Python plot_data_function示例

示例#1

文件： ml_assgn1_ex2.py 项目： Mr-Oneal/Big-Data-Science-distilled

# Train set

# Plot the cost for all iterations
fig, ax1 = plt.subplots()
plot_cost(cost_vector, ax1)
plot_filename = os.path.join(os.getcwd(), 'figures', 'ex2_train_cost.png')
plt.savefig(plot_filename)
min_cost = np.min(cost_vector)
argmin_cost = np.argmin(cost_vector)
print('Final training cost: {:.5f}'.format(cost_vector[-1]))
print('Minimum training cost: {:.5f}, on iteration #{}'.format(
    min_cost, argmin_cost + 1))

# plot our data and decision boundary
fig, ax1 = plt.subplots()
ax1 = plot_data_function(X_train_normalized, y_train, ax1)
plot_boundary(X_train_normalized, theta_final, ax1)
# save the plotted decision boundary as a figure
plot_filename = os.path.join(os.getcwd(), 'figures',
                             'ex2_decision_boundary_train.png')
plt.savefig(plot_filename)

###################################################
# Test set

cost_test = compute_cost(X_test_normalized, y_test, theta_final)
print('Final test cost: {:.5f}'.format(cost_test))

fig, ax1 = plt.subplots()
ax1 = plot_data_function(X_test_normalized, y_test, ax1)
plot_boundary(X_test_normalized, theta_final, ax1)

示例#2

文件： plot_data.py 项目： otnemrasordep/linreg-logreg-nn

from load_data_ex1 import *
from normalize_features import *
import matplotlib.pyplot as plt
from plot_data_function import *

# this loads our data
X, y = load_data_ex1()

# this normalizes our data
X_normalized, mean_vec, std_vec = normalize_features(X)

# After normalizing, we append a column of ones to X_normalized, as the bias term
column_of_ones = np.ones((X_normalized.shape[0], 1))
# append column to the dimension of columns (i.e., 1)
X_normalized = np.append(column_of_ones, X_normalized, axis=1)

fig, ax1 = plt.subplots()
ax1 = plot_data_function(X_normalized, y, ax1)

# enter non-interactive mode of matplotlib, to keep figures open
plt.ioff()
plt.show()

示例#3

文件： plot_data.py 项目： kuchu0102/Machine-Learning

from load_data_ex1 import *
from normalize_features import *
import matplotlib.pyplot as plt
from plot_data_function import *

# this loads our data
X, y = load_data_ex1()

# this normalizes our data
#X_normalized, mean_vec, std_vec = normalize_features(X)

# After normalizing, we append a column of ones to X_normalized, as the bias term
column_of_ones = np.ones((X.shape[0], 1))
# append column to the dimension of columns (i.e., 1)
X = np.append(column_of_ones, X, axis=1)

fig, ax1 = plt.subplots()
ax1 = plot_data_function(X, y, ax1)

# enter non-interactive mode of matplotlib, to keep figures open
plt.ioff()
plt.show()