def test_rotate_moons5(self):
x = np.array([[1.0,1.0],[1.0,1.0]])
theta = math.pi/10
theta1 = math.pi/12
x = my_fn.rotate_moons(theta1, my_fn.rotate_moons(theta, x))
x_f = my_fn.rotate_moons(theta+theta1, x)
self.assertAlmostEqual(x_f.all(), x.all())
def test_rotate_moons6(self):
x = np.array([[1.0,1.0,1.0],[1.0,1.0,1.0]])
with self.assertRaises(TypeError):
my_fn.rotate_moons(0.0, x)
def test_rotate_moons2(self):
x = np.array([[1.0,1.0],[1.0,1.0]])
theta = math.pi/10
x_f = my_fn.rotate_moons(-theta, my_fn.rotate_moons(theta, x))
self.assertAlmostEqual(x_f.all(), x.all())
def test_rotate_moons4(self):
x = np.array([[1.0,1.0],[1.0,1.0]])
x_f = my_fn.rotate_moons(2*math.pi, x)
self.assertAlmostEqual(x_f.all(), x.all())
import matplotlib.pyplot as plt
from sklearn import datasets
import glob
import sys
import math
sys.path.append('../')
import common_fn as my_fn
import tensorflow as tf
#%%
(x, y) = datasets.make_moons(n_samples=840,
shuffle=True,
noise=0.1,
random_state=42)
x_tr = my_fn.translate_moons(0.5, 0.5, x.copy())
x_rot = my_fn.rotate_moons(math.pi / 5, x_tr.copy())
y = y * 0 + 20
y_tr = y.copy() * 0 + 50
y_rot = y.copy() * 0 + 100
x = np.concatenate((x, x_tr), axis=0)
y = np.concatenate((y, y_tr), axis=0)
x = np.concatenate((x, x_rot), axis=0)
y = np.concatenate((y, y_rot), axis=0)
cm = plt.cm.get_cmap('RdYlBu')
sns.set_style('whitegrid')
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(18, 9))
ax.set_xlabel('x')
ax.set_ylabel('Y')
# Plot the samples
plt.scatter(x[:, 0], x[:, 1], c=y, cmap=cm, marker=".")