if Y_predict[i] == Y_test[i]:
equal += 1
print 'Accuracy = %s' % (float(equal) / len(Y_predict))
trainFile = TrainFile("../data/train.csv", True)
trainFile.Read()
testFile = TestFile("../data/test.csv", True)
testFile.Read()
print "Data loaded..."
X = np.array(trainFile.data)
Y = np.array(trainFile.labels)
# just like the face recognition, we compute the avg digit image
avg_digit = compute_avg_digits(X, configs.IMAGE_WIDTH)
print "Avg digit computed ..."
# Substract each input with the avg
X_normalized_avg = normalize_with_avg(X, avg_digit)
X_normalized = preprocessing.normalize(X_normalized_avg)
print "Normalize X ..."
# Eigen Face
n_component = 0.07
pca = PCA(n_components=configs.IMAGE_WIDTH * configs.IMAGE_WIDTH * n_component)
features = pca.fit_transform(X_normalized)
print "Transform done ..."
# split into training and testing
#cutoff = len(Y) * 0.75
scores = cross_val_score(model, X, y, cv=cv, verbose=0, n_jobs=1)
print("Accuracy:")
print("mean: {:3f}, stdev: {:3f}".format(
np.mean(scores), np.std(scores)))
trainFile = TrainFile("../data/train.csv", True)
trainFile.Read()
testFile = TestFile("../data/test.csv", True)
testFile.Read()
print "Data loaded..."
X = np.array(trainFile.data)
Y = np.array(trainFile.labels)
# just like the face recognition, we compute the avg digit image
avg_digit = compute_avg_digits(X, configs.IMAGE_WIDTH)
print "Avg digit computed ..."
# Substract each input with the avg
X_normalized_avg = normalize_with_avg(X, avg_digit)
X_normalized = preprocessing.normalize(X_normalized_avg)
print "Normalize X ..."
# Eigen Face
for n_component in [ 0.06, 0.07, 0.08 ]:
pca = PCA(n_components=configs.IMAGE_WIDTH * configs.IMAGE_WIDTH * n_component)
features = pca.fit_transform(X_normalized)
print "Transform done ..."
features = np.array(features)
Y = np.array(Y)