def main():
data_train, data_test, stars_train, stars_test = read_lexicon(
int(sys.argv[1]))
print("Training size: %d" % len(data_train))
if "-b" in sys.argv:
stars_train, stars_test = binary_stars(stars_train, stars_test)
clf = svm.SVC()
clf.fit(data_train, stars_train)
predictions = clf.predict(data_test)
print("Accuracy: %f\n" % accuracy_score(stars_test, predictions))
def main():
data_train, data_test, stars_train, stars_test = get_datasets(
attrs=["latitude", "longitude"])
print("Training size: %d" % len(data_train))
knn = neighbors.KNeighborsClassifier(n_neighbors=int(sys.argv[1]))
if "-b" in sys.argv:
stars_train, stars_test = binary_stars(stars_train, stars_test)
knn.fit(data_train, stars_train)
predictions = knn.predict(data_test)
print("Accuracy: %f\n" % accuracy_score(stars_test, predictions))
def main():
data_train, data_test, stars_train, stars_test = get_datasets()
print("Training size: %d" % len(data_train))
if "-b" in sys.argv:
stars_train, stars_test = binary_stars(stars_train, stars_test)
logreg = linear_model.LogisticRegression()
logreg.fit(data_train, stars_train)
predictions = logreg.predict(data_test)
print("Accuracy: %f\n" % accuracy_score(stars_test, predictions))
print("Coef for each class and each attribute:\n")
print(logreg.coef_)
def main():
types = None
args = sys.argv
bi = False
if "-b" in sys.argv:
bi = True
args.remove("-b")
if len(args) > 1:
types = sys.argv[1].split(',')
data_train, data_test, stars_train, stars_test = get_datasets(types=types)
print("Training size: %d" % len(data_train))
if bi:
stars_train, stars_test = binary_stars(stars_train, stars_test)
clf = svm.SVC()
clf.fit(data_train, stars_train)
predictions = clf.predict(data_test)
print("Accuracy: %f\n" % accuracy_score(stars_test, predictions))
BINARY_MODE = True
n_classes = 5
NFEAUTRES = 15
def neural_net(data_test, stars_test):
with tf.Session() as sess:
new_saver = tf.train.import_meta_graph(META_PATH)
new_saver.restore(sess, MODEL_PATH)
graph = tf.get_default_graph()
x = graph.get_tensor_by_name("x:0")
y = graph.get_tensor_by_name("y:0")
op_to_restore = graph.get_tensor_by_name("output_op:0")
correct = tf.equal(tf.argmax(op_to_restore, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
print('Test size:', len(stars_test))
print('Accuracy:',accuracy.eval({x: data_test, y: stars_test}))
def convert_stars_obj(stars):
return [[1 if (x + 1) == z else 0 for x in range(n_classes)] for z in stars]
data, stars = get_test_routes()
if BINARY_MODE:
stars, stars_test = binary_stars(stars, stars)
n_classes = 2
data = np.array(data, dtype=np.float32)
stars = np.array(stars, dtype=np.float32)
stars = convert_stars_obj(stars)
neural_net(data, stars)
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
print('Min loss: ', min_loss)
print('Training size:', len(stars_train))
print('Test size:', len(stars_test))
print('Accuracy:', accuracy.eval({x: data_test, y: stars_test}))
def convert_stars_obj(stars):
return [[1 if (x + 1) == z else 0 for x in range(n_classes)]
for z in stars]
data_train, data_test, stars_train, stars_test = get_datasets()
if BINARY_MODE:
stars_train, stars_test = binary_stars(stars_train, stars_test)
n_classes = 2
y = tf.placeholder('float', [None, n_classes], name="y") # label of the data
x = tf.placeholder('float', [None, NFEAUTRES], name="x") #input data
data_train = np.array(data_train, dtype=np.float32)
data_test = np.array(data_test, dtype=np.float32)
stars_train = np.array(stars_train, dtype=np.float32)
stars_test = np.array(stars_test, dtype=np.float32)
stars_test = convert_stars_obj(stars_test)
stars_train = convert_stars_obj(stars_train)
if (batch_size > len(data_train)):
batch_size = len(data_train)
train_neural_network()