week3c.py

import numpy as np
%matplotlib widget
import matplotlib.pyplot as plt
from lab_utils_common import plot_data, sigmoid, draw_vthresh
plt.style.use('./deeplearning.mplstyle')


X = np.array([[0.5, 1.5], [1,1], [1.5, 0.5], [3, 0.5], [2, 2], [1, 2.5]])
y = np.array([0, 0, 0, 1, 1, 1]).reshape(-1,1) 


fig,ax = plt.subplots(1,1,figsize=(4,4))
plot_data(X, y, ax)

ax.axis([0, 4, 0, 3.5])
ax.set_ylabel('$x_1$')
ax.set_xlabel('$x_0$')
plt.show()


# Plot sigmoid(z) over a range of values from -10 to 10
z = np.arange(-10,11)

fig,ax = plt.subplots(1,1,figsize=(5,3))
# Plot z vs sigmoid(z)
ax.plot(z, sigmoid(z), c="b")

ax.set_title("Sigmoid function")
ax.set_ylabel('sigmoid(z)')
ax.set_xlabel('z')
draw_vthresh(ax,0)


# Choose values between 0 and 6
x0 = np.arange(0,6)

x1 = 3 - x0
fig,ax = plt.subplots(1,1,figsize=(5,4))
# Plot the decision boundary
ax.plot(x0,x1, c="b")
ax.axis([0, 4, 0, 3.5])

# Fill the region below the line
ax.fill_between(x0,x1, alpha=0.2)

# Plot the original data
plot_data(X,y,ax)
ax.set_ylabel(r'$x_1$')
ax.set_xlabel(r'$x_0$')
plt.show()