Merge branch 'release/6.1.3'

shogun-toolbox · Dec 7, 2017 · 94be17f · 94be17f
2 parents dad82b4 + 459b968
commit 94be17f
Show file tree

Hide file tree

Showing 40 changed files with 383 additions and 539 deletions.
diff --git a/NEWS b/NEWS
@@ -1,3 +1,17 @@
+2017-12-05 Viktor Gal <viktor.gal@shogun-toolbox.org>
+
+	* SHOGUN Release version 6.1.3 (libshogun 18.0, data 0.11, parameter 1)
+
+	* Features:
+		- Drop all <math.h> function calls [Viktor Gal]
+		- Use c++11 std::isnan, std:isfinite, std::isinf [Viktor Gal]
+	* Bugfixes:
+		- Port ipython notebooks to be python3 compatible [Viktor Gal]
+		- Use the shogun-static library on Windows when linking the interface library [Viktor Gal]
+		- Fix python typemap when compiling with MSVC [Viktor Gal]
+		- Fix ShogunConfig.cmake paths [Viktor Gal]
+		- Fix meta example parser bug in parallel builds [Esben Sørig]
+
 2017-11-29 Viktor Gal <viktor.gal@shogun-toolbox.org>
 
 	* SHOGUN Release version 6.1.2 (libshogun 18.0, data 0.11, parameter 1)

diff --git a/cmake/ShogunInterfaces.cmake b/cmake/ShogunInterfaces.cmake
@@ -33,7 +33,7 @@ SET(INTERFACE_TARGET interface_${INTERFACE_NAME})
 SET(INTERFACE_TARGET_SRC ${INTERFACE_TARGET}_src)
 
 ADD_CUSTOM_TARGET(${INTERFACE_TARGET_SRC}
-	DEPENDS shogun::shogun ${INTERFACE_FILES}
+	DEPENDS ${INTERFACE_FILES}
 	COMMENT "copying SWIG files")
 
 INCLUDE(${SWIG_USE_FILE})
@@ -43,8 +43,11 @@ IF(DEFINED TARGET_SWIGFLAGS)
 ENDIF()
 SET(SWIG_MODULE_${INTERFACE_NAME}_EXTRA_DEPS ${INTERFACE_FILES})
 SWIG_ADD_MODULE(${INTERFACE_TARGET} ${INTERFACE_NAME} shogun.i sg_print_functions.cpp)
-SWIG_LINK_LIBRARIES(${INTERFACE_TARGET} shogun::shogun ${INTERFACE_LIBRARIES})
-
+IF (WIN32)
+	SWIG_LINK_LIBRARIES(${INTERFACE_TARGET} shogun::shogun-static ${INTERFACE_LIBRARIES})
+ELSE ()
+	SWIG_LINK_LIBRARIES(${INTERFACE_TARGET} shogun::shogun ${INTERFACE_LIBRARIES})
+ENDIF ()
 
 #get_cmake_property(_variableNames VARIABLES)
 #foreach (_variableName ${_variableNames})

diff --git a/doc/ipython-notebooks/classification/MKL.ipynb b/doc/ipython-notebooks/classification/MKL.ipynb
@@ -171,17 +171,17 @@
     "[gmm.set_nth_mean(means[i], i) for i in range(num_components)]\n",
     "[gmm.set_nth_cov(covs,i) for i in range(num_components)]\n",
     "gmm.set_coef(array([1.0,0.0,0.0,0.0]))\n",
-    "xntr=array([gmm.sample() for i in xrange(num)]).T\n",
-    "xnte=array([gmm.sample() for i in xrange(5000)]).T\n",
+    "xntr=array([gmm.sample() for i in range(num)]).T\n",
+    "xnte=array([gmm.sample() for i in range(5000)]).T\n",
     "gmm.set_coef(array([0.0,1.0,0.0,0.0]))\n",
-    "xntr1=array([gmm.sample() for i in xrange(num)]).T\n",
-    "xnte1=array([gmm.sample() for i in xrange(5000)]).T\n",
+    "xntr1=array([gmm.sample() for i in range(num)]).T\n",
+    "xnte1=array([gmm.sample() for i in range(5000)]).T\n",
     "gmm.set_coef(array([0.0,0.0,1.0,0.0]))\n",
-    "xptr=array([gmm.sample() for i in xrange(num)]).T\n",
-    "xpte=array([gmm.sample() for i in xrange(5000)]).T\n",
+    "xptr=array([gmm.sample() for i in range(num)]).T\n",
+    "xpte=array([gmm.sample() for i in range(5000)]).T\n",
     "gmm.set_coef(array([0.0,0.0,0.0,1.0]))\n",
-    "xptr1=array([gmm.sample() for i in xrange(num)]).T\n",
-    "xpte1=array([gmm.sample() for i in xrange(5000)]).T\n",
+    "xptr1=array([gmm.sample() for i in range(num)]).T\n",
+    "xpte1=array([gmm.sample() for i in range(5000)]).T\n",
     "traindata=concatenate((xntr,xntr1,xptr,xptr1), axis=1)\n",
     "trainlab=concatenate((-ones(2*num), ones(2*num)))\n",
     "\n",
@@ -269,7 +269,7 @@
     "mkl.train()    \n",
     "\n",
     "w=kernel.get_subkernel_weights()\n",
-    "print w"
+    "print(w)"
    ]
   },
   {
@@ -406,22 +406,22 @@
     "out=mkl.apply()\n",
     "\n",
     "evaluator=ErrorRateMeasure()\n",
-    "print \"Test error is %2.2f%% :MKL\" % (100*evaluator.evaluate(out,BinaryLabels(testlab)))\n",
+    "print(\"Test error is %2.2f%% :MKL\" % (100*evaluator.evaluate(out,BinaryLabels(testlab))))\n",
     "\n",
     "\n",
     "comb_ker0t.init(feats_train,RealFeatures(testdata)) \n",
     "mkl.set_kernel(comb_ker0t)\n",
     "out=mkl.apply()\n",
     "\n",
     "evaluator=ErrorRateMeasure()\n",
-    "print \"Test error is %2.2f%% :Subkernel1\"% (100*evaluator.evaluate(out,BinaryLabels(testlab)))\n",
+    "print(\"Test error is %2.2f%% :Subkernel1\"% (100*evaluator.evaluate(out,BinaryLabels(testlab))))\n",
     "\n",
     "comb_ker1t.init(feats_train, RealFeatures(testdata))\n",
     "mkl.set_kernel(comb_ker1t)\n",
     "out=mkl.apply()\n",
     "\n",
     "evaluator=ErrorRateMeasure()\n",
-    "print \"Test error is %2.2f%% :subkernel2\" % (100*evaluator.evaluate(out,BinaryLabels(testlab)))\n"
+    "print(\"Test error is %2.2f%% :subkernel2\" % (100*evaluator.evaluate(out,BinaryLabels(testlab))))\n"
    ]
   },
   {
@@ -546,7 +546,7 @@
     "\n",
     "\n",
     "w, mkl=train_mkl(c, feats_tr)\n",
-    "print w\n",
+    "print(w)\n",
     "out=test_mkl(mkl,grid)\n",
     "\n",
     "z=out.get_values().reshape((size, size))\n",
@@ -659,8 +659,8 @@
     "Nsplit = 2\n",
     "all_ks = range(1, 21)\n",
     "\n",
-    "print Xall.shape\n",
-    "print Xtrain.shape"
+    "print(Xall.shape)\n",
+    "print(Xtrain.shape)"
    ]
   },
   {
@@ -679,7 +679,7 @@
    "outputs": [],
    "source": [
     "def plot_example(dat, lab):\n",
-    "    for i in xrange(5):\n",
+    "    for i in range(5):\n",
     "        ax=subplot(1,5,i+1)\n",
     "        title(int(lab[i]))\n",
     "        ax.imshow(dat[:,i].reshape((16,16)), interpolation='nearest')\n",
@@ -753,7 +753,7 @@
     "out =  mkl.apply()\n",
     "evaluator = MulticlassAccuracy()\n",
     "accuracy = evaluator.evaluate(out, labels_rem)\n",
-    "print \"Accuracy = %2.2f%%\" % (100*accuracy)\n",
+    "print(\"Accuracy = %2.2f%%\" % (100*accuracy))\n",
     "\n",
     "idx=where(out.get_labels() != Yrem)[0]\n",
     "Xbad=Xrem[:,idx]\n",
@@ -772,7 +772,7 @@
    "outputs": [],
    "source": [
     "w=kernel.get_subkernel_weights()\n",
-    "print w"
+    "print(w)"
    ]
   },
   {
@@ -794,7 +794,7 @@
     "evaluator = MulticlassAccuracy()\n",
     "accuracy = evaluator.evaluate(out, labels_rem)\n",
     "\n",
-    "print \"Accuracy = %2.2f%%\" % (100*accuracy)\n",
+    "print(\"Accuracy = %2.2f%%\" % (100*accuracy))\n",
     "\n",
     "idx=np.where(out.get_labels() != Yrem)[0]\n",
     "Xbad=Xrem[:,idx]\n",
@@ -825,7 +825,7 @@
     "evaluator = MulticlassAccuracy()\n",
     "accuracy = evaluator.evaluate(out, labels_rem)\n",
     "\n",
-    "print \"Accuracy = %2.2f%%\" % (100*accuracy)\n",
+    "print(\"Accuracy = %2.2f%%\" % (100*accuracy))\n",
     "\n",
     "idx=np.where(out.get_labels() != Yrem)[0]\n",
     "Xbad=Xrem[:,idx]\n",
@@ -942,9 +942,9 @@
    "outputs": [],
    "source": [
     "mkl.train()\n",
-    "print \"Weights:\"\n",
+    "print(\"Weights:\")\n",
     "w=kernel.get_subkernel_weights()\n",
-    "print w\n",
+    "print(w)\n",
     "\n",
     "#initialize with test features\n",
     "kernel.init(feats_train, feats_test)     \n",

diff --git a/doc/ipython-notebooks/classification/SupportVectorMachines.ipynb b/doc/ipython-notebooks/classification/SupportVectorMachines.ipynb
@@ -194,7 +194,7 @@
     "def solve (x1):\n",
     "    return -( ( (w[0])*x1 + b )/w[1] )\n",
     "\n",
-    "x2=map(solve, x1)\n",
+    "x2=list(map(solve, x1))\n",
     "\n",
     "#plot\n",
     "plt.figure(figsize=(6,6))\n",
@@ -395,7 +395,7 @@
     "libsvm_obj=svm.get_objective()\n",
     "primal_obj, dual_obj=svm.compute_svm_primal_objective(), svm.compute_svm_dual_objective()\n",
     "\n",
-    "print libsvm_obj, primal_obj, dual_obj"
+    "print(libsvm_obj, primal_obj, dual_obj)"
    ]
   },
   {
@@ -413,7 +413,7 @@
    },
    "outputs": [],
    "source": [
-    "print \"duality_gap\", dual_obj-primal_obj"
+    "print(\"duality_gap\", dual_obj-primal_obj)"
    ]
   },
   {
@@ -635,10 +635,10 @@
     "gmm.set_nth_cov(np.array([[1.0,0.0],[0.0,1.0]]),1)\n",
     "\n",
     "gmm.set_coef(np.array([1.0,0.0]))\n",
-    "xntr=np.array([gmm.sample() for i in xrange(num)]).T\n",
+    "xntr=np.array([gmm.sample() for i in range(num)]).T\n",
     "\n",
     "gmm.set_coef(np.array([0.0,1.0]))\n",
-    "xptr=np.array([gmm.sample() for i in xrange(num)]).T\n",
+    "xptr=np.array([gmm.sample() for i in range(num)]).T\n",
     "\n",
     "traindata=np.concatenate((xntr,xptr), axis=1)\n",
     "trainlab=np.concatenate((-np.ones(num), np.ones(num)))\n",
@@ -847,7 +847,7 @@
     "\n",
     "Err=sg.ErrorRateMeasure()\n",
     "error=Err.evaluate(output, lab_test)\n",
-    "print 'Error:', error\n",
+    "print('Error:', error)\n",
     "\n",
     "#set normalization\n",
     "gaussian_kernel=sg.GaussianKernel()\n",
@@ -863,7 +863,7 @@
     "\n",
     "Err=sg.ErrorRateMeasure()\n",
     "error=Err.evaluate(output, lab_test)\n",
-    "print 'Error with normalization:', error"
+    "print('Error with normalization:', error)"
    ]
   },
   {
@@ -902,24 +902,24 @@
     "[gmm.set_nth_mean(means[i], i) for i in range(num_components)]\n",
     "[gmm.set_nth_cov(covs,i) for i in range(num_components)]\n",
     "gmm.set_coef(np.array([1.0,0.0,0.0,0.0]))\n",
-    "xntr=np.array([gmm.sample() for i in xrange(num)]).T\n",
-    "xnte=np.array([gmm.sample() for i in xrange(5000)]).T\n",
+    "xntr=np.array([gmm.sample() for i in range(num)]).T\n",
+    "xnte=np.array([gmm.sample() for i in range(5000)]).T\n",
     "gmm.set_coef(np.array([0.0,1.0,0.0,0.0]))\n",
-    "xntr1=np.array([gmm.sample() for i in xrange(num)]).T\n",
-    "xnte1=np.array([gmm.sample() for i in xrange(5000)]).T\n",
+    "xntr1=np.array([gmm.sample() for i in range(num)]).T\n",
+    "xnte1=np.array([gmm.sample() for i in range(5000)]).T\n",
     "gmm.set_coef(np.array([0.0,0.0,1.0,0.0]))\n",
-    "xptr=np.array([gmm.sample() for i in xrange(num)]).T\n",
-    "xpte=np.array([gmm.sample() for i in xrange(5000)]).T\n",
+    "xptr=np.array([gmm.sample() for i in range(num)]).T\n",
+    "xpte=np.array([gmm.sample() for i in range(5000)]).T\n",
     "gmm.set_coef(np.array([0.0,0.0,0.0,1.0]))\n",
-    "xptr1=np.array([gmm.sample() for i in xrange(num)]).T\n",
-    "xpte1=np.array([gmm.sample() for i in xrange(5000)]).T\n",
+    "xptr1=np.array([gmm.sample() for i in range(num)]).T\n",
+    "xpte1=np.array([gmm.sample() for i in range(5000)]).T\n",
     "traindata=np.concatenate((xntr,xntr1,xptr,xptr1), axis=1)\n",
     "testdata=np.concatenate((xnte,xnte1,xpte,xpte1), axis=1)\n",
     "\n",
-    "l0 = np.array([0.0 for i in xrange(num)])\n",
-    "l1 = np.array([1.0 for i in xrange(num)])\n",
-    "l2 = np.array([2.0 for i in xrange(num)])\n",
-    "l3 = np.array([3.0 for i in xrange(num)])\n",
+    "l0 = np.array([0.0 for i in range(num)])\n",
+    "l1 = np.array([1.0 for i in range(num)])\n",
+    "l2 = np.array([2.0 for i in range(num)])\n",
+    "l3 = np.array([3.0 for i in range(num)])\n",
     "\n",
     "trainlab=np.concatenate((l0,l1,l2,l3))\n",
     "testlab=np.concatenate((l0,l1,l2,l3))\n",

diff --git a/doc/ipython-notebooks/clustering/KMeans.ipynb b/doc/ipython-notebooks/clustering/KMeans.ipynb
@@ -195,7 +195,7 @@
      "input": [
       "def plotResult(title = 'KMeans Plot'):\n",
       "    figure,axis = pyplot.subplots(1,1)\n",
-      "    for i in xrange(totalPoints):\n",
+      "    for i in range(totalPoints):\n",
       "        if result[i]==0.0:\n",
       "            axis.plot(rectangle[0,i], rectangle[1,i], 'o', color='g', markersize=3)\n",
       "        else:\n",
@@ -638,7 +638,7 @@
      "input": [
       "# plot the clusters over the original points in 2 dimensions\n",
       "figure,axis = pyplot.subplots(1,1)\n",
-      "for i in xrange(150):\n",
+      "for i in range(150):\n",
       "    if result[i]==0.0:\n",
       "        axis.plot(obsmatrix[2,i],obsmatrix[3,i],'ko',color='r', markersize=5)\n",
       "    elif result[i]==1.0:\n",
@@ -707,7 +707,7 @@
       "    return (diff,accuracy)\n",
       "\n",
       "(diff,accuracy_4d) = analyzeResult(result)\n",
-      "print 'Accuracy : ' + str(accuracy_4d)\n",
+      "print('Accuracy : ' + str(accuracy_4d))\n",
       "\n",
       "# plot the difference between ground truth and predicted clusters\n",
       "figure,axis = pyplot.subplots(1,1)\n",
@@ -839,7 +839,7 @@
      "collapsed": false,
      "input": [
       "(diff,accuracy_1d) = analyzeResult(result)\n",
-      "print 'Accuracy : ' + str(accuracy_1d)\n",
+      "print('Accuracy : ' + str(accuracy_1d))\n",
       "\n",
       "# plot the difference between ground truth and predicted clusters\n",
       "figure,axis = pyplot.subplots(1,1)\n",
@@ -925,7 +925,7 @@
      "collapsed": false,
      "input": [
       "(diff,accuracy_2d) = analyzeResult(result)\n",
-      "print 'Accuracy : ' + str(accuracy_2d)\n",
+      "print('Accuracy : ' + str(accuracy_2d))\n",
       "\n",
       "# plot the difference between ground truth and predicted clusters\n",
       "figure,axis = pyplot.subplots(1,1)\n",
@@ -1001,7 +1001,7 @@
      "collapsed": false,
      "input": [
       "(diff,accuracy_3d) = analyzeResult(result)\n",
-      "print 'Accuracy : ' + str(accuracy_3d)\n",
+      "print('Accuracy : ' + str(accuracy_3d))\n",
       "\n",
       "# plot the difference between ground truth and predicted clusters\n",
       "figure,axis = pyplot.subplots(1,1)\n",

diff --git a/doc/ipython-notebooks/distributions/KernelDensity.ipynb b/doc/ipython-notebooks/distributions/KernelDensity.ipynb
@@ -82,8 +82,8 @@
       "\n",
       "# generates samples from the distribution\n",
       "def generate_samples(n_samples,mu1,sigma1,mu2,sigma2):\n",
-      "    samples1 = np.random.normal(mu1,sigma1,(1,n_samples/2))\n",
-      "    samples2 = np.random.normal(mu2,sigma2,(1,n_samples/2))\n",
+      "    samples1 = np.random.normal(mu1,sigma1,(1,int(n_samples/2)))\n",
+      "    samples2 = np.random.normal(mu2,sigma2,(1,int(n_samples/2)))\n",
       "    samples = np.concatenate((samples1,samples2),1)\n",
       "    return samples\n",
       "\n",
@@ -383,7 +383,7 @@
       "    query_feats=RealFeatures(np.array([x[0,:],y[0,:]]))\n",
       "    z=np.array([kdestimator.get_log_density(query_feats)])\n",
       "    z=np.exp(z)\n",
-      "    for i in xrange(1,x.shape[0]):\n",
+      "    for i in range(1,x.shape[0]):\n",
       "        query_feats=RealFeatures(np.array([x[i,:],y[i,:]]))\n",
       "        zi=np.exp(kdestimator.get_log_density(query_feats))\n",
       "        z=np.vstack((z,zi))\n",
@@ -441,9 +441,9 @@
       "\n",
       "# classify using our decision rule\n",
       "z=[]\n",
-      "for i in xrange(0,x.shape[0]):\n",
+      "for i in range(0,x.shape[0]):\n",
       "    zj=[]\n",
-      "    for j in xrange(0,x.shape[1]):\n",
+      "    for j in range(0,x.shape[1]):\n",
       "        if ((z1[i,j]>z2[i,j]) and (z1[i,j]>z3[i,j])):\n",
       "            zj.append(1)\n",
       "        elif (z2[i,j]>z3[i,j]):\n",