anaconda的一些命令

    先安裝好TensorFlow。

1.安裝sklearn

    本安裝方式是在anaconda prompt上用命令來更新

（1）激活TensorFlow：activate tensorflow
（2）查看是否有sklearn：conda list
（3）安裝：conda install scikit-learn

    sklearn使用示例：

>>> import numpy as np
>>> from sklearn.model_selection import train_test_split
>>> X, y = np.arange(10).reshape((5, 2)), range(5)
>>> X
array([[0, 1],
       [2, 3],
       [4, 5],
       [6, 7],
       [8, 9]])
>>> list(y)
[0, 1, 2, 3, 4]

>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, test_size=0.33, random_state=42)
...
>>> X_train
array([[4, 5],
       [0, 1],
       [6, 7]])
>>> y_train
[2, 0, 3]
>>> X_test
array([[2, 3],
       [8, 9]])
>>> y_test
[1, 4]

>>> train_test_split(y, shuffle=False)
[[0, 1, 2], [3, 4]]

2.安裝matplotlib

    安裝與1相同

（1）激活TensorFlow：activate tensorflow
（2）查看是否有sklearn：conda list
（3）安裝：conda install matplotlib

matplotlib使用示例：

import matplotlib
import numpy
import scipy
import matplotlib.pyplot as plt
 
plt.plot([1,2,3])
plt.ylabel('some numbers')
plt.show()

import numpy as np
import matplotlib.pyplot as plt
 
X = np.arange(-5.0, 5.0, 0.1)
Y = np.arange(-5.0, 5.0, 0.1)
 
x, y = np.meshgrid(X, Y)
f = 17 * x ** 2 - 16 * np.abs(x) * y + 17 * y ** 2 - 225
 
fig = plt.figure()
cs = plt.contour(x, y, f, 0, colors = 'r')
plt.show()

import numpy as np
import matplotlib.pyplot as plt
  
N = 5
menMeans = (20, 35, 30, 35, 27)
menStd =   (2, 3, 4, 1, 2)
  
ind = np.arange(N)  # the x locations for the groups
width = 0.35        # the width of the bars
  
fig, ax = plt.subplots()
rects1 = ax.bar(ind, menMeans, width, color='r', yerr=menStd)
  
womenMeans = (25, 32, 34, 20, 25)
womenStd =   (3, 5, 2, 3, 3)
rects2 = ax.bar(ind+width, womenMeans, width, color='y', yerr=womenStd)
  
# add some
ax.set_ylabel('Scores')
ax.set_title('Scores by group and gender')
ax.set_xticks(ind+width)
ax.set_xticklabels( ('G1', 'G2', 'G3', 'G4', 'G5') )
  
ax.legend( (rects1[0], rects2[0]), ('Men', 'Women') )
  
def autolabel(rects):
    # attach some text labels
    for rect in rects:
        height = rect.get_height()
        ax.text(rect.get_x()+rect.get_width()/2., 1.05*height, '%d'%int(height),
                ha='center', va='bottom')
  
autolabel(rects1)
autolabel(rects2)
  
plt.show()