spkit.data.sinusoidal

spkit.data.sinusoidal(N=[100, 100], s=0.1, return_para=False, **kwargs)

Generate a 2-class dataset separated by a sinusoidal line

Sample a dataset from a dataset separated by a sinusoidal line

Parameters:

N: list or two int, default =[100,100]

• vector that fix the number of samples from each class

• example N = [100,100], 100 samples for each class

s: scalar, default=0.1

• standard deviation of the gaussian noise.

  New in version 0.0.9.7: Added to return parameters

return_para: bool, default=False

• if True, return the parameters

Returns:

X: 2d-array

• data matrix with a sample for each row

• shape (n, 2)

  Changed in version 0.0.9.7: shape is changed to (n, 2)

y: 1d-array

• vector with the labels

  Changed in version 0.0.9.7: shape is changed to (n, )

s: scalar

• parameter, if return_para=True

See also

gaussian, linear, moons, mclass_gauss, spiral, create_dataset

Examples

#sp.data.sinusoidal
import numpy as np
import matplotlib.pyplot as plt
import spkit as sp
np.random.seed(2)
X, y =  sp.data.sinusoidal(N =[100, 100],s=0.1)
np.random.seed(None)
plt.figure()
plt.plot(X[y==0,0],X[y==0,1],'o')
plt.plot(X[y==1,0],X[y==1,1],'o')
plt.xlabel('x1')
plt.ylabel('x2')
plt.title('Sinusodal Data')
plt.show()