatures
- from modshogun import SplineKernel
+ from shogun import RealFeatures
+ from shogun import SplineKernel
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
diff --git a/examples/undocumented/python/kernel_ssk_string.py b/examples/undocumented/python/kernel_ssk_string.py
index 3a19ca83c59..bfa5e28ed1a 100644
--- a/examples/undocumented/python/kernel_ssk_string.py
+++ b/examples/undocumented/python/kernel_ssk_string.py
@@ -18,8 +18,8 @@
parameter_list = [[traindat,testdat,2,0.75],[traindat,testdat,3,0.75]]
def kernel_ssk_string (fm_train_dna=traindat, fm_test_dna=testdat, maxlen=1, decay=1):
- from modshogun import SubsequenceStringKernel
- from modshogun import StringCharFeatures, DNA
+ from shogun import SubsequenceStringKernel
+ from shogun import StringCharFeatures, DNA
feats_train=StringCharFeatures(fm_train_dna, DNA)
feats_test=StringCharFeatures(fm_test_dna, DNA)
diff --git a/examples/undocumented/python/kernel_top.py b/examples/undocumented/python/kernel_top.py
index cb5835f75ea..5f76324121e 100644
--- a/examples/undocumented/python/kernel_top.py
+++ b/examples/undocumented/python/kernel_top.py
@@ -15,9 +15,9 @@
def kernel_top (fm_train_dna=traindat,fm_test_dna=testdat,label_train_dna=label_traindat,pseudo=1e-1,
order=1,gap=0,reverse=False,kargs=[1, False, True]):
- from modshogun import StringCharFeatures, StringWordFeatures, TOPFeatures, DNA
- from modshogun import PolyKernel
- from modshogun import HMM, BW_NORMAL
+ from shogun import StringCharFeatures, StringWordFeatures, TOPFeatures, DNA
+ from shogun import PolyKernel
+ from shogun import HMM, BW_NORMAL
N=1 # toy HMM with 1 state
M=4 # 4 observations -> DNA
diff --git a/examples/undocumented/python/kernel_tstudent.py b/examples/undocumented/python/kernel_tstudent.py
index f3df33e1967..3cca9640459 100644
--- a/examples/undocumented/python/kernel_tstudent.py
+++ b/examples/undocumented/python/kernel_tstudent.py
@@ -9,9 +9,9 @@
parameter_list=[[traindat,testdat, 2.0],[traindat,testdat, 3.0]]
def kernel_tstudent (fm_train_real=traindat,fm_test_real=testdat, degree=2.0):
- from modshogun import RealFeatures
- from modshogun import TStudentKernel
- from modshogun import EuclideanDistance
+ from shogun import RealFeatures
+ from shogun import TStudentKernel
+ from shogun import EuclideanDistance
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
diff --git a/examples/undocumented/python/kernel_wave.py b/examples/undocumented/python/kernel_wave.py
index 3935711cbb3..accf91cbe96 100644
--- a/examples/undocumented/python/kernel_wave.py
+++ b/examples/undocumented/python/kernel_wave.py
@@ -9,9 +9,9 @@
parameter_list=[[traindat,testdat, 1.0],[traindat,testdat, 10.0]]
def kernel_wave (fm_train_real=traindat,fm_test_real=testdat, theta=1.0):
- from modshogun import RealFeatures
- from modshogun import WaveKernel
- from modshogun import EuclideanDistance
+ from shogun import RealFeatures
+ from shogun import WaveKernel
+ from shogun import EuclideanDistance
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
diff --git a/examples/undocumented/python/kernel_wavelet.py b/examples/undocumented/python/kernel_wavelet.py
index cf92ea7d384..f18df45b22b 100644
--- a/examples/undocumented/python/kernel_wavelet.py
+++ b/examples/undocumented/python/kernel_wavelet.py
@@ -9,8 +9,8 @@
parameter_list=[[traindat,testdat, 1.5, 1.0],[traindat,testdat, 1.0, 1.5]]
def kernel_wavelet (fm_train_real=traindat,fm_test_real=testdat, dilation=1.5, translation=1.0):
- from modshogun import RealFeatures
- from modshogun import WaveletKernel
+ from shogun import RealFeatures
+ from shogun import WaveletKernel
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
diff --git a/examples/undocumented/python/kernel_weighted_comm_word_string.py b/examples/undocumented/python/kernel_weighted_comm_word_string.py
index 23d7d8bdf6e..763d4705b27 100644
--- a/examples/undocumented/python/kernel_weighted_comm_word_string.py
+++ b/examples/undocumented/python/kernel_weighted_comm_word_string.py
@@ -7,9 +7,9 @@
parameter_list = [[traindat,testdat],[traindat,testdat]]
def kernel_weighted_comm_word_string (fm_train_dna=traindat,fm_test_dna=testdat,order=3,gap=0,reverse=True ):
- from modshogun import WeightedCommWordStringKernel
- from modshogun import StringWordFeatures, StringCharFeatures, DNA
- from modshogun import SortWordString
+ from shogun import WeightedCommWordStringKernel
+ from shogun import StringWordFeatures, StringCharFeatures, DNA
+ from shogun import SortWordString
charfeat=StringCharFeatures(fm_train_dna, DNA)
feats_train=StringWordFeatures(charfeat.get_alphabet())
diff --git a/examples/undocumented/python/kernel_weighted_degree_position_string.py b/examples/undocumented/python/kernel_weighted_degree_position_string.py
index f49e2c68c52..9c515b12ecf 100644
--- a/examples/undocumented/python/kernel_weighted_degree_position_string.py
+++ b/examples/undocumented/python/kernel_weighted_degree_position_string.py
@@ -7,8 +7,8 @@
parameter_list = [[traindat,testdat,20],[traindat,testdat,22]]
def kernel_weighted_degree_position_string (fm_train_dna=traindat,fm_test_dna=testdat,degree=20):
- from modshogun import StringCharFeatures, DNA
- from modshogun import WeightedDegreePositionStringKernel, MSG_DEBUG
+ from shogun import StringCharFeatures, DNA
+ from shogun import WeightedDegreePositionStringKernel, MSG_DEBUG
feats_train=StringCharFeatures(fm_train_dna, DNA)
#feats_train.io.set_loglevel(MSG_DEBUG)
diff --git a/examples/undocumented/python/kernel_weighted_degree_string.py b/examples/undocumented/python/kernel_weighted_degree_string.py
index 60ea708ab21..96c131ceba0 100644
--- a/examples/undocumented/python/kernel_weighted_degree_string.py
+++ b/examples/undocumented/python/kernel_weighted_degree_string.py
@@ -7,8 +7,8 @@
parameter_list = [[traindat,testdat,3],[traindat,testdat,20]]
def kernel_weighted_degree_string (fm_train_dna=traindat,fm_test_dna=testdat,degree=20):
- from modshogun import StringCharFeatures, DNA
- from modshogun import WeightedDegreeStringKernel, MSG_DEBUG
+ from shogun import StringCharFeatures, DNA
+ from shogun import WeightedDegreeStringKernel, MSG_DEBUG
feats_train=StringCharFeatures(fm_train_dna, DNA)
#feats_train.io.set_loglevel(MSG_DEBUG)
diff --git a/examples/undocumented/python/labels_io.py b/examples/undocumented/python/labels_io.py
index e88dc0fea81..b98179d0d6f 100644
--- a/examples/undocumented/python/labels_io.py
+++ b/examples/undocumented/python/labels_io.py
@@ -2,7 +2,7 @@
parameter_list=[[]]
def labels_io():
- from modshogun import RegressionLabels, CSVFile
+ from shogun import RegressionLabels, CSVFile
lab=RegressionLabels()
f=CSVFile("../data/label_train_regression.dat","r")
f.set_delimiter(" ")
diff --git a/examples/undocumented/python/library_fisher2x3.py b/examples/undocumented/python/library_fisher2x3.py
index a4c56f030e8..fc6de163164 100644
--- a/examples/undocumented/python/library_fisher2x3.py
+++ b/examples/undocumented/python/library_fisher2x3.py
@@ -1,6 +1,6 @@
#!/usr/bin/env python
from numpy import *
-from modshogun import *
+from shogun import *
x=array([[20.0,15,15],[10,20,20]])
y=array([[21.0,21,18],[19,19,22]])
diff --git a/examples/undocumented/python/library_time.py b/examples/undocumented/python/library_time.py
index 58ef6ea6779..cf7fde8309e 100644
--- a/examples/undocumented/python/library_time.py
+++ b/examples/undocumented/python/library_time.py
@@ -1,6 +1,6 @@
#!/usr/bin/env python
import time
-from modshogun import Time
+from shogun import Time
parameter_list = [[5],[1.0]]
def library_time (sleep_secs):
diff --git a/examples/undocumented/python/mathematics_sparseinversecovariance.py b/examples/undocumented/python/mathematics_sparseinversecovariance.py
index e047ece8fa7..3f82d661005 100644
--- a/examples/undocumented/python/mathematics_sparseinversecovariance.py
+++ b/examples/undocumented/python/mathematics_sparseinversecovariance.py
@@ -8,7 +8,7 @@
def mathematics_sparseinversecovariance (data,lc):
try:
- from modshogun import SparseInverseCovariance
+ from shogun import SparseInverseCovariance
except ImportError:
print("SparseInverseCovariance not available")
exit(0)
diff --git a/examples/undocumented/python/metric_lmnn.py b/examples/undocumented/python/metric_lmnn.py
index eafb4e9b728..f9aa05d1184 100644
--- a/examples/undocumented/python/metric_lmnn.py
+++ b/examples/undocumented/python/metric_lmnn.py
@@ -8,7 +8,7 @@
def metric_lmnn(train_fname=traindat,test_fname=testdat,label_train_fname=label_traindat,k=3):
try:
- from modshogun import RealFeatures,MulticlassLabels,LMNN,KNN,CSVFile
+ from shogun import RealFeatures,MulticlassLabels,LMNN,KNN,CSVFile
except ImportError:
return
diff --git a/examples/undocumented/python/mkl_binclass.py b/examples/undocumented/python/mkl_binclass.py
index 1a72fbda3cb..c5211faa5e4 100644
--- a/examples/undocumented/python/mkl_binclass.py
+++ b/examples/undocumented/python/mkl_binclass.py
@@ -1,13 +1,13 @@
#!/usr/bin/env python
-from modshogun import CombinedFeatures, RealFeatures, BinaryLabels
-from modshogun import CombinedKernel, PolyKernel, CustomKernel
-from modshogun import MKLClassification
+from shogun import CombinedFeatures, RealFeatures, BinaryLabels
+from shogun import CombinedKernel, PolyKernel, CustomKernel
+from shogun import MKLClassification
from tools.load import LoadMatrix
lm=LoadMatrix()
#only run example if SVMLight is included as LibSVM solver crashes in MKLClassification
try:
- from modshogun import SVMLight
+ from shogun import SVMLight
except ImportError:
print("SVMLight not available")
exit(0)
diff --git a/examples/undocumented/python/mkl_multiclass.py b/examples/undocumented/python/mkl_multiclass.py
index 61df41ee970..d88cc7bdf58 100644
--- a/examples/undocumented/python/mkl_multiclass.py
+++ b/examples/undocumented/python/mkl_multiclass.py
@@ -12,9 +12,9 @@
def mkl_multiclass (fm_train_real, fm_test_real, label_train_multiclass,
width, C, epsilon, num_threads, mkl_epsilon, mkl_norm):
- from modshogun import CombinedFeatures, RealFeatures, MulticlassLabels
- from modshogun import CombinedKernel, GaussianKernel, LinearKernel,PolyKernel
- from modshogun import MKLMulticlass
+ from shogun import CombinedFeatures, RealFeatures, MulticlassLabels
+ from shogun import CombinedKernel, GaussianKernel, LinearKernel,PolyKernel
+ from shogun import MKLMulticlass
kernel = CombinedKernel()
feats_train = CombinedFeatures()
diff --git a/examples/undocumented/python/modelselection_grid_search_kernel.py b/examples/undocumented/python/modelselection_grid_search_kernel.py
index 6f41f34b28a..63caf4dddab 100644
--- a/examples/undocumented/python/modelselection_grid_search_kernel.py
+++ b/examples/undocumented/python/modelselection_grid_search_kernel.py
@@ -12,18 +12,18 @@
from numpy import random
import math
-from modshogun import CrossValidation, CrossValidationResult
-from modshogun import ContingencyTableEvaluation, ACCURACY
-from modshogun import StratifiedCrossValidationSplitting
-from modshogun import BinaryLabels
-from modshogun import RealFeatures
-from modshogun import GaussianKernel, PowerKernel
-from modshogun import LibSVM
-from modshogun import MinkowskiMetric
-from modshogun import GridSearchModelSelection
-from modshogun import ModelSelectionParameters, R_EXP, R_LINEAR
-from modshogun import ParameterCombination
-from modshogun import Math
+from shogun import CrossValidation, CrossValidationResult
+from shogun import ContingencyTableEvaluation, ACCURACY
+from shogun import StratifiedCrossValidationSplitting
+from shogun import BinaryLabels
+from shogun import RealFeatures
+from shogun import GaussianKernel, PowerKernel
+from shogun import LibSVM
+from shogun import MinkowskiMetric
+from shogun import GridSearchModelSelection
+from shogun import ModelSelectionParameters, R_EXP, R_LINEAR
+from shogun import ParameterCombination
+from shogun import Math
def create_param_tree():
root=ModelSelectionParameters()
diff --git a/examples/undocumented/python/modelselection_grid_search_krr.py b/examples/undocumented/python/modelselection_grid_search_krr.py
index 64f9ec3cc00..dd673c53ede 100644
--- a/examples/undocumented/python/modelselection_grid_search_krr.py
+++ b/examples/undocumented/python/modelselection_grid_search_krr.py
@@ -24,14 +24,14 @@
def modelselection_grid_search_krr (fm_train=traindat,fm_test=testdat,label_train=label_traindat,\
width=2.1,C=1,epsilon=1e-5,tube_epsilon=1e-2):
- from modshogun import CrossValidation, CrossValidationResult
- from modshogun import MeanSquaredError
- from modshogun import CrossValidationSplitting
- from modshogun import RegressionLabels
- from modshogun import RealFeatures
- from modshogun import KernelRidgeRegression
- from modshogun import GridSearchModelSelection
- from modshogun import ModelSelectionParameters
+ from shogun import CrossValidation, CrossValidationResult
+ from shogun import MeanSquaredError
+ from shogun import CrossValidationSplitting
+ from shogun import RegressionLabels
+ from shogun import RealFeatures
+ from shogun import KernelRidgeRegression
+ from shogun import GridSearchModelSelection
+ from shogun import ModelSelectionParameters
# training data
features_train=RealFeatures(traindat)
@@ -91,9 +91,9 @@ def modelselection_grid_search_krr (fm_train=traindat,fm_test=testdat,label_trai
# creates all the parameters to optimize
def create_param_tree():
- from modshogun import ModelSelectionParameters, R_EXP, R_LINEAR
- from modshogun import ParameterCombination
- from modshogun import GaussianKernel, PolyKernel
+ from shogun import ModelSelectionParameters, R_EXP, R_LINEAR
+ from shogun import ParameterCombination
+ from shogun import GaussianKernel, PolyKernel
import math
root=ModelSelectionParameters()
diff --git a/examples/undocumented/python/modelselection_grid_search_liblinear.py b/examples/undocumented/python/modelselection_grid_search_liblinear.py
index 89788dc25e3..dd267c97f32 100644
--- a/examples/undocumented/python/modelselection_grid_search_liblinear.py
+++ b/examples/undocumented/python/modelselection_grid_search_liblinear.py
@@ -22,15 +22,15 @@
parameter_list = [[traindat,label_traindat]]
def modelselection_grid_search_liblinear (traindat=traindat, label_traindat=label_traindat):
- from modshogun import CrossValidation, CrossValidationResult
- from modshogun import ContingencyTableEvaluation, ACCURACY
- from modshogun import StratifiedCrossValidationSplitting
- from modshogun import GridSearchModelSelection
- from modshogun import ModelSelectionParameters, R_EXP
- from modshogun import ParameterCombination
- from modshogun import BinaryLabels
- from modshogun import RealFeatures
- from modshogun import LibLinear, L2R_L2LOSS_SVC
+ from shogun import CrossValidation, CrossValidationResult
+ from shogun import ContingencyTableEvaluation, ACCURACY
+ from shogun import StratifiedCrossValidationSplitting
+ from shogun import GridSearchModelSelection
+ from shogun import ModelSelectionParameters, R_EXP
+ from shogun import ParameterCombination
+ from shogun import BinaryLabels
+ from shogun import RealFeatures
+ from shogun import LibLinear, L2R_L2LOSS_SVC
# build parameter tree to select C1 and C2
param_tree_root=ModelSelectionParameters()
diff --git a/examples/undocumented/python/modelselection_grid_search_libsvr.py b/examples/undocumented/python/modelselection_grid_search_libsvr.py
index 5fc5b9e9e16..deaf18a7495 100644
--- a/examples/undocumented/python/modelselection_grid_search_libsvr.py
+++ b/examples/undocumented/python/modelselection_grid_search_libsvr.py
@@ -24,16 +24,16 @@
def modelselection_grid_search_libsvr (fm_train=traindat,fm_test=testdat,label_train=label_traindat,\
width=2.1,C=1,epsilon=1e-5,tube_epsilon=1e-2):
- from modshogun import CrossValidation, CrossValidationResult
- from modshogun import MeanSquaredError
- from modshogun import CrossValidationSplitting
- from modshogun import RegressionLabels
- from modshogun import RealFeatures
- from modshogun import GaussianKernel
- from modshogun import LibSVR
- from modshogun import GridSearchModelSelection
- from modshogun import ModelSelectionParameters, R_EXP
- from modshogun import ParameterCombination
+ from shogun import CrossValidation, CrossValidationResult
+ from shogun import MeanSquaredError
+ from shogun import CrossValidationSplitting
+ from shogun import RegressionLabels
+ from shogun import RealFeatures
+ from shogun import GaussianKernel
+ from shogun import LibSVR
+ from shogun import GridSearchModelSelection
+ from shogun import ModelSelectionParameters, R_EXP
+ from shogun import ParameterCombination
# training data
features_train=RealFeatures(traindat)
diff --git a/examples/undocumented/python/modelselection_parameter_tree.py b/examples/undocumented/python/modelselection_parameter_tree.py
index f756a86af04..d1b221bbdb8 100644
--- a/examples/undocumented/python/modelselection_parameter_tree.py
+++ b/examples/undocumented/python/modelselection_parameter_tree.py
@@ -12,12 +12,12 @@
parameter_list=[[None]]
def modelselection_parameter_tree (dummy):
- from modshogun import ParameterCombination
- from modshogun import ModelSelectionParameters, R_EXP, R_LINEAR
- from modshogun import PowerKernel
- from modshogun import GaussianKernel
- from modshogun import DistantSegmentsKernel
- from modshogun import MinkowskiMetric
+ from shogun import ParameterCombination
+ from shogun import ModelSelectionParameters, R_EXP, R_LINEAR
+ from shogun import PowerKernel
+ from shogun import GaussianKernel
+ from shogun import DistantSegmentsKernel
+ from shogun import MinkowskiMetric
import math
root=ModelSelectionParameters()
diff --git a/examples/undocumented/python/modelselection_random_search_liblinear.py b/examples/undocumented/python/modelselection_random_search_liblinear.py
index 466637ce245..3eef55e44ba 100644
--- a/examples/undocumented/python/modelselection_random_search_liblinear.py
+++ b/examples/undocumented/python/modelselection_random_search_liblinear.py
@@ -20,15 +20,15 @@
parameter_list = [[traindat,label_traindat]]
def modelselection_random_search_liblinear (traindat=traindat, label_traindat=label_traindat):
- from modshogun import CrossValidation, CrossValidationResult
- from modshogun import ContingencyTableEvaluation, ACCURACY
- from modshogun import StratifiedCrossValidationSplitting
- from modshogun import RandomSearchModelSelection
- from modshogun import ModelSelectionParameters, R_EXP
- from modshogun import ParameterCombination
- from modshogun import BinaryLabels
- from modshogun import RealFeatures
- from modshogun import LibLinear, L2R_L2LOSS_SVC
+ from shogun import CrossValidation, CrossValidationResult
+ from shogun import ContingencyTableEvaluation, ACCURACY
+ from shogun import StratifiedCrossValidationSplitting
+ from shogun import RandomSearchModelSelection
+ from shogun import ModelSelectionParameters, R_EXP
+ from shogun import ParameterCombination
+ from shogun import BinaryLabels
+ from shogun import RealFeatures
+ from shogun import LibLinear, L2R_L2LOSS_SVC
# build parameter tree to select C1 and C2
param_tree_root=ModelSelectionParameters()
diff --git a/examples/undocumented/python/multiclass_c45classifiertree.py b/examples/undocumented/python/multiclass_c45classifiertree.py
index a6364e4c6f1..aca26ef09b3 100644
--- a/examples/undocumented/python/multiclass_c45classifiertree.py
+++ b/examples/undocumented/python/multiclass_c45classifiertree.py
@@ -12,7 +12,7 @@
def multiclass_c45classifiertree(train=traindat,test=testdat,labels=label_traindat,ft=feattypes):
try:
- from modshogun import RealFeatures, MulticlassLabels, CSVFile, C45ClassifierTree
+ from shogun import RealFeatures, MulticlassLabels, CSVFile, C45ClassifierTree
from numpy import random, int32
except ImportError:
print("Could not import Shogun and/or numpy modules")
diff --git a/examples/undocumented/python/multiclass_id3classifiertree.py b/examples/undocumented/python/multiclass_id3classifiertree.py
index e9fcccafd13..58f7cb0ed74 100644
--- a/examples/undocumented/python/multiclass_id3classifiertree.py
+++ b/examples/undocumented/python/multiclass_id3classifiertree.py
@@ -18,7 +18,7 @@
def multiclass_id3classifiertree(train=train_data,labels=train_labels,test=test_data):
try:
- from modshogun import RealFeatures, MulticlassLabels, ID3ClassifierTree
+ from shogun import RealFeatures, MulticlassLabels, ID3ClassifierTree
except ImportError:
return
diff --git a/examples/undocumented/python/multiclass_randomforest.py b/examples/undocumented/python/multiclass_randomforest.py
index b1c91c1a582..a110b58e739 100644
--- a/examples/undocumented/python/multiclass_randomforest.py
+++ b/examples/undocumented/python/multiclass_randomforest.py
@@ -12,7 +12,7 @@
def multiclass_randomforest(train=traindat,test=testdat,labels=label_traindat,ft=feattypes):
try:
- from modshogun import RealFeatures, MulticlassLabels, CSVFile, RandomForest, MajorityVote
+ from shogun import RealFeatures, MulticlassLabels, CSVFile, RandomForest, MajorityVote
except ImportError:
print("Could not import Shogun modules")
return
diff --git a/examples/undocumented/python/preprocessor_dimensionreductionpreprocessor.py b/examples/undocumented/python/preprocessor_dimensionreductionpreprocessor.py
index 27ff5f23506..577def4dc9a 100644
--- a/examples/undocumented/python/preprocessor_dimensionreductionpreprocessor.py
+++ b/examples/undocumented/python/preprocessor_dimensionreductionpreprocessor.py
@@ -7,10 +7,10 @@
parameter_list = [[data, 20], [data, 30]]
def preprocessor_dimensionreductionpreprocessor (data, k):
- from modshogun import RealFeatures
- from modshogun import DimensionReductionPreprocessor
+ from shogun import RealFeatures
+ from shogun import DimensionReductionPreprocessor
try:
- from modshogun import LocallyLinearEmbedding
+ from shogun import LocallyLinearEmbedding
except ImportError:
print("LocallyLinearEmbedding not available")
exit(0)
diff --git a/examples/undocumented/python/preprocessor_fisherlda.py b/examples/undocumented/python/preprocessor_fisherlda.py
index af6fc69790c..5ff12da25dd 100644
--- a/examples/undocumented/python/preprocessor_fisherlda.py
+++ b/examples/undocumented/python/preprocessor_fisherlda.py
@@ -1,6 +1,6 @@
#!/usr/bin/env python
from tools.load import LoadMatrix
-from modshogun import *
+from shogun import *
lm=LoadMatrix()
@@ -10,9 +10,9 @@
parameter_list = [[data, labels, CANVAR_FLDA], [data, labels, CLASSIC_FLDA]]
def preprocessor_fisherlda (data, labels, method):
- from modshogun import RealFeatures, MulticlassLabels, CANVAR_FLDA
- from modshogun import FisherLda
- from modshogun import MulticlassLabels
+ from shogun import RealFeatures, MulticlassLabels, CANVAR_FLDA
+ from shogun import FisherLda
+ from shogun import MulticlassLabels
sg_features = RealFeatures(data)
sg_labels = MulticlassLabels(labels)
diff --git a/examples/undocumented/python/preprocessor_kernelpca.py b/examples/undocumented/python/preprocessor_kernelpca.py
index 7484a33298b..40905167fb2 100644
--- a/examples/undocumented/python/preprocessor_kernelpca.py
+++ b/examples/undocumented/python/preprocessor_kernelpca.py
@@ -7,9 +7,9 @@
parameter_list = [[data, 0.01, 1.0], [data, 0.05, 2.0]]
def preprocessor_kernelpca (data, threshold, width):
- from modshogun import RealFeatures
- from modshogun import KernelPCA
- from modshogun import GaussianKernel
+ from shogun import RealFeatures
+ from shogun import KernelPCA
+ from shogun import GaussianKernel
features = RealFeatures(data)
diff --git a/examples/undocumented/python/preprocessor_logplusone.py b/examples/undocumented/python/preprocessor_logplusone.py
index ebe72278f35..11115edac6b 100644
--- a/examples/undocumented/python/preprocessor_logplusone.py
+++ b/examples/undocumented/python/preprocessor_logplusone.py
@@ -9,9 +9,9 @@
def preprocessor_logplusone (fm_train_real=traindat,fm_test_real=testdat,width=1.4,size_cache=10):
- from modshogun import Chi2Kernel
- from modshogun import RealFeatures
- from modshogun import LogPlusOne
+ from shogun import Chi2Kernel
+ from shogun import RealFeatures
+ from shogun import LogPlusOne
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
diff --git a/examples/undocumented/python/preprocessor_normone.py b/examples/undocumented/python/preprocessor_normone.py
index 93483fb034c..24afe917cdd 100644
--- a/examples/undocumented/python/preprocessor_normone.py
+++ b/examples/undocumented/python/preprocessor_normone.py
@@ -9,9 +9,9 @@
def preprocessor_normone (fm_train_real=traindat,fm_test_real=testdat,width=1.4,size_cache=10):
- from modshogun import Chi2Kernel
- from modshogun import RealFeatures
- from modshogun import NormOne
+ from shogun import Chi2Kernel
+ from shogun import RealFeatures
+ from shogun import NormOne
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
diff --git a/examples/undocumented/python/preprocessor_pca.py b/examples/undocumented/python/preprocessor_pca.py
index e0f69258786..23149502390 100644
--- a/examples/undocumented/python/preprocessor_pca.py
+++ b/examples/undocumented/python/preprocessor_pca.py
@@ -7,8 +7,8 @@
parameter_list = [[data]]
def preprocessor_pca (data):
- from modshogun import RealFeatures
- from modshogun import PCA
+ from shogun import RealFeatures
+ from shogun import PCA
features = RealFeatures(data)
diff --git a/examples/undocumented/python/preprocessor_prunevarsubmean.py b/examples/undocumented/python/preprocessor_prunevarsubmean.py
index 1dc4e8e500a..91bcc0376a4 100644
--- a/examples/undocumented/python/preprocessor_prunevarsubmean.py
+++ b/examples/undocumented/python/preprocessor_prunevarsubmean.py
@@ -8,9 +8,9 @@
parameter_list = [[traindat,testdat,1.5,10],[traindat,testdat,1.5,10]]
def preprocessor_prunevarsubmean (fm_train_real=traindat,fm_test_real=testdat,width=1.4,size_cache=10):
- from modshogun import Chi2Kernel
- from modshogun import RealFeatures
- from modshogun import PruneVarSubMean
+ from shogun import Chi2Kernel
+ from shogun import RealFeatures
+ from shogun import PruneVarSubMean
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
diff --git a/examples/undocumented/python/preprocessor_randomfouriergausspreproc.py b/examples/undocumented/python/preprocessor_randomfouriergausspreproc.py
index 9d608381abc..f1e9a0dc656 100644
--- a/examples/undocumented/python/preprocessor_randomfouriergausspreproc.py
+++ b/examples/undocumented/python/preprocessor_randomfouriergausspreproc.py
@@ -7,13 +7,13 @@
parameter_list = [[traindat,testdat,1.5,10],[traindat,testdat,1.5,10]]
-from modshogun import Math_init_random;
+from shogun import Math_init_random;
Math_init_random(12345);
def preprocessor_randomfouriergausspreproc (fm_train_real=traindat,fm_test_real=testdat,width=1.4,size_cache=10):
- from modshogun import Chi2Kernel
- from modshogun import RealFeatures
- from modshogun import RandomFourierGaussPreproc
+ from shogun import Chi2Kernel
+ from shogun import RealFeatures
+ from shogun import RandomFourierGaussPreproc
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
diff --git a/examples/undocumented/python/preprocessor_sortulongstring.py b/examples/undocumented/python/preprocessor_sortulongstring.py
index 364845c401d..4b817b2996f 100644
--- a/examples/undocumented/python/preprocessor_sortulongstring.py
+++ b/examples/undocumented/python/preprocessor_sortulongstring.py
@@ -9,9 +9,9 @@
def preprocessor_sortulongstring (fm_train_dna=traindna,fm_test_dna=testdna,order=3,gap=0,reverse=False,use_sign=False):
- from modshogun import CommUlongStringKernel
- from modshogun import StringCharFeatures, StringUlongFeatures, DNA
- from modshogun import SortUlongString
+ from shogun import CommUlongStringKernel
+ from shogun import StringCharFeatures, StringUlongFeatures, DNA
+ from shogun import SortUlongString
charfeat=StringCharFeatures(DNA)
diff --git a/examples/undocumented/python/preprocessor_sortwordstring.py b/examples/undocumented/python/preprocessor_sortwordstring.py
index a99c175bd30..231d334298a 100644
--- a/examples/undocumented/python/preprocessor_sortwordstring.py
+++ b/examples/undocumented/python/preprocessor_sortwordstring.py
@@ -9,9 +9,9 @@
def preprocessor_sortwordstring (fm_train_dna=traindna,fm_test_dna=testdna,order=3,gap=0,reverse=False,use_sign=False):
- from modshogun import CommWordStringKernel
- from modshogun import StringCharFeatures, StringWordFeatures, DNA
- from modshogun import SortWordString
+ from shogun import CommWordStringKernel
+ from shogun import StringCharFeatures, StringWordFeatures, DNA
+ from shogun import SortWordString
charfeat=StringCharFeatures(fm_train_dna, DNA)
feats_train=StringWordFeatures(charfeat.get_alphabet())
diff --git a/examples/undocumented/python/regression_cartree.py b/examples/undocumented/python/regression_cartree.py
index b478b9db637..ad89191af11 100644
--- a/examples/undocumented/python/regression_cartree.py
+++ b/examples/undocumented/python/regression_cartree.py
@@ -8,7 +8,7 @@
def regression_cartree(num_train=500,num_test=50,x_range=15,noise_var=0.2,ft=feattypes):
try:
- from modshogun import RealFeatures, RegressionLabels, CSVFile, CARTree, PT_REGRESSION
+ from shogun import RealFeatures, RegressionLabels, CSVFile, CARTree, PT_REGRESSION
from numpy import random
except ImportError:
print("Could not import Shogun and/or numpy modules")
diff --git a/examples/undocumented/python/regression_chaidtree.py b/examples/undocumented/python/regression_chaidtree.py
index 02dd22f906b..60b7f883ca6 100644
--- a/examples/undocumented/python/regression_chaidtree.py
+++ b/examples/undocumented/python/regression_chaidtree.py
@@ -8,7 +8,7 @@
def regression_chaidtree(num_train=500,num_test=50,x_range=15,noise_var=0.2,ft=feattypes):
try:
- from modshogun import RealFeatures, RegressionLabels, CSVFile, CHAIDTree, PT_REGRESSION
+ from shogun import RealFeatures, RegressionLabels, CSVFile, CHAIDTree, PT_REGRESSION
from numpy import random
except ImportError:
print("Could not import Shogun and/or numpy modules")
diff --git a/examples/undocumented/python/regression_randomforest.py b/examples/undocumented/python/regression_randomforest.py
index 7dce6cd4db7..acfc0d2073e 100644
--- a/examples/undocumented/python/regression_randomforest.py
+++ b/examples/undocumented/python/regression_randomforest.py
@@ -12,7 +12,7 @@
def regression_randomforest(num_train=500,num_test=50,x_range=15,noise_var=0.2,ft=feattypes):
try:
- from modshogun import RealFeatures, RegressionLabels, CSVFile, RandomForest, MeanRule, PT_REGRESSION
+ from shogun import RealFeatures, RegressionLabels, CSVFile, RandomForest, MeanRule, PT_REGRESSION
except ImportError:
print("Could not import Shogun modules")
return
diff --git a/examples/undocumented/python/regression_svrlight.py b/examples/undocumented/python/regression_svrlight.py
index 3be9f624af9..e9126fc8c7d 100644
--- a/examples/undocumented/python/regression_svrlight.py
+++ b/examples/undocumented/python/regression_svrlight.py
@@ -17,10 +17,10 @@ def regression_svrlight (fm_train=traindat,fm_test=testdat,label_train=label_tra
width=1.2,C=1,epsilon=1e-5,tube_epsilon=1e-2,num_threads=3):
- from modshogun import RegressionLabels, RealFeatures
- from modshogun import GaussianKernel
+ from shogun import RegressionLabels, RealFeatures
+ from shogun import GaussianKernel
try:
- from modshogun import SVRLight
+ from shogun import SVRLight
except ImportError:
print('No support for SVRLight available.')
return
diff --git a/examples/undocumented/python/serialization_complex_example.py b/examples/undocumented/python/serialization_complex_example.py
index 56ec37e6f68..33966a40452 100644
--- a/examples/undocumented/python/serialization_complex_example.py
+++ b/examples/undocumented/python/serialization_complex_example.py
@@ -9,12 +9,12 @@ def serialization_complex_example (num=5, dist=1, dim=10, C=2.0, width=10):
import os
from numpy import concatenate, zeros, ones
from numpy.random import randn, seed
- from modshogun import RealFeatures, MulticlassLabels
- from modshogun import GMNPSVM
- from modshogun import GaussianKernel
- from modshogun import SerializableHdf5File,SerializableAsciiFile, \
+ from shogun import RealFeatures, MulticlassLabels
+ from shogun import GMNPSVM
+ from shogun import GaussianKernel
+ from shogun import SerializableHdf5File,SerializableAsciiFile, \
SerializableJsonFile,SerializableXmlFile,MSG_DEBUG
- from modshogun import NormOne, LogPlusOne
+ from shogun import NormOne, LogPlusOne
from tempfile import NamedTemporaryFile
seed(17)
diff --git a/examples/undocumented/python/serialization_matrix.py b/examples/undocumented/python/serialization_matrix.py
index 3e946b8feda..6d6be7ea5b3 100644
--- a/examples/undocumented/python/serialization_matrix.py
+++ b/examples/undocumented/python/serialization_matrix.py
@@ -1,5 +1,5 @@
#!/usr/bin/env python
-from modshogun import *
+from shogun import *
from numpy import array
parameter_list=[[[[1.0,2,3],[4,5,6]]]]
diff --git a/examples/undocumented/python/serialization_string_kernels.py b/examples/undocumented/python/serialization_string_kernels.py
index 3116006290c..9971b8532b8 100644
--- a/examples/undocumented/python/serialization_string_kernels.py
+++ b/examples/undocumented/python/serialization_string_kernels.py
@@ -1,17 +1,17 @@
#!/usr/bin/env python
-from modshogun import WeightedDegreeStringKernel, LinearKernel, PolyKernel, GaussianKernel, CTaxonomy
-from modshogun import CombinedKernel, WeightedDegreeRBFKernel
-from modshogun import StringCharFeatures, RealFeatures, CombinedFeatures, StringWordFeatures, SortWordString
-from modshogun import DNA, PROTEIN, Labels
-from modshogun import WeightedDegreeStringKernel, CombinedKernel, WeightedCommWordStringKernel, WeightedDegreePositionStringKernel
-from modshogun import StringCharFeatures, DNA, StringWordFeatures, CombinedFeatures
-
-from modshogun import MSG_DEBUG
-from modshogun import RealFeatures, BinaryLabels, DNA, Alphabet
-from modshogun import WeightedDegreeStringKernel, GaussianKernel
+from shogun import WeightedDegreeStringKernel, LinearKernel, PolyKernel, GaussianKernel, CTaxonomy
+from shogun import CombinedKernel, WeightedDegreeRBFKernel
+from shogun import StringCharFeatures, RealFeatures, CombinedFeatures, StringWordFeatures, SortWordString
+from shogun import DNA, PROTEIN, Labels
+from shogun import WeightedDegreeStringKernel, CombinedKernel, WeightedCommWordStringKernel, WeightedDegreePositionStringKernel
+from shogun import StringCharFeatures, DNA, StringWordFeatures, CombinedFeatures
+
+from shogun import MSG_DEBUG
+from shogun import RealFeatures, BinaryLabels, DNA, Alphabet
+from shogun import WeightedDegreeStringKernel, GaussianKernel
try:
- from modshogun import SVMLight
+ from shogun import SVMLight
except ImportError:
print("SVMLight is not available")
exit(0)
diff --git a/examples/undocumented/python/serialization_svmlight.py b/examples/undocumented/python/serialization_svmlight.py
index 0db71cd698c..f130c61a071 100644
--- a/examples/undocumented/python/serialization_svmlight.py
+++ b/examples/undocumented/python/serialization_svmlight.py
@@ -2,11 +2,11 @@
parameter_list=[[10, 1, 2.1, 2.0]]
def serialization_svmlight (num, dist, width, C):
- from modshogun import MSG_DEBUG
- from modshogun import RealFeatures, BinaryLabels, DNA, Alphabet
- from modshogun import WeightedDegreeStringKernel, GaussianKernel
+ from shogun import MSG_DEBUG
+ from shogun import RealFeatures, BinaryLabels, DNA, Alphabet
+ from shogun import WeightedDegreeStringKernel, GaussianKernel
try:
- from modshogun import SVMLight
+ from shogun import SVMLight
except ImportError:
print("SVMLight not available")
exit(0)
diff --git a/examples/undocumented/python/so_multiclass.py b/examples/undocumented/python/so_multiclass.py
index 4923ea553c8..b6ba20bd0a9 100644
--- a/examples/undocumented/python/so_multiclass.py
+++ b/examples/undocumented/python/so_multiclass.py
@@ -26,8 +26,8 @@ def gen_data(num_classes,num_samples,dim):
def so_multiclass (fm_train_real=traindat,label_train_multiclass=label_traindat):
try:
- from modshogun import RealFeatures
- from modshogun import MulticlassModel, MulticlassSOLabels, PrimalMosekSOSVM, RealNumber
+ from shogun import RealFeatures
+ from shogun import MulticlassModel, MulticlassSOLabels, PrimalMosekSOSVM, RealNumber
except ImportError:
print("Mosek not available")
return
diff --git a/examples/undocumented/python/stochasticgbmachine.py b/examples/undocumented/python/stochasticgbmachine.py
index 5fbd12b74ec..dae5d67ffce 100644
--- a/examples/undocumented/python/stochasticgbmachine.py
+++ b/examples/undocumented/python/stochasticgbmachine.py
@@ -11,7 +11,7 @@
def stochasticgbmachine(train=traindat,train_labels=label_traindat,ft=feat_types):
try:
- from modshogun import RealFeatures, RegressionLabels, CSVFile, CARTree, StochasticGBMachine, SquaredLoss
+ from shogun import RealFeatures, RegressionLabels, CSVFile, CARTree, StochasticGBMachine, SquaredLoss
except ImportError:
print("Could not import Shogun modules")
return
diff --git a/examples/undocumented/python/streaming_vw.py b/examples/undocumented/python/streaming_vw.py
index e93ac0ed3d6..8e21dbdbccb 100644
--- a/examples/undocumented/python/streaming_vw.py
+++ b/examples/undocumented/python/streaming_vw.py
@@ -1,8 +1,8 @@
#!/usr/bin/env python
-from modshogun import StreamingVwFile
-from modshogun import T_SVMLIGHT
-from modshogun import StreamingVwFeatures
-from modshogun import VowpalWabbit
+from shogun import StreamingVwFile
+from shogun import T_SVMLIGHT
+from shogun import StreamingVwFeatures
+from shogun import VowpalWabbit
parameter_list=[[None]]
diff --git a/examples/undocumented/python/streaming_vw_createcache.py b/examples/undocumented/python/streaming_vw_createcache.py
index ae28748603a..2404f0350b7 100644
--- a/examples/undocumented/python/streaming_vw_createcache.py
+++ b/examples/undocumented/python/streaming_vw_createcache.py
@@ -1,9 +1,9 @@
#!/usr/bin/env python
-from modshogun import StreamingVwFile
-from modshogun import StreamingVwCacheFile
-from modshogun import T_SVMLIGHT
-from modshogun import StreamingVwFeatures
-from modshogun import VowpalWabbit
+from shogun import StreamingVwFile
+from shogun import StreamingVwCacheFile
+from shogun import T_SVMLIGHT
+from shogun import StreamingVwFeatures
+from shogun import VowpalWabbit
parameter_list=[['../data/fm_train_sparsereal.dat']]
diff --git a/examples/undocumented/python/structure_discrete_hmsvm_bmrm.py b/examples/undocumented/python/structure_discrete_hmsvm_bmrm.py
index 9fee3bd2826..59cf961f4b8 100644
--- a/examples/undocumented/python/structure_discrete_hmsvm_bmrm.py
+++ b/examples/undocumented/python/structure_discrete_hmsvm_bmrm.py
@@ -9,10 +9,10 @@
parameter_list=[[data_dict]]
def structure_discrete_hmsvm_bmrm (m_data_dict=data_dict):
- from modshogun import RealMatrixFeatures, SequenceLabels, HMSVMModel, Sequence, TwoStateModel
- from modshogun import StructuredAccuracy, SMT_TWO_STATE
+ from shogun import RealMatrixFeatures, SequenceLabels, HMSVMModel, Sequence, TwoStateModel
+ from shogun import StructuredAccuracy, SMT_TWO_STATE
try:
- from modshogun import DualLibQPBMSOSVM
+ from shogun import DualLibQPBMSOSVM
except ImportError:
print("DualLibQPBMSOSVM not available")
exit(0)
diff --git a/examples/undocumented/python/structure_discrete_hmsvm_mosek.py b/examples/undocumented/python/structure_discrete_hmsvm_mosek.py
index 14cecf439bf..47ac6e4b161 100644
--- a/examples/undocumented/python/structure_discrete_hmsvm_mosek.py
+++ b/examples/undocumented/python/structure_discrete_hmsvm_mosek.py
@@ -9,11 +9,11 @@
parameter_list=[[data_dict]]
def structure_discrete_hmsvm_mosek (m_data_dict=data_dict):
- from modshogun import RealMatrixFeatures, SequenceLabels, HMSVMModel, Sequence, TwoStateModel
- from modshogun import StructuredAccuracy, SMT_TWO_STATE
+ from shogun import RealMatrixFeatures, SequenceLabels, HMSVMModel, Sequence, TwoStateModel
+ from shogun import StructuredAccuracy, SMT_TWO_STATE
try:
- from modshogun import PrimalMosekSOSVM
+ from shogun import PrimalMosekSOSVM
except ImportError:
print("Mosek not available")
return
diff --git a/examples/undocumented/python/structure_dynprog.py b/examples/undocumented/python/structure_dynprog.py
index b0cb2f13036..e97ddbf5954 100644
--- a/examples/undocumented/python/structure_dynprog.py
+++ b/examples/undocumented/python/structure_dynprog.py
@@ -4,7 +4,7 @@
parameter_list=[['../data/DynProg_example_py.pickle.gz']]
-from modshogun import *
+from shogun import *
import numpy
from numpy import array,Inf,float64,matrix,frompyfunc,zeros
diff --git a/examples/undocumented/python/structure_factor_graph_model.py b/examples/undocumented/python/structure_factor_graph_model.py
index 2c37f7582cc..face601195c 100644
--- a/examples/undocumented/python/structure_factor_graph_model.py
+++ b/examples/undocumented/python/structure_factor_graph_model.py
@@ -1,7 +1,7 @@
#!/usr/bin/env python
import numpy as np
-from modshogun import TableFactorType
+from shogun import TableFactorType
# create the factor type with GT parameters
tid = 0
@@ -20,10 +20,10 @@
fac_type_b = TableFactorType(tid_b, cards_b, w_gt_b)
def gen_data(ftype, num_samples, show_data = False):
- from modshogun import Math
- from modshogun import FactorType, Factor, TableFactorType, FactorGraph
- from modshogun import FactorGraphObservation, FactorGraphLabels, FactorGraphFeatures
- from modshogun import MAPInference, TREE_MAX_PROD
+ from shogun import Math
+ from shogun import FactorType, Factor, TableFactorType, FactorGraph
+ from shogun import FactorGraphObservation, FactorGraphLabels, FactorGraphFeatures
+ from shogun import MAPInference, TREE_MAX_PROD
Math.init_random(17)
@@ -95,11 +95,11 @@ def gen_data(ftype, num_samples, show_data = False):
parameter_list = [[samples,labels,w_all,ftype_all]]
def structure_factor_graph_model(tr_samples = samples, tr_labels = labels, w = w_all, ftype = ftype_all):
- from modshogun import SOSVMHelper, LabelsFactory
- from modshogun import FactorGraphModel, MAPInference, TREE_MAX_PROD
- from modshogun import StochasticSOSVM, FWSOSVM
+ from shogun import SOSVMHelper, LabelsFactory
+ from shogun import FactorGraphModel, MAPInference, TREE_MAX_PROD
+ from shogun import StochasticSOSVM, FWSOSVM
try:
- from modshogun import DualLibQPBMSOSVM
+ from shogun import DualLibQPBMSOSVM
except ImportError:
print("DualLibQPBMSOSVM not available")
exit(0)
diff --git a/examples/undocumented/python/structure_graphcuts.py b/examples/undocumented/python/structure_graphcuts.py
index 3615f90c6e8..59174ea05a1 100644
--- a/examples/undocumented/python/structure_graphcuts.py
+++ b/examples/undocumented/python/structure_graphcuts.py
@@ -3,11 +3,11 @@
import numpy as np
import itertools
-from modshogun import Factor, TableFactorType, FactorGraph
-from modshogun import FactorGraphObservation, FactorGraphLabels, FactorGraphFeatures
-from modshogun import FactorGraphModel, GRAPH_CUT
-from modshogun import GraphCut
-from modshogun import StochasticSOSVM
+from shogun import Factor, TableFactorType, FactorGraph
+from shogun import FactorGraphObservation, FactorGraphLabels, FactorGraphFeatures
+from shogun import FactorGraphModel, GRAPH_CUT
+from shogun import GraphCut
+from shogun import StochasticSOSVM
def generate_data(num_train_samples, len_label, len_feat):
""" Generate synthetic dataset
diff --git a/examples/undocumented/python/structure_hierarchical_multilabel_classification.py b/examples/undocumented/python/structure_hierarchical_multilabel_classification.py
index 05392a2fab1..05c3e70e3ee 100644
--- a/examples/undocumented/python/structure_hierarchical_multilabel_classification.py
+++ b/examples/undocumented/python/structure_hierarchical_multilabel_classification.py
@@ -7,10 +7,10 @@
http://kt.ijs.si/DragiKocev/PhD/resources/doku.php?id=hmc_classification#imageclef07d
"""
-from modshogun import MultilabelSOLabels, HierarchicalMultilabelModel
-from modshogun import RealFeatures
-from modshogun import StochasticSOSVM
-from modshogun import StructuredAccuracy, LabelsFactory
+from shogun import MultilabelSOLabels, HierarchicalMultilabelModel
+from shogun import RealFeatures
+from shogun import StochasticSOSVM
+from shogun import StructuredAccuracy, LabelsFactory
import numpy as np
import time
diff --git a/examples/undocumented/python/structure_multiclass_bmrm.py b/examples/undocumented/python/structure_multiclass_bmrm.py
index 1a293763b79..d7dbd034a47 100644
--- a/examples/undocumented/python/structure_multiclass_bmrm.py
+++ b/examples/undocumented/python/structure_multiclass_bmrm.py
@@ -25,15 +25,15 @@ def gen_data(num_classes,num_samples,dim):
parameter_list = [[traindat,label_traindat]]
def structure_multiclass_bmrm(fm_train_real=traindat,label_train_multiclass=label_traindat):
- from modshogun import MulticlassSOLabels, LabelsFactory
- from modshogun import RealFeatures
- from modshogun import SOSVMHelper
+ from shogun import MulticlassSOLabels, LabelsFactory
+ from shogun import RealFeatures
+ from shogun import SOSVMHelper
try:
- from modshogun import BMRM, PPBMRM, P3BMRM, DualLibQPBMSOSVM
+ from shogun import BMRM, PPBMRM, P3BMRM, DualLibQPBMSOSVM
except ImportError:
print("At least one of BMRM, PPBMRM, P3BMRM, DualLibQPBMSOSVM not available")
exit(0)
- from modshogun import MulticlassModel, RealNumber
+ from shogun import MulticlassModel, RealNumber
labels = MulticlassSOLabels(label_train_multiclass)
features = RealFeatures(fm_train_real.T)
diff --git a/examples/undocumented/python/structure_plif_hmsvm_bmrm.py b/examples/undocumented/python/structure_plif_hmsvm_bmrm.py
index 91822d366a6..bd7542607a0 100644
--- a/examples/undocumented/python/structure_plif_hmsvm_bmrm.py
+++ b/examples/undocumented/python/structure_plif_hmsvm_bmrm.py
@@ -3,9 +3,9 @@
parameter_list=[[50, 125, 10, 2]]
def structure_plif_hmsvm_bmrm (num_examples, example_length, num_features, num_noise_features):
- from modshogun import RealMatrixFeatures, TwoStateModel, StructuredAccuracy
+ from shogun import RealMatrixFeatures, TwoStateModel, StructuredAccuracy
try:
- from modshogun import DualLibQPBMSOSVM
+ from shogun import DualLibQPBMSOSVM
except ImportError:
print("DualLibQPBMSOSVM not available")
exit(0)
diff --git a/examples/undocumented/python/structure_plif_hmsvm_mosek.py b/examples/undocumented/python/structure_plif_hmsvm_mosek.py
index 2ef2e0dc40c..8e918f08512 100644
--- a/examples/undocumented/python/structure_plif_hmsvm_mosek.py
+++ b/examples/undocumented/python/structure_plif_hmsvm_mosek.py
@@ -3,10 +3,10 @@
parameter_list=[[100, 250, 10, 2]]
def structure_plif_hmsvm_mosek (num_examples, example_length, num_features, num_noise_features):
- from modshogun import RealMatrixFeatures, TwoStateModel, StructuredAccuracy
+ from shogun import RealMatrixFeatures, TwoStateModel, StructuredAccuracy
try:
- from modshogun import PrimalMosekSOSVM
+ from shogun import PrimalMosekSOSVM
except ImportError:
print("Mosek not available")
return
diff --git a/examples/undocumented/python/tests_check_commwordkernel_memleak.py b/examples/undocumented/python/tests_check_commwordkernel_memleak.py
index 1ecced84692..5da5b8b07df 100644
--- a/examples/undocumented/python/tests_check_commwordkernel_memleak.py
+++ b/examples/undocumented/python/tests_check_commwordkernel_memleak.py
@@ -3,9 +3,9 @@
def tests_check_commwordkernel_memleak (num, order, gap, reverse):
import gc
- from modshogun import Alphabet,StringCharFeatures,StringWordFeatures,DNA
- from modshogun import SortWordString, MSG_DEBUG
- from modshogun import CommWordStringKernel, IdentityKernelNormalizer
+ from shogun import Alphabet,StringCharFeatures,StringWordFeatures,DNA
+ from shogun import SortWordString, MSG_DEBUG
+ from shogun import CommWordStringKernel, IdentityKernelNormalizer
from numpy import mat
POS=[num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
diff --git a/examples/undocumented/python/transfer_multitask_clustered_logistic_regression.py b/examples/undocumented/python/transfer_multitask_clustered_logistic_regression.py
index 87ac204ab44..e3dc8b2a64b 100644
--- a/examples/undocumented/python/transfer_multitask_clustered_logistic_regression.py
+++ b/examples/undocumented/python/transfer_multitask_clustered_logistic_regression.py
@@ -11,9 +11,9 @@
parameter_list = [[traindat,testdat,label_traindat]]
def transfer_multitask_clustered_logistic_regression (fm_train=traindat,fm_test=testdat,label_train=label_traindat):
- from modshogun import BinaryLabels, RealFeatures, Task, TaskGroup, MSG_DEBUG
+ from shogun import BinaryLabels, RealFeatures, Task, TaskGroup, MSG_DEBUG
try:
- from modshogun import MultitaskClusteredLogisticRegression
+ from shogun import MultitaskClusteredLogisticRegression
except ImportError:
print("MultitaskClusteredLogisticRegression not available")
exit()
diff --git a/examples/undocumented/python/transfer_multitask_l12_logistic_regression.py b/examples/undocumented/python/transfer_multitask_l12_logistic_regression.py
index 56c7c84e57b..22c47c0c2f8 100644
--- a/examples/undocumented/python/transfer_multitask_l12_logistic_regression.py
+++ b/examples/undocumented/python/transfer_multitask_l12_logistic_regression.py
@@ -11,9 +11,9 @@
parameter_list = [[traindat,testdat,label_traindat]]
def transfer_multitask_l12_logistic_regression (fm_train=traindat,fm_test=testdat,label_train=label_traindat):
- from modshogun import BinaryLabels, RealFeatures, Task, TaskGroup
+ from shogun import BinaryLabels, RealFeatures, Task, TaskGroup
try:
- from modshogun import MultitaskL12LogisticRegression
+ from shogun import MultitaskL12LogisticRegression
except ImportError:
print("MultitaskL12LogisticRegression not available")
exit(0)
diff --git a/examples/undocumented/python/transfer_multitask_leastsquares_regression.py b/examples/undocumented/python/transfer_multitask_leastsquares_regression.py
index 8ec07e61d5c..1b1357d96a5 100644
--- a/examples/undocumented/python/transfer_multitask_leastsquares_regression.py
+++ b/examples/undocumented/python/transfer_multitask_leastsquares_regression.py
@@ -11,9 +11,9 @@
parameter_list = [[traindat,testdat,label_traindat]]
def transfer_multitask_leastsquares_regression (fm_train=traindat,fm_test=testdat,label_train=label_traindat):
- from modshogun import RegressionLabels, RealFeatures, Task, TaskGroup
+ from shogun import RegressionLabels, RealFeatures, Task, TaskGroup
try:
- from modshogun import MultitaskLeastSquaresRegression
+ from shogun import MultitaskLeastSquaresRegression
except ImportError:
print("MultitaskLeastSquaresRegression not available")
exit(0)
diff --git a/examples/undocumented/python/transfer_multitask_logistic_regression.py b/examples/undocumented/python/transfer_multitask_logistic_regression.py
index d475cb7757f..5c97b6f6a2d 100644
--- a/examples/undocumented/python/transfer_multitask_logistic_regression.py
+++ b/examples/undocumented/python/transfer_multitask_logistic_regression.py
@@ -11,9 +11,9 @@
parameter_list = [[traindat,testdat,label_traindat]]
def transfer_multitask_logistic_regression (fm_train=traindat,fm_test=testdat,label_train=label_traindat):
- from modshogun import BinaryLabels, RealFeatures, Task, TaskGroup
+ from shogun import BinaryLabels, RealFeatures, Task, TaskGroup
try:
- from modshogun import MultitaskLogisticRegression
+ from shogun import MultitaskLogisticRegression
except ImportError:
print("MultitaskLogisticRegression not available")
exit()
diff --git a/examples/undocumented/python/transfer_multitask_trace_logistic_regression.py b/examples/undocumented/python/transfer_multitask_trace_logistic_regression.py
index a128a969cbf..c72912dd63a 100644
--- a/examples/undocumented/python/transfer_multitask_trace_logistic_regression.py
+++ b/examples/undocumented/python/transfer_multitask_trace_logistic_regression.py
@@ -11,9 +11,9 @@
parameter_list = [[traindat,testdat,label_traindat]]
def transfer_multitask_trace_logistic_regression (fm_train=traindat,fm_test=testdat,label_train=label_traindat):
- from modshogun import BinaryLabels, RealFeatures, Task, TaskGroup
+ from shogun import BinaryLabels, RealFeatures, Task, TaskGroup
try:
- from modshogun import MultitaskTraceLogisticRegression
+ from shogun import MultitaskTraceLogisticRegression
except ImportError:
print("MultitaskTraceLogisticRegression not available")
exit(0)
diff --git a/examples/undocumented/python/variational_classifier.py b/examples/undocumented/python/variational_classifier.py
index c7a6dc327f8..7461d660b24 100644
--- a/examples/undocumented/python/variational_classifier.py
+++ b/examples/undocumented/python/variational_classifier.py
@@ -37,12 +37,12 @@
label_binary_traindat = '%s/label_train_twoclass.dat'%path
try:
- from modshogun import GaussianProcessClassification
+ from shogun import GaussianProcessClassification
except ImportError:
print("GaussianProcessClassification is not available")
exit(0)
-from modshogun import *
+from shogun import *
parameter_list=[
[KLCholeskyInferenceMethod,traindat,testdat,label_binary_traindat,0,0,1e-5,1e-2,0],
[KLCovarianceInferenceMethod,traindat,testdat,label_binary_traindat,0,0,1e-5,1e-2,0],
diff --git a/setup.py b/setup.py
index 3739e4df857..389f78e3f64 100644
--- a/setup.py
+++ b/setup.py
@@ -243,12 +243,12 @@ def shogun_package_directories():
def shogun_data_files():
data_files = list()
libshogun_files = glob.glob(os.path.join(shogun_generated_install, 'lib/libshogun*'))
- modshogun_so_destination = os.path.join('lib', python_package_path(shogun_python_packages_location))
- modshogun_so_file = glob.glob(os.path.join(shogun_python_packages_location, '_modshogun.so'))[0]
+ shogun_so_destination = os.path.join('lib', python_package_path(shogun_python_packages_location))
+ shogun_so_file = glob.glob(os.path.join(shogun_python_packages_location, '_shogun.so'))[0]
# appending data files
data_files.append(('lib', libshogun_files))
- data_files.append((modshogun_so_destination, [modshogun_so_file]))
+ data_files.append((shogun_so_destination, [shogun_so_file]))
if show_debug_information:
print('Shogun Python package data files:')
@@ -335,7 +335,7 @@ def run(self):
# Shogun package content
packages = shogun_packages(),
package_dir = shogun_package_directories(),
- py_modules =['modshogun'],
+ py_modules =['shogun'],
data_files = shogun_data_files(),
# Shogun dependencies
diff --git a/src/.r-install.sh b/src/.r-install.sh
index 2c3f97912bf..887deb61fcd 100755
--- a/src/.r-install.sh
+++ b/src/.r-install.sh
@@ -129,7 +129,7 @@ else
echo "Installing modular shogun interface for R"
cat >"$1/$2/NAMESPACE" <"$1/$2/R/$2" <