from autograd import grad
grad (func) Differential
np.shape
reshape (-1,1) rearranged, (- 1) is automatically arranged in a row vecter, (- 1,1) is automatically aligned in one dimension 1
z.reshape(-1)
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16])
z.reshape(-1,1)
array([[ 1], [ 2], [ 3], [ 4], [ 5], [ 6], [ 7], [ 8], [ 9], [10], [11], [12], [13], [14], [15], [16]])
np.vstack (tup) use
In the vertical direction of the matrix are stacked.
Note: the arrays must have the same shape along all but the first axis in addition to a first external dimension, the dimensions of the matrix are stacked to be consistent.
Sample code:
import numpy as np
ARR1 np.array = ([1, 2, 3])
arr2 np.array = ([4, 5, 6])
res = np.vstack ((ARR1, ARR2))
np.hstack (tup)
in the horizontal direction of the array stacked.
Note:
TUP: Sequence of ndarrays
All MUST have have Arrays All But The Same Shape The SECOND Along Axis.
Sample code:
import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
res = np.hstack((arr1, arr2))
[1 2 3 4 5 6]
arr1 = np.array([[1, 2], [3, 4], [5, 6]])
arr2 = np.array([[7, 8], [9, 0], [0, 1]])
res = np.hstack((arr1, arr2))
[[1 2 7 8] [3 4 9 0] [5 6 0 1]]
np.random.shuffle (data) with the upset
np.random.normal(0,1,(256,10)) # normalize (mean ,std , shape)
Arbitrary integer (0,3, size = (256)) # 0 ~ 3 (not included 3) np.random.randint, size of
But also traverses the index through the elements
enumerate may also receive a second parameter, the starting value for the specified index
List1 = [ "this", "a", "an", "test"]
for index, item in enumerate(list1):
print index, item
>>>
This 0
1 is
2 a
3 Test
Supplement
the number of rows to be counted if the file can be written:
count = len (open (filepath, 'r'). readlines ())
This method is simple, but may be slower, even when the file is large can not work.
You can use enumerate ():
count = 0
for index, line in enumerate(open(filepath,'r'))：
count += 1
Seaborn Matplot is based on the implementation of higher order visualization API, you can make the drawing more convenient and easy. I think the relationship Matplot with Seaborn is like Tensorflow with Keras.