def compute_cost(X, t, w):
# YOUR CODE here:
data_mean, std = scal.mean_std(X)
X = scal.standardize(X, data_mean, std)
N = len(X) # Length of X square ft stored in N
first = (1) / (2 * N) # The first part of the equation 1/2N
second = np.sum((
np.dot(X, w) -
t)**2) # second part of equation which consists of sum and squaring it
cost = first * second # The cost function all together to get the actual value
return cost
def train(X, t, eta, epochs):
# YOUR CODE here:
data_mean, std = scal.mean_std(X)
data_scaled = scal.standardize(X, data_mean, std)
w_t = np.array([0, 0, 0, 0])
for e in range(epochs):
w_tplus1 = w_t - (eta * compute_gradient(data_scaled, t, w_t))
w_t = w_tplus1
return w_t
def scale(X):
dataset_ones = np.column_stack((np.ones(X.shape[0]), X))
data_mean, std = scal.mean_std(dataset_ones)
data_scaled = scal.standardize(dataset_ones, data_mean, std)
return data_scaled[:, 1]
type=str,
default='../data/polyfit',
help='Directory for the houses dataset.')
FLAGS, unparsed = parser.parse_known_args()
# Read the training data.
X_dataset, t_dataset = read_data(FLAGS.input_data_dir + "/dataset.txt")
X_train, t_train = read_data(FLAGS.input_data_dir + "/train.txt")
X_test, t_test = read_data(FLAGS.input_data_dir + "/test.txt")
X_devel, t_devel = read_data(FLAGS.input_data_dir + "/devel.txt")
# # Plotting for 4a # #
# plot for dataset, Part A
dataset_ones = np.column_stack((np.ones(X_dataset.shape[0]), X_dataset))
data_mean, std = scal.mean_std(dataset_ones)
scaled_dataset_x = scal.standardize(dataset_ones, data_mean, std)
plt.figure(0)
plt.title('X_dataset vs t_dataset')
plt.scatter(scaled_dataset_x[:, 1], t_dataset,
color='blue') # the dataset data
plt.xlabel("scaled dataset x")
plt.ylabel("t_dataset")
plt.savefig('X-t-dataset.png')
# # Part B # #
# # Plotting for 4b # #
# plot for train data
dataset_ones = np.column_stack((np.ones(X_train.shape[0]), X_train))
label='Train') # the train data
plt.plot(epochs_test_data,
costs_test_data,
marker='^',
color='limegreen',
label='Test') # the test data
plt.xlabel("#epochs")
plt.ylabel("j(w)")
leg1 = plt.legend()
plt.savefig('train_test_line.png')
# figure 2 linear aproximation
data_mean, std = scal.mean_std(x_train_biased)
scaled_xtrain = scal.standardize(x_train_biased, data_mean, std)
data_mean, std = scal.mean_std(x_test_biased)
scaled_xtest = scal.standardize(x_test_biased, data_mean, std)
plt.figure(2)
plt.title('House price vs adjusted floor size')
plt.scatter(scaled_xtrain[:, 1], ttrain, marker='o', label='Train')
plt.scatter(scaled_xtest[:, 1],
ttest,
marker='^',
color='limegreen',
label='Test')
plt.xlabel('adjusted floor size')
plt.ylabel('house price')
def main():
"""Run main function."""
parser = argparse.ArgumentParser('Univariate Exercise.')
parser.add_argument('-i',
'--input_data_dir',
type=str,
default='../data/univariate',
help='Directory for the univariate houses dataset.')
FLAGS, unparsed = parser.parse_known_args()
# Read the training and test data.
Xtrain, ttrain = read_data(FLAGS.input_data_dir + "/train.txt")
Xtest, ttest = read_data(FLAGS.input_data_dir + "/test.txt")
# Normalize and add bias term to Train data
mean_train, std_train = scaling.mean_std(Xtrain)
Xtrain = scaling.standardize(Xtrain, mean_train, std_train)
X1train = np.append(np.ones_like(ttrain), Xtrain, axis=1)
# Normalize and add bias term to Test data
Xtest = scaling.standardize(Xtest, mean_train, std_train) # XXX
X1test = np.append(np.ones_like(ttest), Xtest, axis=1)
# Hyperparameters
epochs, step, learning_rate = 200, 10, 0.1
# Get w from Train and use it on Test
w = train_BGD(X1train, ttrain, learning_rate, epochs)
print('epochs = {} w_BGD = {}'.format(epochs, w))
# Parameters for Test
rmse = compute_rmse(X1train, ttrain, w)
cost = compute_cost(X1train, ttrain, w)
grad = compute_gradient(X1train, ttrain, w)
print_train_outputs(epochs, learning_rate, mean_train, std_train, w, rmse,
cost)
# Compare w from normal eqn and BGD
w_norm_eqn = train_norm_eqn(X1train, ttrain)
w_bgd = train_BGD(X1train, ttrain, learning_rate, epochs)
w_sgd = train_SGD(X1train, ttrain, learning_rate, 115)
print("\n")
print("w_norm_eqn = {}".format(w_norm_eqn))
print("epochs = 200 w_bgd = {}".format(w_bgd))
print("epochs = 115 w_sgd = {}".format(w_sgd))
# plots
plot_cost_history(X1train, ttrain, epochs, step, learning_rate)
plot_train_test(X1train, ttrain, X1test, ttest, w_bgd, w_sgd)
# Compare w from BGD and SGD
#
w_norm_eqn = train_norm_eqn(X1train, ttrain)
iters = 200
a_mins = []
for i in np.arange(iters):
w_sgd = train_SGD(X1train, ttrain, learning_rate, i)
print("\n")
print("i = {}".format(i))
print("w_norm_eqn = {}".format(w_norm_eqn))
print('iters = {} w_BGD = {}'.format(iters, w))
print("iters = {} w_SGD = {}".format(i, w_sgd))
abs_diff_min = [
abs(w_sgd[0][0] - w_norm_eqn[0][0]) +
abs(w_sgd[0][1] - w_norm_eqn[0][1])
][0]
a_mins.append(abs_diff_min)
print("abs_diff_min = {}".format(abs_diff_min))
print('np.argmin(abs_diff_min) = ', np.argmin(a_mins))
print("len(a_mins) = {}".format(len(a_mins)))
