def test_plot_decision_boundary(self):
negatives = self.data[self.data[:, 2] ==
0] # SELECT * FROM self.data WHERE col2 == 0
positives = self.data[self.data[:, 2] ==
1] # SELECT * FROM self.data WHERE col2 == 1
plt.xlabel("Microchip Test 1")
plt.ylabel("Microchip Test 2")
plt.scatter(negatives[:, 0],
negatives[:, 1],
c='y',
marker='o',
s=40,
linewidths=1,
label="y=0")
plt.scatter(positives[:, 0],
positives[:, 1],
c='b',
marker='+',
s=40,
linewidths=2,
label="y=1")
dimension = 6
X_mapped = map_feature(self.X[:, 0], self.X[:, 1], dimension)
m, n = X_mapped.shape
X_mapped = np.hstack((np.ones((m, 1)), X_mapped))
theta = np.zeros((n + 1, 1))
lamda = 1.0
theta_optimized, min_cost = regularized_gradient_descent(
X_mapped, self.y, theta, lamda)
x1 = np.linspace(-1, 1.5, 50)
x2 = np.linspace(-1, 1.5, 50)
X1, X2 = np.meshgrid(x1, x2)
hypo = np.zeros((len(x1), len(x2)))
for i in range(0, len(x1)):
for j in range(0, len(x2)):
mapped = map_feature(
np.array([X1[i][j]]).reshape((1, 1)),
np.array([X2[i][j]]).reshape((1, 1)), dimension)
mapped = np.hstack((np.ones((1, 1)), mapped))
hypo[i][j] = hypothesis(mapped, theta_optimized)[0]
plt.contour(X1, X2, hypo, [0.5], label='Decision Boundary')
plt.legend()
plt.show()
def test_regularized_gradient_descent(self):
dimension = 6
X_mapped = map_feature(self.X[:, 0], self.X[:, 1], dimension)
m, n = X_mapped.shape
X_mapped = np.hstack((np.ones((m, 1)), X_mapped))
theta = np.zeros((n + 1, 1))
lamda = 1.0
theta_optimized, min_cost = regularized_gradient_descent(
X_mapped, self.y, theta, lamda)
expected_theta_optimized = np.array([
1.27268726, 0.62557024, 1.18096643, -2.01919814, -0.91761464,
-1.43194196, 0.12375928, -0.36513066, -0.35703386, -0.17485797,
-1.4584374, -0.05129691, -0.6160397, -0.27464158, -1.19282551,
-0.24270352, -0.20570051, -0.04499796, -0.27782728, -0.29525866,
-0.45613268, -1.0437783, 0.02762813, -0.29265655, 0.01543383,
-0.32759297, -0.14389219, -0.92460139
])
expected_min_cost = 0.5290027422883413
np.testing.assert_almost_equal(theta_optimized,
expected_theta_optimized,
decimal=5)
self.assertAlmostEqual(min_cost, expected_min_cost, places=3)
