import imatplotlib.pyplot as plt ,,, sklearn

>>> A = np.array([[ 1,0,0], [0,1,0], [1,1,0]])
>>> A
array([[1, 0, 0],
       [0, 1, 0],
       [1, 1, 0]])
>>> print A
[[1 0 0]
 [0 1 0]
 [1 1 0]]
>>> print A.T
[[1 0 1]
 [0 1 1]
 [0 

 

import matplotlib.pyplot as plt

import numpy as np


x =  np.linspace(0,1,101)
y =  -x * np.log2(x)-(1-x)*np.log2(1-x)
y[np.isnan(y)] = 0
plt.plot(x,y)
plt.show()

'''
y  = [        nan  0.08079314  0.14144054  0.19439186  0.24229219  0.28639696
  0.32744492  0.36592365  0.40217919  0.43646982  0.46899559  0.49991596
  0.52936087  0.55743819  0.58423881  0.6098403   0.63430955  0.65770478
  0.68007705  0.70147146  0.72192809  0.74148274  0.7601675   0.7780113
  0.79504028  0.81127812  0.82674637  0.84146464  0.85545081  0.86872125
  0.8812909   0.89317346  0.90438146  0.91492637  0.9248187   0.93406806
  0.94268319  0.95067209  0.95804202  0.96479955  0.97095059  0.97650047
  0.9814539   0.98581504  0.98958752  0.99277445  0.99537844  0.99740159
  0.99884554  0.99971144  1.          0.99971144  0.99884554  0.99740159
  0.99537844  0.99277445  0.98958752  0.98581504  0.9814539   0.97650047
  0.97095059  0.96479955  0.95804202  0.95067209  0.94268319  0.93406806
  0.9248187   0.91492637  0.90438146  0.89317346  0.8812909   0.86872125
  0.85545081  0.84146464  0.82674637  0.81127812  0.79504028  0.7780113
  0.7601675   0.74148274  0.72192809  0.70147146  0.68007705  0.65770478
  0.63430955  0.6098403   0.58423881  0.55743819  0.52936087  0.49991596
  0.46899559  0.43646982  0.40217919  0.36592365  0.32744492  0.28639696
  0.24229219  0.19439186  0.14144054  0.08079314         nan]
'''

 

import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = Axes3D(fig)

x = np.linspace(0.1,2,31)
y = np.linspace(-2,2,31)

X,Y = np.meshgrid(x,y)
Z = -np.log(X)+X*X+Y*Y/2-0.5

ax.plot_surface(X,Y,Z,rstride=1,cstride=1,cmap='rainbow')
plt.show()

 

import numpy as np
import sklearn.datasets as d
import matplotlib.pyplot as plt

reg_data = d.make_regression(100,1,1,1,1.0)
plt.plot(reg_data[0],reg_data[1])
plt.show()