Fixed bug with icount size

CHANGELOG.md

@@ -1,6 +1,10 @@
 # Change Log
 All changes to 'hpelm' toolbox will be documented in this file.
 
+## [1.0.3] - 22-03-2016
+### Fixed
+- Fixed a bug with "icount" size
+
 ## [1.0.3] - 15-03-2016
 ### Fixed
 - HPELM loading, various small errors

hpelm/hp_elm.py

@@ -128,7 +128,7 @@ def add_data(self, fX, fT, istart=0, icount=np.inf, fHH=None, fHT=None):
         N = X.shape[0]
         _prepare_fHH(fHH, fHT, self.nnet, self.precision)
         # custom range adjustments
-        icount = min(istart + icount, N)
+        icount = min(icount, N - istart)
         nb = int(np.ceil(float(icount) / self.batch))  # number of batches
 
         # weighted classification initialization
@@ -199,7 +199,7 @@ def predict(self, fX, fY=None, istart=0, icount=np.inf):
         X, _ = self._checkdata(fX, None)
         N = X.shape[0]
         # custom range adjustments
-        icount = min(istart + icount, N)
+        icount = min(icount, N - istart)
         nb = int(np.ceil(float(icount) / self.batch))  # number of batches
         # make file to store results
         if isinstance(fY, basestring):
@@ -256,7 +256,7 @@ def project(self, fX, fH=None, istart=0, icount=np.inf):
         X, _ = self._checkdata(fX, None)
         N = X.shape[0]
         # custom range adjustments
-        icount = min(istart + icount, N)
+        icount = min(icount, N - istart)
         nb = int(np.ceil(float(icount) / self.batch))  # number of batches
         # make file to store results
         if isinstance(fH, basestring):
@@ -335,7 +335,7 @@ def _error(self, T, Y, istart=0, icount=np.inf):
             icount (int): number of samples to process
         """
         N = T.shape[0]
-        icount = min(istart + icount, N)
+        icount = min(icount, N - istart)
         nb = int(np.ceil(float(icount) / self.batch))  # number of batches
 
         if self.classification == "c":
@@ -490,7 +490,7 @@ def add_data_async(self, fX, fT, istart=0, icount=np.inf, fHH=None, fHT=None):
         # TODO: adapt for GPU solver
         _prepare_fHH(fHH, fHT, self.nnet, self.precision)
         # custom range adjustments
-        icount = min(istart + icount, N)
+        icount = min(icount, N - istart)
         nb = int(np.ceil(float(icount) / self.batch))
 
         # weighted classification initialization
@@ -567,7 +567,7 @@ def predict_async(self, fX, fY, istart=0, icount=np.inf):
         X, _ = self._checkdata(fX, None)
         N = X.shape[0]
         # custom range adjustments
-        icount = min(istart + icount, N)
+        icount = min(icount, N - istart)
         nb = int(np.ceil(float(icount) / self.batch))  # number of batches
         # make file to store results
         make_hdf5((icount, self.nnet.outputs), fY)

setup.py

@@ -24,7 +24,7 @@ def readme():
 # sphinx-apidoc -f -o docs hpelm; cd docs; make html; cd ../
 
 setup(name='hpelm',
-      version='1.0.3',
+      version='1.0.4',
       description='High-Performance implementation of an Extreme Learning Machine',
       long_description=readme(),
       classifiers=[