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#%%
import numpy as np
def make_data(example_nr, n = 200, noise = 1):
'''
Generate data for training a simple neural network.
Arguments:
example_nr: a number 1 to 3 for each example.
n: number of points in each class set.
noise: noise level, best between 0.5 and 2.
Returns:
X: 2 x 2n array of points (there are n points in each class).
T: 2 x 2n target values.
x: grid points for testing the neural network.
dim: size of the area covered by the grid points.
Authors: Vedrana Andersen Dahl and Anders Bjorholm Dahl - 25/3-2020
vand@dtu.dk, abda@dtu.dk
'''
rg = np.random.default_rng()
dim = (100, 100)
QX, QY = np.meshgrid(range(0, dim[0]), range(0, dim[1]))
x_grid = np.c_[np.ravel(QX), np.ravel(QY)]
# Targets: first half class 0, second half class 1
T = np.vstack((np.tile([True, False], (n, 1)),
np.tile([False, True], (n, 1))))
if example_nr == 1 : # two separated clusters
X = np.vstack((np.tile([30., 30.], (n, 1)),
np.tile([70., 70.], (n, 1))))
X += rg.normal(size=X.shape, scale=10*noise) # add noise
elif example_nr == 2 : # concentric clusters
rand_ang = 2 * np.pi * rg.uniform(size=n)
X = np.vstack((30 * np.array([np.cos(rand_ang), np.sin(rand_ang)]).T,
np.tile([0., 0.], (n, 1))))
X += [50, 50] # center
X += rg.normal(size=X.shape, scale=5*noise)# add noise
elif example_nr == 3 : # 2x2 checkerboard
n1 = n//2
n2 = n//2 + n%2 # if n is odd n2 will have 1 element more
X = np.vstack((np.tile([30., 30.], (n1, 1)),
np.tile([70., 70.], (n2, 1)),
np.tile([30. ,70.], (n1, 1)),
np.tile([70., 30.], (n2, 1))))
X += rg.normal(size=X.shape, scale=10*noise) # add noise
else:
print('No data returned - example_nr must be 1, 2, or 3')
o = rg.permutation(range(2*n))
return X[o].T, T[o].T, x_grid.T, dim
#%% Test of the data generation
if __name__ == "__main__":
#%%
import matplotlib.pyplot as plt
n = 1000
noise = 1
fig, ax = plt.subplots(1, 3)
for i, a in enumerate(ax):
example_nr = i + 1
X, T, x_grid, dim = make_data(example_nr, n, noise)
a.scatter(X[0][T[0]], X[1][T[0]], c='r', alpha=0.3, s=15)
a.scatter(X[0][T[1]], X[1][T[1]], c='g', alpha=0.3, s=15)
a.set_aspect('equal', 'box')
a.set_title(f'Example {i} data')
plt.show()
#%% Before training, you should make data zero mean
c = np.mean(X, axis=1, keepdims=True)
X_c = X - c
fig, ax = plt.subplots(1,1)
ax.scatter(X_c[0][T[0]], X_c[1][T[0]], c='r', alpha=0.3, s=15)
ax.scatter(X_c[0][T[1]], X_c[1][T[1]], c='g', alpha=0.3, s=15)
ax.set_aspect('equal', 'box')
plt.title('Zero-mean data')
plt.show()
# %%