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')