spkit.data.mclass_gauss

spkit.data.mclass_gauss(N=100, nClasses=2, var=0.1, ShowPlot=False, return_para=False)

Generate Multi-class gaussian samples

Parameters:

N: int, deafult=100

• number of samples from each class

• example N = 100, 100 samples for each class

nClasses: int, default=0.5

• number of classes

var: scalar, str, default=0.1

• variance - noise

ShowPlot: bool, default=False

• Plot the data,

  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, )

See also

gaussian, linear, moons, sinusoidal, spiral, create_dataset

Examples

#sp.data.mclass_gauss
import numpy as np
import matplotlib.pyplot as plt
import spkit as sp
np.random.seed(4)
X, y =  sp.data.mclass_gauss(N=100,nClasses=3,var=0.3)
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.plot(X[y==2,0],X[y==2,1],'o')
plt.xlabel('x1')
plt.ylabel('x2')
plt.title('Multi-Class Gaussian Data')
plt.show()