from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0) # Feature Scaling from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test) # Fitting classifier to the Training set from sklearn.svm import SVM classifier = SVM(kernel='rbf', random_state=0) classifier.fit(X_train, y_train) # Predicting the Test set results y_pred = classifier.predict(X_test) # Making the Confusion Matrix from sklearn.metrics import confusion_matrix cm = confusion_matrix(y_test, y_pred) # Visualising the Training set results from matplotlib.colors import ListedColormap X_set, y_set = X_train, y_train X1, X2 = np.meshgrid( np.arange(start=X_set[:, 0].min() - 1, stop=X_set[:, 0].max() + 1, step=0.01),
for i in range(len(x)): N = 2803 - len(x[0]) t1 = np.pad(x[0], (0, N), 'median') X.append(t1) svm = SVM() X = np.array(X) X = np.squeeze(X) model = svm.fit(X, Y) accuracy = [] final_result = [] for i in range(len(testing_set)): temp = preprocessor(Folders[1], testing_set[i]) N = 2803 - len(temp) temp_1 = np.array(temp).reshape(1, len(temp)) temp_2 = np.pad(temp_1[0], (0, N), 'median')