def train_perceptron(kernel_name, kernel, learning_rate):
"""Train a perceptron with the given kernel.
This function trains a perceptron with a given kernel and then
uses that perceptron to make predictions.
The output predictions are saved to src/output/p05_{kernel_name}_predictions.txt.
The output plots are saved to src/output_{kernel_name}_output.pdf.
Args:
kernel_name: The name of the kernel.
kernel: The kernel function.
learning_rate: The learning rate for training.
"""
train_x, train_y = util.load_csv('../data/ds5_train.csv')
state = initial_state()
for x_i, y_i in zip(train_x, train_y):
update_state(state, kernel, learning_rate, x_i, y_i)
test_x, test_y = util.load_csv('../data/ds5_train.csv')
plt.figure(figsize=(12, 8))
util.plot_contour(lambda a: predict(state, kernel, a))
util.plot_points(test_x, test_y)
plt.savefig('./output/p05_{}_output.pdf'.format(kernel_name))
predict_y = [
predict(state, kernel, test_x[i, :]) for i in range(test_y.shape[0])
]
np.savetxt('./output/p05_{}_predictions'.format(kernel_name), predict_y)
def main():
# plot
DEBUG = False
if DEBUG:
from util import plot_points
from matplotlib import pyplot as plt
plt.figure()
Xa, Ya = util.load_csv('../data/ds1_a.csv', add_intercept=False)
Ya = (Ya == 1).astype(np.float)
plot_points(Xa, Ya)
plt.figure()
Xb, Yb = util.load_csv('../data/ds1_b.csv', add_intercept=False)
Yb = (Yb == 1).astype(np.float)
plot_points(Xb, Yb)
plt.show()
import sys
sys.exit()
# print('==== Training model on data set A ====')
# Xa, Ya = util.load_csv('../data/ds1_a.csv', add_intercept=True)
# logistic_regression(Xa, Ya)
print('\n==== Training model on data set B ====')
Xb, Yb = util.load_csv('../data/ds1_b.csv', add_intercept=True)
logistic_regression(Xb, Yb)
def train_perceptron(train_path, test_path, kernel_name, kernel,
learning_rate):
"""Train a perceptron with the given kernel.
This function trains a perceptron with a given kernel and then uses that perceptron to make predictions.
The output predictions are saved to src/output/p05_{kernel_name}_predictions.txt
The output plots are saved to src/output_{kernel_name}_output.pdf
Args:
kernel_name: The name of the kernel
kernel: The kernel function
learning_rate: The learning rate for training
"""
train_x, train_y = util.load_csv(train_path)
test_x, test_y = util.load_csv(test_path)
state = initial_state()
for x_i, y_i in zip(train_x, train_y):
update_state(state, kernel, learning_rate, x_i, y_i)
plt.figure()
util.plot_contour(lambda a: predict(state, kernel, a))
util.plot_points(test_x, test_y)
plt.title(
f"Kernel: {kernel_name} || Test data, color corresponds to real label")
plt.legend()
plt.xlabel("x1")
plt.ylabel("x2")
y_pred = np.array(
[predict(state, kernel, test_x[i, :]) for i in range(test_y.shape[0])])
plt.figure()
util.plot_points(test_x, y_pred)
plt.title(
f"Kernel: {kernel_name} || Test data, color corresponds to predicted label"
)
plt.legend()
plt.xlabel("x1")
plt.ylabel("x2")
plt.show()
def train(pct, x_train, y_train, x_eval, y_eval):
"""Train a perceptron with the given kernel.
This function trains a perceptron with a given kernel and then
uses that perceptron to make predictions.
The output predictions are saved to src/output/p05_{kernel_name}_predictions.txt.
The output plots are saved to src/output_{kernel_name}_output.pdf.
Args:
kernel_name: The name of the kernel.
kernel: The kernel function.
learning_rate: The learning rate for training.
"""
for x, y in zip(x_train, y_train):
pct.update(x, y)
plt.figure(figsize=(12, 8))
util.plot_contour(lambda x: pct.predict(x))
util.plot_points(x_eval, y_eval)
plt.savefig('./output/p05_{}_output.pdf'.format(pct.kernel_name))
y_pred = [pct.predict(x_eval[i, :]) for i in range(y_eval.shape[0])]
np.savetxt('./output/p05_{}_predictions'.format(pct.kernel_name), y_pred)
def plot_clusters(key, location_type):
util.plot_points(util.rearrange_data(get_cluster_coords(key, location_type)))
def plot_seeds(key):
util.plot_points(util.rearrange_data(get_seed_coords(key)))
