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array_create.py
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151 lines (128 loc) · 3.02 KB
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# _*_ coding:utf-8 _*_
"""
This file is about ways to create different types of np.array(),
like identity, diagonal matrix and so on.
"""
import numpy as np
def common_create():
"""
common way of creating array
:return: none
"""
arr1 = np.array([1, 2])
print arr1
# [1 2]
print arr1.shape
# (2L,)
arr2 = np.array([[1, 2], [3.1, 4.]])
print arr2
# [[ 1. 2. ]
# [ 3.1 4. ]]
print arr2.shape
# (2L, 2L)
arr3 = np.array([[1, 2], [3, 4]], dtype=complex)
print arr3
# [[ 1.+0.j 2.+0.j]
# [ 3.+0.j 4.+0.j]]
def about_shape():
print np.array([[1, 2, 3], [3, 4, 5]]).shape
# (2L, 3L)
print np.array([[1, 2, 3]]).shape
# (1L, 3L)
print np.array([1, 2, 3]).shape
# (3L,)
arr1 = np.array([[1, 2, 3], [3, 4, 5]])
arr2 = np.array([1, 2, 3])
print arr1 * arr2
# [[ 1 4 9]
# [ 3 8 15]]
# arr22 = np.array([[1], [2], [3]])
# print arr1 * arr22
print np.dot(arr1, arr2)
# [14 26]
def about_reshape():
arr = np.array([[1, 2, 3], [4, 5, 6], [10, 11, 12], [13, 14, 15]])
print arr.reshape(2, 6)
# [[ 1 2 3 4 5 6]
# [10 11 12 13 14 15]]
b = np.arange(1, 13).reshape((2, 2, 3))
print b
# [[[ 1 2 3]
# [ 4 5 6]]
#
# [[ 7 8 9]
# [10 11 12]]]
print b.reshape((2, 6))
# [[ 1 2 3 4 5 6]
# [ 7 8 9 10 11 12]]
def lst_2_array():
"""
list, tuple to array
:return: none
"""
tp = (1, 2, 3)
lst = [[1, 2], [3, 4]]
print np.array(lst).shape
# (2L, 2L)
print np.array(lst)
# [[1 2]
# [3 4]]
print np.asarray(lst)
# [[1 2]
# [3 4]]
print np.asarray(tp)
# [1 2 3]
def file_2np_arr():
"""txt file to numpy array"""
data_path = '../machine_learn/dataset/perception/dataset.txt'
x = np.loadtxt(data_path, dtype=float)
print x
# [ [ 1.1 2.2 0]
# [ 3.5 3.6 1]]
def empty_arr():
arr1 = np.arange(12).reshape(3, 4)
print arr1
# [[ 0 1 2 3]
# [ 4 5 6 7]
# [ 8 9 10 11]]
arr2 = np.empty(arr1.shape)
print arr2
# [[ 0. 0. 0. 0.]
# [ 0. 0. 0. 0.]
# [ 0. 0. 0. 0.]]
def test_ndim():
# Number of array dimensions.
x = np.array([1, 2, 3])
print x.ndim
# 1
y = np.array([[1, 2, 3], [4, 5, 6]])
print y.ndim
# 2
z = np.arange(12).reshape((2, 2, 3))
print z.ndim
# 3
if __name__ == '__main__':
test_ndim()
# empty_arr()
# file_2np_arr()
# broadcast_demo()
# about_shape()
# common_create()
# lst_2_array()
# about_reshape()
lst = [[1.1, 2.3, 3], [3, 4, 5]]
arr = np.array(lst)
# print arr[1, :]
# [ 3. 4. 5.]
# print arr[1]
# [ 3. 4. 5.]
# print arr[...]
a = np.array([[1.1, 2.3, 3]])
# print a[0].tolist()
# print "".join([str(i) + "-" for i in a[0].tolist()])
# 1.1-2.3-3.0-
# if 4 in [1, 3, 5]:
# print 'in it'
# print arr[0, 1], arr[0, 1].flatten.A[0]
# print np.array(lst)[:-1]
pass