def plt_1d(class1, class2):
prior1 = 0.5
prior2 = 0.5
mean1 = np.array([np.mean(class1[:, 0])])
mean2 = np.array([np.mean(class2[:, 0])])
# print mean1, mean2
cov1 = np.array([[np.cov([class1[:, 0]])]])
cov2 = np.array([[np.cov([class2[:, 0]])]])
# print cov1, cov2
discriminant_function1 = gdf.gen_discriminant_function_of_normal_distribution(mean1, cov1, prior1)
discriminant_function2 = gdf.gen_discriminant_function_of_normal_distribution(mean2, cov2, prior2)
# X = np.linspace(np.amin(class1[:, 0]), np.amax(class1[:, 0]), 200)
X = np.linspace(-100, 100, 100)
y1 = [discriminant_function1(np.array([x])) for x in X]
y2 = [discriminant_function2(np.array([x])) for x in X]
plt.plot(X, y1)
plt.plot(X, y2)
plt.show()
def classifier2(x):
discriminant_function1 = gdf.gen_discriminant_function_of_normal_distribution(mean1, cov1, prior1)
discriminant_function2 = gdf.gen_discriminant_function_of_normal_distribution(mean2, cov2, prior2)
if discriminant_function1(x) > discriminant_function2(x):
# print x, "class 1"
return 1
elif discriminant_function1(x) < discriminant_function2(x):
# print x, "class 2"
return 2
else:
# print x, "unsure"
return 0
def plt_2d(class1, class2):
prior1 = 0.5
prior2 = 0.5
mean1 = np.mean(class1[:, 0:2], axis=0)
mean2 = np.mean(class2[:, 0:2], axis=0)
# print mean1, mean2
cov1 = np.cov([class1[:, 0], class1[:, 1]])
cov2 = np.cov([class2[:, 0], class2[:, 1]])
# print cov1, cov2
discriminant_function1 = gdf.gen_discriminant_function_of_normal_distribution(mean1, cov1, prior1)
discriminant_function2 = gdf.gen_discriminant_function_of_normal_distribution(mean2, cov2, prior2)
x = np.linspace(-100, 100, 100)
y = np.linspace(-100, 100, 100)
X, Y = np.meshgrid(x, y)
z1 = [discriminant_function1(np.array([x, y])) for x, y in zip(X[0], Y[:, 0])]
z2 = [discriminant_function2(np.array([x, y])) for x, y in zip(X[0], Y[:, 0])]
figure = plt.figure()
axes = figure.add_subplot(111, projection='3d')
axes.plot_surface(X, Y, z1, cmap="Greys")
axes.plot_surface(X, Y, z2, cmap="Blues")
plt.show()
