Update imports in applications

applications/arts/signal_sensor.py

@@ -12,11 +12,11 @@
 
 from util import *
 
-from shogun.Features import StringCharFeatures, StringWordFeatures, CombinedFeatures, DNA
-from shogun.Kernel import CombinedKernel, WeightedDegreePositionStringKernel
-from shogun.Kernel import K_COMMWORDSTRING, CommWordStringKernel, IdentityKernelNormalizer
-from shogun.Preprocessor import SortWordString
-from shogun.Classifier import KernelMachine
+from shogun import StringCharFeatures, StringWordFeatures, CombinedFeatures, DNA
+from shogun import CombinedKernel, WeightedDegreePositionStringKernel
+from shogun import K_COMMWORDSTRING, CommWordStringKernel, IdentityKernelNormalizer
+from shogun import SortWordString
+from shogun import KernelMachine
 
 
 class Sensor(object):

applications/asp/asp

@@ -24,9 +24,9 @@ try:
 	import genomic
 	import model
 	import seqdict
-	import shogun.Kernel
+	import shogun
 
-	d=shogun.Kernel.WeightedDegreeStringKernel(1)
+	d=shogun.WeightedDegreeStringKernel(1)
 	if (d.version.get_version_revision() < 2997):
 		print
 		print "ERROR: SHOGUN VERSION 0.6.2 or later required"

applications/asp/signal_detectors.py

@@ -13,10 +13,10 @@
 import numpy
 import seqdict
 
-from shogun.Classifier import LibSVM
-from shogun.Features import StringCharFeatures,DNA
-from shogun.Kernel import WeightedDegreeStringKernel
-from shogun.Library import DynamicIntArray
+from shogun import LibSVM
+from shogun import StringCharFeatures,DNA
+from shogun import WeightedDegreeStringKernel
+from shogun import DynamicIntArray
 
 class svm_splice_model(object):
 	def __init__(self, order, traindat, alphas, b, (window_left,offset,window_right), consensus):

applications/easysvm/esvm/experiment.py

@@ -28,28 +28,28 @@
 from poim import compute_poims
 
 import shogun
-from shogun.Kernel import GaussianKernel, WeightedDegreePositionStringKernel
-from shogun.Kernel import WeightedDegreeStringKernel
-from shogun.Kernel import LinearKernel, PolyKernel, LocalAlignmentStringKernel
-from shogun.Kernel import LocalityImprovedStringKernel
-from shogun.Kernel import CommWordStringKernel, WeightedCommWordStringKernel, CommUlongStringKernel
-from shogun.Kernel import CombinedKernel
-from shogun.Kernel import SLOWBUTMEMEFFICIENT
-from shogun.Kernel import AvgDiagKernelNormalizer
-from shogun.Features import RealFeatures, Labels, StringCharFeatures, DNA, StringWordFeatures, StringUlongFeatures, PROTEIN
-from shogun.Features import CombinedFeatures
-from shogun.Classifier import LibSVM,GPBTSVM
+from shogun import GaussianKernel, WeightedDegreePositionStringKernel
+from shogun import WeightedDegreeStringKernel
+from shogun import LinearKernel, PolyKernel, LocalAlignmentStringKernel
+from shogun import LocalityImprovedStringKernel
+from shogun import CommWordStringKernel, WeightedCommWordStringKernel, CommUlongStringKernel
+from shogun import CombinedKernel
+from shogun import SLOWBUTMEMEFFICIENT
+from shogun import AvgDiagKernelNormalizer
+from shogun import RealFeatures, Labels, StringCharFeatures, DNA, StringWordFeatures, StringUlongFeatures, PROTEIN
+from shogun import CombinedFeatures
+from shogun import LibSVM,GPBTSVM
 
 DefaultSVM = LibSVM
 try:
-    from shogun.Classifier import SVMLight
+    from shogun import SVMLight
     LinAddSVM = SVMLight
     LinearSVM = SVMLight
 except:
     LinAddSVM = GPBTSVM
     LinearSVM = LibSVM
 
-from shogun.Preprocessor import SortWordString, SortUlongString
+from shogun import SortWordString, SortUlongString
 
 from utils import calcprc, calcroc, accuracy
 from utils import getPartitionedSet, getCurrentSplit

applications/easysvm/esvm/plots.py

@@ -27,8 +27,8 @@
 import warnings
 import shutil
 
-from shogun.Features import Labels
-from shogun.Evaluation import *
+from shogun import Labels
+from shogun import *
 
 def plotroc(output, LTE, draw_random=False, figure_fname="", roc_label='ROC'):
     """Plot the receiver operating characteristic curve"""

applications/easysvm/esvm/utils.py

@@ -23,8 +23,8 @@
 import warnings
 import shutil
 
-from shogun.Features import Labels
-from shogun.Evaluation import *
+from shogun import Labels
+from shogun import *
 
 ################################################################################
 # evaluation functions

applications/easysvm/tutpaper/svm_params.py

@@ -14,9 +14,9 @@
 
 import numpy
 import shogun
-from shogun.Kernel import GaussianKernel, LinearKernel, PolyKernel
-from shogun.Features import RealFeatures, BinaryLabels
-from shogun.Classifier import LibSVM
+from shogun import GaussianKernel, LinearKernel, PolyKernel
+from shogun import RealFeatures, BinaryLabels
+from shogun import LibSVM
 
 from numpy import arange
 import matplotlib

applications/msplicer/msplicer

@@ -22,9 +22,9 @@ try:
 	import genomic
 	import model
 	import seqdict
-	import shogun.Structure
+	import shogun
 
-	d=shogun.Structure.DynProg()
+	d=shogun.DynProg()
 	if (d.version.get_version_revision() < 2997):
 		print
 		print "ERROR: SHOGUN VERSION 0.6.2 or later required"
@@ -152,7 +152,7 @@ class msplicer:
 
 
 	def initialize_dynprog(self, seq):
-		dyn=shogun.Structure.DynProg()
+		dyn=shogun.DynProg()
 
 		self.content.initialize_content(dyn)
 
@@ -266,7 +266,7 @@ class msplicer:
 				dyn.best_path_set_my_state_seq(my_states)
 				dyn.best_path_set_my_pos_seq(my_pos)
 
-				dyn.io.set_loglevel(shogun.Structure.M_DEBUG)
+				dyn.io.set_loglevel(shogun.M_DEBUG)
 				dyn.best_path_deriv_call()
 
 def print_version():
@@ -348,7 +348,7 @@ WS160, WS160gc, orfWS160gc
 
 
 if __name__ == '__main__':
-	dyn=shogun.Structure.DynProg()
+	dyn=shogun.DynProg()
 	(startstop, fafname, modelfname, outfile ) = parse_options()
 	p=msplicer()
 	p.load_model(modelfname);

applications/msplicer/plif.py

@@ -10,9 +10,9 @@
 #
 
 from numpy import array
-from shogun.Structure import Plif
-from shogun.Structure import PlifArray
-from shogun.Library import DynamicPlifArray
+from shogun import Plif
+from shogun import PlifArray
+from shogun import DynamicPlifArray
 
 class plif:
 	def __init__(self, model):

applications/tapkee/swissroll_embedding.py

@@ -5,12 +5,12 @@
 N = X.shape[1]
 converters = []
 
-from shogun.Converter import LocallyLinearEmbedding
+from shogun import LocallyLinearEmbedding
 lle = LocallyLinearEmbedding()
 lle.set_k(9)
 converters.append((lle, "LLE with k=%d" % lle.get_k()))
 
-from shogun.Converter import MultidimensionalScaling
+from shogun import MultidimensionalScaling
 mds = MultidimensionalScaling()
 converters.append((mds, "Classic MDS"))
 
@@ -19,29 +19,29 @@
 lmds.set_landmark_number(20)
 converters.append((lmds,"Landmark MDS with %d landmarks" % lmds.get_landmark_number()))
 
-from shogun.Converter import Isomap
+from shogun import Isomap
 cisomap = Isomap()
 cisomap.set_k(9)
 converters.append((cisomap,"Isomap with k=%d" % cisomap.get_k()))
 
-from shogun.Converter import DiffusionMaps
-from shogun.Kernel import GaussianKernel
+from shogun import DiffusionMaps
+from shogun import GaussianKernel
 dm = DiffusionMaps()
 dm.set_t(2)
 dm.set_width(1000.0)
 converters.append((dm,"Diffusion Maps with t=%d, sigma=%.1f" % (dm.get_t(),dm.get_width())))
 
-from shogun.Converter import HessianLocallyLinearEmbedding
+from shogun import HessianLocallyLinearEmbedding
 hlle = HessianLocallyLinearEmbedding()
 hlle.set_k(6)
 converters.append((hlle,"Hessian LLE with k=%d" % (hlle.get_k())))
 
-from shogun.Converter import LocalTangentSpaceAlignment
+from shogun import LocalTangentSpaceAlignment
 ltsa = LocalTangentSpaceAlignment()
 ltsa.set_k(6)
 converters.append((ltsa,"LTSA with k=%d" % (ltsa.get_k())))
 
-from shogun.Converter import LaplacianEigenmaps
+from shogun import LaplacianEigenmaps
 le = LaplacianEigenmaps()
 le.set_k(20)
 le.set_tau(100.0)
@@ -67,7 +67,7 @@
 plt.suptitle('Swissroll embedding',fontsize=9)
 plt.subplots_adjust(hspace=0.4)
 
-from shogun.Features import RealFeatures
+from shogun import RealFeatures
 
 for (i, (converter, label)) in enumerate(converters):
 	X = numpy.genfromtxt('../../data/toy/swissroll.dat',unpack=True).T