fix python ml tutorials

doc/py_tutorials/py_ml/py_knn/py_knn_opencv/py_knn_opencv.markdown

@@ -42,9 +42,9 @@ train_labels = np.repeat(k,250)[:,np.newaxis]
 test_labels = train_labels.copy()
 
 # Initiate kNN, train the data, then test it with test data for k=1
-knn = cv2.KNearest()
+knn = cv2.ml.KNearest_create()
-knn.train(train,train_labels)
+knn.train(train, cv2.ml.ROW_SAMPLE, train_labels)
-ret,result,neighbours,dist = knn.find_nearest(test,k=5)
+ret,result,neighbours,dist = knn.findNearest(test,k=5)
 
 # Now we check the accuracy of classification
 # For that, compare the result with test_labels and check which are wrong
@@ -103,9 +103,9 @@ responses, trainData = np.hsplit(train,[1])
 labels, testData = np.hsplit(test,[1])
 
 # Initiate the kNN, classify, measure accuracy.
-knn = cv2.KNearest()
+knn = cv2.ml.KNearest_create()
-knn.train(trainData, responses)
+knn.train(trainData, cv2.ml.ROW_SAMPLE, responses)
-ret, result, neighbours, dist = knn.find_nearest(testData, k=5)
+ret, result, neighbours, dist = knn.findNearest(testData, k=5)
 
 correct = np.count_nonzero(result == labels)
 accuracy = correct*100.0/10000

doc/py_tutorials/py_ml/py_knn/py_knn_understanding/py_knn_understanding.markdown

@@ -114,9 +114,9 @@ So let's see how it works. New comer is marked in green color.
 newcomer = np.random.randint(0,100,(1,2)).astype(np.float32)
 plt.scatter(newcomer[:,0],newcomer[:,1],80,'g','o')
 
-knn = cv2.KNearest()
+knn = cv2.ml.KNearest_create()
-knn.train(trainData,responses)
+knn.train(trainData, cv2.ml.ROW_SAMPLE, responses)
-ret, results, neighbours ,dist = knn.find_nearest(newcomer, 3)
+ret, results, neighbours ,dist = knn.findNearest(newcomer, 3)
 
 print "result: ", results,"\n"
 print "neighbours: ", neighbours,"\n"
@@ -140,7 +140,7 @@ obtained as arrays.
 @code{.py}
 # 10 new comers
 newcomers = np.random.randint(0,100,(10,2)).astype(np.float32)
-ret, results,neighbours,dist = knn.find_nearest(newcomer, 3)
+ret, results,neighbours,dist = knn.findNearest(newcomer, 3)
 # The results also will contain 10 labels.
 @endcode
 Additional Resources

doc/py_tutorials/py_ml/py_svm/py_svm_opencv/py_svm_opencv.markdown

@@ -64,9 +64,6 @@ import numpy as np
 SZ=20
 bin_n = 16 # Number of bins
 
-svm_params = dict( kernel_type = cv2.SVM_LINEAR,
-                    svm_type = cv2.SVM_C_SVC,
-                    C=2.67, gamma=5.383 )
 
 affine_flags = cv2.WARP_INVERSE_MAP|cv2.INTER_LINEAR
 
@@ -105,8 +102,13 @@ hogdata = [map(hog,row) for row in deskewed]
 trainData = np.float32(hogdata).reshape(-1,64)
 responses = np.float32(np.repeat(np.arange(10),250)[:,np.newaxis])
 
-svm = cv2.SVM()
+svm = cv2.ml.SVM_create()
-svm.train(trainData,responses, params=svm_params)
+svm.setKernel(cv2.ml.SVM_LINEAR)
+svm.setType(cv2.ml.SVM_C_SVC)
+svm.setC(2.67)
+svm.setGamma(5.383)
+
+svm.train(trainData, cv2.ml.ROW_SAMPLE, responses)
 svm.save('svm_data.dat')
 
 ######     Now testing      ########################
@@ -114,7 +116,7 @@ svm.save('svm_data.dat')
 deskewed = [map(deskew,row) for row in test_cells]
 hogdata = [map(hog,row) for row in deskewed]
 testData = np.float32(hogdata).reshape(-1,bin_n*4)
-result = svm.predict_all(testData)
+result = svm.predict(testData)
 
 #######   Check Accuracy   ########################
 mask = result==responses