def main():
X, y = read_dataset("../data/regression_example2.txt", ',')
# 5 points into training set, 5 points into test set
X_TRAIN = X[:int(X.shape[0] / 2), ]
y_train = y[:int(X.shape[0] / 2), ]
X_TEST = X[int(X.shape[0] / 2):, ]
y_test = y[int(X.shape[0] / 2):, ]
print("Reading-in ready")
########################################################
# FIND REGRESSION POLYNOMIAL OF DEGREE 4 of form y = ax^4 + bx^3 + cx^2 + dx WITH REGULARIZER
########################################################
# TRY TO CHANGE LAMBDA VALUE TO SOME OTHER NUMBER: TRY - [0, 1, 10, 30, 100, 10000]
lmbda = 10000
X_TRAIN_2 = X_TRAIN**2
X_TRAIN_3 = X_TRAIN**3
X_TRAIN_4 = X_TRAIN**4
X_TRAIN_FINAL = np.concatenate((X_TRAIN_4, X_TRAIN_3, X_TRAIN_2, X_TRAIN),
axis=1)
w = find_weights_with_regularizer(X_TRAIN_FINAL, y_train, lmbda)
xt_range = np.arange(0, 11, 0.5).reshape((-1, 1))
xt_range_2 = xt_range**2
xt_range_3 = xt_range**3
xt_range_4 = xt_range**4
xt_range_final = np.concatenate(
(xt_range_4, xt_range_3, xt_range_2, xt_range), axis=1)
regression_predictions = np.dot(xt_range_final, w)
# TRAINING DATA and TEST DATA
pl.scatter(X_TRAIN, y_train, s=20, color='blue')
pl.scatter(X_TEST, y_test, s=20, color='orange')
pl.plot(xt_range, regression_predictions, color='red')
pl.title(
"RIDGE REGRESSION $y = ax^4 + bx^3 + cx^2 + dx$ with $\lambda = $" +
str(lmbda))
pl.xlabel("x")
pl.ylabel("y")
pl.show()
for i in range(5):
pass
def main():
X, y = read_dataset("../data/regression_example1.txt", ',')
print("Reading-in ready")
#####################################
# FIND REGRESSION LINE of form y = ax^3 + bx^2 + cx + d
#####################################
x_range = []
regression_predictions = []
# YOUR CODE STARTS HERE
# YOUR CODE ENDS HERE
pl.scatter(X, y, s=10)
pl.plot(x_range, regression_predictions, color='red')
pl.title("Linear regression $y = ax^2 + bx^2 + cx + d$ fit to data")
pl.xlabel("City population size, million")
pl.ylabel("Hot-dog selling van profit, K")
pl.show()
def main():
X, y = read_dataset("../data/train.txt", ' ')
T = np.loadtxt("../data/test.txt")
print("Loading done")
def main():
X, y = read_dataset("../data/regression_example1.txt", ',')
print("Reading-in ready")
# ONLY DATA
# draw data with labels
pl.scatter(X, y, s=10)
pl.title("Data")
pl.xlabel("City population size, million")
pl.ylabel("Hot-dog selling van profit, K")
pl.show()
#####################################
# FIND REGRESSION LINE of form y = ax
#####################################
w = find_weights(X, y)
# we found line parameters, now draw line
x_range = np.arange(5, 23, 0.5).reshape((-1, 1))
regression_predictions = np.dot(x_range, w)
pl.scatter(X, y, s=10)
pl.plot(x_range, regression_predictions, color='red')
pl.title("Linear regression $y = ax$ fit to data")
pl.xlabel("City population size, million")
pl.ylabel("Hot-dog selling van profit, K")
pl.show()
#########################################
# FIND REGRESSION LINE of form y = ax + b
#########################################
Xb = np.ones((X.shape[0], 1))
Xb = np.concatenate((X, Xb), axis=1)
w = find_weights(Xb, y)
# we found line parameters, now draw line
x_range = np.arange(5, 23, 0.5).reshape((-1, 1))
x_range_b = np.ones((x_range.shape[0], 1))
x_range_b = np.concatenate((x_range, x_range_b), axis=1)
regression_predictions = np.dot(x_range_b, w)
pl.scatter(X, y, s=10)
pl.plot(x_range, regression_predictions, color='red')
pl.title("Linear regression $y = ax + b$ fit to data")
pl.xlabel("City population size, million")
pl.ylabel("Hot-dog selling van profit, K")
pl.show()
########################################################
# FIND REGRESSION POLYNOMIAL OF DEGREE 2 of form y = ax^2
########################################################
X2_2 = X**2
X2 = np.concatenate((X2_2, X), axis=1)
w = find_weights(X2, y)
x_range = np.arange(5, 23, 0.5).reshape((-1, 1))
x_range_2_2 = x_range**2
x_range_2 = np.concatenate((x_range_2_2, x_range), axis=1)
regression_predictions = np.dot(x_range_2, w)
pl.scatter(X, y, s=10)
pl.plot(x_range, regression_predictions, color='red')
pl.title("Linear regression $y = ax^2$ fit to data")
pl.xlabel("City population size, million")
pl.ylabel("Hot-dog selling van profit, K")
pl.show()
########################################################
# FIND REGRESSION POLYNOMIAL OF DEGREE 4 of form y = ax^4 + bx^3 + cx^2 + dx
########################################################
X4_2 = X**2
X4_3 = X**3
X4_4 = X**4
X4 = np.concatenate((X4_4, X4_3, X4_2, X), axis=1)
w = find_weights(X4, y)
x_range = np.arange(5, 23, 0.5).reshape((-1, 1))
x_range_4_2 = x_range**2
x_range_4_3 = x_range**3
x_range_4_4 = x_range**4
x_range_4 = np.concatenate(
(x_range_4_4, x_range_4_3, x_range_4_2, x_range), axis=1)
regression_predictions = np.dot(x_range_4, w)
pl.scatter(X, y, s=10)
pl.plot(x_range, regression_predictions, color='red')
pl.title("Linear regression $y = ax^4 + bx^3 + cx^2 + dx$ fit to data")
pl.xlabel("City population size, million")
pl.ylabel("Hot-dog selling van profit, K")
pl.show()