spkit.data.linear

spkit.data.linear(N=[100, 100], m='random', b='random', s=0.1, return_para=False, **kwargs)

Generate a 2-class dataset separated by a linear boundary

Generating samples using:

\[y = m*x = b \]
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

m: scalar, str, default=’random’

  • slope of the separating line.

b: scalar, str, default=’random’

  • bias of the line. Default is random.

s: float,default= 0.1

  • standard deviation of the gaussian noise. Default is 0.1

    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, )

(m, b, s): parameters

  • if return_para is True

See also

gaussian, moons, sinusoidal, mclass_gauss, spiral, create_dataset

Examples

#sp.data.linear
import numpy as np
import matplotlib.pyplot as plt
import spkit as sp
np.random.seed(3)
X, y =  sp.data.linear(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('Linear Class Data')
plt.show()
../../_images/spkit-data-linear-1.png