import numpy as np
import matplotlib.pyplot as plt
np.random.seed(6)
import math

from sklearn.datasets.samples_generator import make_blobs

(X,y) =  make_blobs(n_samples=50,n_features=2,centers=2,cluster_std=1.05,random_state=40)
#we need to add 1 to X values (we can say its bias)
X1 = np.c_[np.ones((X.shape[0])),X]

plt.scatter(X1[:,1],X1[:,2],marker='o',c=y)
plt.axis([-5,10,-12,-1])
plt.show()
c9665ee27f63c37fc17d6bd7628f939c44cb64fa
<mask token>

<mask token>
np.random.seed(6)
<mask token>
plt.scatter(X1[:, 1], X1[:, 2], marker='o', c=y)
plt.axis([-5, 10, -12, -1])
plt.show()

<mask token>
np.random.seed(6)
<mask token>
X, y = make_blobs(n_samples=50, n_features=2, centers=2, cluster_std=1.05,
    random_state=40)
X1 = np.c_[np.ones(X.shape[0]), X]
plt.scatter(X1[:, 1], X1[:, 2], marker='o', c=y)
plt.axis([-5, 10, -12, -1])
plt.show()

import numpy as np
import matplotlib.pyplot as plt
np.random.seed(6)
import math
from sklearn.datasets.samples_generator import make_blobs
X, y = make_blobs(n_samples=50, n_features=2, centers=2, cluster_std=1.05,
    random_state=40)
X1 = np.c_[np.ones(X.shape[0]), X]
plt.scatter(X1[:, 1], X1[:, 2], marker='o', c=y)
plt.axis([-5, 10, -12, -1])
plt.show()

import numpy as np
import matplotlib.pyplot as plt
np.random.seed(6)
import math

from sklearn.datasets.samples_generator import make_blobs

(X,y) =  make_blobs(n_samples=50,n_features=2,centers=2,cluster_std=1.05,random_state=40)
#we need to add 1 to X values (we can say its bias)
X1 = np.c_[np.ones((X.shape[0])),X]

plt.scatter(X1[:,1],X1[:,2],marker='o',c=y)
plt.axis([-5,10,-12,-1])
plt.show()