Python Numpy Tutorial
Python Numpy Tutorial
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These notes accompany the Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition.

  • Numpy有Matlab版。

Python

  • Python is a high-level, dynamically typed multiparadigm programming language.

Basic data types

  • Numbers
    • type()显示字符类型
    • 没有 ++ 和 –
  • Booleans
    • 使用 and or not而不是&& ! 等
  • Strings
    • 可以使用单引号或双引号
    • len()显示字符串长度
    • 使用 + 用于字符串拼接
    • hw12 = '%s %s %d' % (hello, world, 12) 字符串格式化

    • String类型有很多方法

      s = "hello"
print s.capitalize() # Capitalize a string; prints "Hello"
print s.upper() # Convert a string to uppercase; prints "HELLO"
print s.rjust(7) # Right-justify a string, padding with spaces; prints
print s.center(7) # Center a string, padding with spaces; prints " hello
print s.replace('l', '(ell)') # Replace all instances of one substring with
# prints "he(ell)(ell)o"
print ' world '.strip() # Strip leading and trailing whitespace; pr

Containers

  • 包含Lists，Dictionaries，settings，Tuples 几种类型

Lists

  • xs=[3,1,2]创建list
  • list可以包含不同类型的元素
  • list可以出栈和进栈操作

  • Slicing

    nums = range(5) # range is a built-in function that creates a list of int
print nums # Prints "[0, 1, 2, 3, 4]"
print nums[2:4] # Get a slice from index 2 to 4 (exclusive); prints "[2,3]" 不包括第4个
print nums[2:] # Get a slice from index 2 to the end; prints "[2, 3, 4]
print nums[:2] # Get a slice from the start to index 2 (exclusive); prints "[0, 1]"
print nums[:] # Get a slice of the whole list; prints ["0, 1, 2, 3, 4]
print nums[:-1] # Slice indices can be negative; prints ["0, 1, 2, 3]"
nums[2:4] = [8, 9] # Assign a new sublist to a slice
print nums # Prints "[0, 1, 8, 9, 4]"
  • Loops
    • for ... in ... ：
    • 枚举遍历列表for ... in enumerate(...)：

  • List comprehensions:变换类型

    nums = [0, 1, 2, 3, 4]
squares = []
for x in nums:
  squares.append(x ** 2)
# or
squares = [x ** 2 for x in nums]
# 包含条件
even_squares = [x ** 2 for x in nums if x % 2 == 0]
print squares # Prints [0, 1, 4, 9, 16]

Dictionaries

  • dictionary保存一对(key, value).

  • Loops:

    d = {'person': 2, 'cat': 4, 'spider': 8}
for animal in d:
  legs = d[animal]
  print 'A %s has %d legs' % (animal, legs)
# Prints "A person has 2 legs", "A spider has 8 legs",
# "A cat has 4 legs

# or use `iteritems`
d = {'person': 2, 'cat': 4, 'spider': 8}
for animal, legs in d.iteritems():
  print 'A %s has %d legs' % (animal, legs)
# Prints "A person has 2 legs", "A spider has 8 legs",
# "A cat has 4 legs

Sets

  • a set is an unordered collection of distinct elements.无序的

Tuples

  • A tuple is an (immutable不变的) ordered list of values.

[Functions]https://docs.python.org/2/tutorial/controlflow.html#defining-functions

  • def 定义函数

Classes

  • class ...():

Numpy

  • Numpy is the core library for scientiAc computing in Python.
  • Numpy的安装时需要源文件编译，若使用Anaconda则可以直接使用~

Arrays

  • A numpy array is a grid of values.相同类型
  • The number of dimensions is the rank of the array;
  • the shape of an array is a tuple of integers giving the size of the array along each dimension.
  • import numpy as 导入包

Array indexing

  • Slicing
    • b = a[:2, 1:3] 前两行，第2，3列。
    • 索引从0开始
    • 序号不包含后一个
    • 矩阵处理有点像matlab
  • Integer array indexng 数组索引
  • Boolean array indexing 判断数组条件

Datatypes

  • Every numpy array is a grid of elements of the same type.
  • 可以强制定义类型

Array math

  • 基本的数学操作是可以在array上使用的，也可以使用方法（函数）
    • + np.add()
    • - np.subtract()
    • * np.multiply() 元素乘
    • / np.divide()
  • dot函数用来矩阵相乘，两种形式v.dot(w), np.dot(v,w)
  • np.sum 函数计算和。np.sum(x, axis=0) 列相加，axis=1行相加。没有则是全部相加。
  • 更多函数见 http://docs.scipy.org/doc/numpy/reference/routines.math.html
  • x.T转置矩阵x。 rank=1的数组不做任何变化。
  • 更多对array的操作见 http://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html

Broadcasting

  • np.tile(v,(4,1)) stack 4 copies of v on top of each other

  • Numpy broadcasting allows us to perform this computation without actually creating multiple copies of v.

    import numpy as np
# We will add the vector v to each row of the matrix x,
# storing the result in the matrix y
x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
v = np.array([1, 0, 1])
y = x + v # Add v to each row of x using broadcasting
print y # Prints "[[ 2 2 4]
# [ 5 5 7]
# [ 8 8 10]
# [11 11 13]]"

# y = x + v 此公式依然有效，当x是4*3矩阵而v是3*1矩阵
  • 广播(broadcasting)两个array遵循以下规则
    1. 如果array没有相同的rank，可以用1填充地址array知道两个array有相同的长度
    2. 如果两个array在同一维度有相同的大小或者其中一个array在该维度为1，则可以直接进行操作
    3. 如果arrays在所有维度都兼容可以一起广播
    4. 广播后的每个array等于最大的那个array
    5. 在任意维度，当其中一个大小为1而其他array大于1，那么前一个array填充为相同大小
      • 详见 http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html
      • 或 http://scipy.github.io/old-wiki/pages/EricsBroadcastingDoc
import numpy as np
# Compute outer product of vectors
v = np.array([1,2,3]) # v has shape (3,)
w = np.array([4,5]) # w has shape (2,)
# To compute an outer product, we first reshape v to be a column
# vector of shape (3, 1); we can then broadcast it against w to yield
# an output of shape (3, 2), which is the outer product of v and w:
# [[ 4 5]
# [ 8 10]
# [12 15]]
print np.reshape(v, (3, 1)) * w
# Add a vector to each row of a matrix
x = np.array([[1,2,3], [4,5,6]])
# x has shape (2, 3) and v has shape (3,) so they broadcast to (2, 3),
# giving the following matrix:
# [[2 4 6]
# [5 7 9]]
print x + v
# Add a vector to each column of a matrix
# x has shape (2, 3) and w has shape (2,).
# If we transpose x then it has shape (3, 2) and can be broadcast
# against w to yield a result of shape (3, 2); transposing this result
# yields the final result of shape (2, 3) which is the matrix x with
# the vector w added to each column. Gives the following matrix:
# [[ 5 6 7]
# [ 9 10 11]]
print (x.T + w).T
# Another solution is to reshape w to be a row vector of shape (2, 1);
# we can then broadcast it directly against x to produce the same
# output.
print x + np.reshape(w, (2, 1))
# Multiply a matrix by a constant:
# x has shape (2, 3). Numpy treats scalars as arrays of shape ();
# these can be broadcast together to shape (2, 3), producing the
# following array:
# [[ 2 4 6]
# [ 8 10 12]]
print x * 2

SciPy

  • SciPy builds on Numpy’s function, and provides a large number of functions that operate on numpy arrays and are useful for different types of scientific and engineering applications.

Image operations

  • SciPy可以导入一个image到numpy arrays，并且可以write,resize等等。
  • from scipy.misc import imread, imsave, imresize
  • imread读入图片
  • imresizeresize图片
  • imsave将图片保存到本地

MATLAB Ales

  • scipy.io.loadmat和scipy.io.savemat可以读写Matlab文件

Distance between points

  • scipy.spatial.distance.pdist计算一个给定数据集中所有点的距离
  • http://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.pdist.html
  • scipy.spatial.distance.cdist计算两个数据集之间的距离
  • http://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html

Matplotlib

  • 画图的库，与Matlab相似

Plotting

  • import matplotlib.pyplot as plt
  • plt.plot 作图
  • plt.show 显示图像

Subplots

Images

  • imshow显示图像
  • 有些图像不是uint8格式可能会导致图像效果不好，这时，可以强制给定uint8来显示。plt.imshow(np.uint8(img_tinted))

Finished at 2016-09-06 18:18 Sydney

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