学习Python


#数据类型
#在 Python 3.0之前是“语句”,在 Python 3.0中是“函数”,因此print需要加括号
x=3

print (type(x))

print (x)

print (x+12)

print(x**2)

print(12*19)

t = True
f = False
print (type(t)) # Prints "<type 'bool'>"
print (t and f) # Logical AND; prints "False"
print (t or f)  # Logical OR; prints "True"
print (not t)   # Logical NOT; prints "False"
print (t != f)  # Logical XOR; prints "True" 
print '\n\n'


#字符串
hello = 'hello'   # String literals can use single quotes
world = "world"   # or double quotes; it does not matter.
print (hello)       # Prints "hello"
print (len(hello))  # String length; prints "5"
hw = hello + ' ' + world  # String concatenation
print (hw)  # prints "hello world"
hw12 = '%s %s %d' % (hello, world, 12)  # sprintf style string formatting
print (hw12)  # prints "hello world 12"

#字符串函数
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 "  hello"
print (s.center(7))     # Center a string, padding with spaces; prints " hello "
print (s.replace('l', '(ell)'))  # Replace all instances of one substring with another;
                               # prints "he(ell)(ell)o"
print ('  world '.strip())  # Strip leading and trailing whitespace; prints "world"
                            #剥离前置后置空格
print '\n\n'



#list
xs = [3, 1, 2]   # Create a list
print(xs)
print (xs, xs[2] ) # Prints "[3, 1, 2] 2"
print (xs[-3])     # Negative indices count from the end of the list; prints "2"
xs[2] = 'foo'    # Lists can contain elements of different types
print (xs)         # Prints "[3, 1, 'foo']"
xs.append('bar') # Add a new element to the end of the list
print (xs)         # Prints "[3, 1, 'foo', 'bar']"
x = xs.pop()     # Remove and return the last element of the list
print (x, xs )     # Prints "bar [3, 1, 'foo']"

#Slicing
nums = range(5)    # range is a built-in(内置) function that creates a list of integers
print (nums[-4])         # Prints "[0, 1, 2, 3, 4]"
print (nums[2:4])    # Get a slice from index 2 to 4 (exclusive); prints "[2, 3]"
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, 2]"
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]"
print '\n\n'


#loops
animals = ['cat', 'dog', 'monkey']
for animal in animals:
    print (animal)
# Prints "cat", "dog", "monkey", each on its own line.

animals = ['cat', 'dog', 'monkey']
for idx, animal in enumerate(animals):
    print ('#%d: %s' % (idx + 1, animal))
# Prints "#1: cat", "#2: dog", "#3: monkey", each on its own line
print '\n\n'   


#list comprehension(列表推导式):
nums = [0, 1, 2, 3, 4]
squares = []
for x in nums:
    squares.append(x ** 2)
print (squares)   # Prints [0, 1, 4, 9, 16]


nums = [0, 1, 2, 3, 4]
squares = [x ** 2 for x in nums]
print (squares)   # Prints [0, 1, 4, 9, 16]


nums = [0, 1, 2, 3, 4]
even_squares = [x ** 2 for x in nums if x % 2 == 0]
print (even_squares)  # Prints "[0, 4, 16]"
print '\n\n'


#Dictionaries
d = {'cat': 'cute', 'dog': 'furry'}  # Create a new dictionary with some data
print (d['cat'])       # Get an entry from a dictionary; prints "cute"
print ('cat' in d)     # Check if a dictionary has a given key; prints "True"
d['fish'] = 'wet'    # Set an entry in a dictionary
print (d['fish'])      # Prints "wet"
# print d['monkey']  # KeyError: 'monkey' not a key of d
print (d.get('monkey', 'N/A'))  # Get an element with a default; prints "N/A"
print (d.get('fish', 'N/A') )   # Get an element with a default; prints "wet"
del d['fish']        # Remove an element from a dictionary
print (d.get('fish', 'N/A')) # "fish" is no longer a key; prints "N/A"
print '\n\n'


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"
print '\n\n'    


#Dictionary comprehensions字典推导式: 
nums = [0, 1, 2, 3, 4]
even_num_to_square = {x: x ** 2 for x in nums if x % 2 == 0}
print even_num_to_square  # Prints "{0: 0, 2: 4, 4: 16}"
print '\n\n'

#Sets
animals = {'cat', 'dog'}
print 'cat' in animals   # Check if an element is in a set; prints "True"
print 'fish' in animals  # prints "False"
animals.add('fish')      # Add an element to a set
print 'fish' in animals  # Prints "True"
print len(animals)       # Number of elements in a set; prints "3"
animals.add('cat')       # Adding an element that is already in the set does nothing
print len(animals)       # Prints "3"
animals.remove('cat')    # Remove an element from a set
print len(animals)       # Prints "2"
print '\n'

#Set Loops:集合中的元素无序,不能确定访问的先后顺序
animals = {'cat', 'dog', 'fish'}
for idx, animal in enumerate(animals):
    print '#%d: %s' % (idx + 1, animal)
# Prints "#1: fish", "#2: dog", "#3: cat"
print '\n'

#Set comprehensions(集合推导式):
from math import sqrt
nums = {int(sqrt(x)) for x in range(30)}
print nums  # Prints "set([0, 1, 2, 3, 4, 5])"
print '\n\n'

#Tuples(元组)
d = {(x, x + 1): x for x in range(10)}  # Create a dictionary with tuple keys
t = (5, 6)       # Create a tuple
print type(t)    # Prints "<type 'tuple'>"
print d
print d[t]       # Prints "5"
print d[(1, 2)]  # Prints "1"
print '\n\n'

#Functions
def sign(x):
    if x > 0:
        return 'positive'
    elif x < 0:
        return 'negative'
    else:
        return 'zero'

for x in [-1, 0, 1]:
    print sign(x)
# Prints "negative", "zero", "positive"
print '\n'   

def hello(name, loud=False):
    if loud:
        print 'HELLO, %s!' % name.upper()
    else:
        print 'Hello, %s' % name

hello('Bob') # Prints "Hello, Bob"
hello('Fred', loud=True)  # Prints "HELLO, FRED!"
print '\n\n'

#Classes
class Greeter(object):

    # Constructor
    def __init__(self, name):
        self.name = name  # Create an instance variable

    # Instance method
    def greet(self, loud=False):
        if loud:
            print 'HELLO, %s!' % self.name.upper()
        else:
            print 'Hello, %s' % self.name

g = Greeter('Fred')  # Construct an instance of the Greeter class
g.greet()            # Call an instance method; prints "Hello, Fred"
g.greet(loud=True)   # Call an instance method; prints "HELLO, FRED!"
print '\n\n'

#Numpy
#Arrays
import numpy as np

a = np.array([1, 2, 3])  # Create a rank 1 array
print type(a)            # Prints "<type 'numpy.ndarray'>"
print a.shape            # Prints "(3,)"
print a[0], a[1], a[2]   # Prints "1 2 3"
a[0] = 5                 # Change an element of the array
print a                  # Prints "[5, 2, 3]"

b = np.array([[1,2,3],[4,5,6]])   # Create a rank 2 array
print b.shape                     # Prints "(2, 3)"
print b[0, 0], b[0, 1], b[1, 0]   # Prints "1 2 4"
print '\n'

import numpy as np

a = np.zeros((2,2))  # Create an array of all zeros
print a              # Prints "[[ 0.  0.]
                     #          [ 0.  0.]]"

b = np.ones((1,2))   # Create an array of all ones
print b              # Prints "[[ 1.  1.]]"

c = np.full((2,2), 7) # Create a constant array
print c               # Prints "[[ 7.  7.]
                      #          [ 7.  7.]]"

d = np.eye(2)        # Create a 2x2 identity matrix(单位矩阵)
print d              # Prints "[[ 1.  0.]
                     #          [ 0.  1.]]"

e = np.random.random((2,2)) # Create an array filled with random values
print e                     # Might print "[[ 0.91940167  0.08143941]
                            #               [ 0.68744134  0.87236687]]"
print '\n\n'

#Array indexing
import numpy as np

# Create the following rank 2 array with shape (3, 4)
# [[ 1  2  3  4]
#  [ 5  6  7  8]
#  [ 9 10 11 12]]
a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
print a
# Use slicing to pull out the subarray consisting of the first 2 rows
# and columns 1 and 2; b is the following array of shape (2, 2):
# [[2 3]
#  [6 7]]
b = a[:2, 1:3]
print b #b数组只是a数组的引用
# A slice of an array is a view into the same data, so modifying it
# will modify the original array.
print a[0, 1]   # Prints "2"
b[0, 0] = 77    # b[0, 0] is the same piece of data as a[0, 1]
print a[0, 1]   # Prints "77"
print '\n\n'



import numpy as np

# Create the following rank 2 array with shape (3, 4)
# [[ 1  2  3  4]
#  [ 5  6  7  8]
#  [ 9 10 11 12]]
a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])

# Two ways of accessing the data in the middle row of the array.
# Mixing integer indexing with slices yields an array of lower rank,
# while using only slices yields an array of the same rank as the
# original array:
row_r1 = a[1, :]    # Rank 1 view of the second row of a  
row_r2 = a[1:2, :]  # Rank 2 view of the second row of a
print row_r1, row_r1.shape  # Prints "[5 6 7 8] (4,)"
print row_r2, row_r2.shape  # Prints "[[5 6 7 8]] (1, 4)"

# We can make the same distinction when accessing columns of an array:
col_r1 = a[:, 1]
col_r2 = a[:, 1:2]
print col_r1, col_r1.shape  # Prints "[ 2  6 10] (3,)"
print col_r2, col_r2.shape  # Prints "[[ 2]
                            #          [ 6]
                            #          [10]] (3, 1)"
print '\n\n\n\n'

import numpy as np

a = np.array([[1,2], [3, 4], [5, 6]])

# An example of integer array indexing.
# The returned array will have shape (3,) and 
print a[[0, 1, 2], [0, 1, 0]]  # Prints "[1 4 5]"

# The above example of integer array indexing is equivalent to this:
print np.array([a[0, 0], a[1, 1], a[2, 0]])  # Prints "[1 4 5]"

# When using integer array indexing, you can reuse the same
# element from the source array:
print a[[0, 0], [1, 1]]  # Prints "[2 2]"

# Equivalent to the previous integer array indexing example
print np.array([a[0, 1], a[0, 1]])  # Prints "[2 2]"


# Create a new array from which we will select elements
a = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])

print a  # prints "array([[ 1,  2,  3],
         #                [ 4,  5,  6],
         #                [ 7,  8,  9],
         #                [10, 11, 12]])"

# Create an array of indices
b = np.array([0, 2, 0, 1])

# Select one element from each row of a using the indices in b
print a[np.arange(4), b]  # Prints "[ 1  6  7 11]"

# Mutate one element from each row of a using the indices in b
a[np.arange(4), b] += 10

print a  # prints "array([[11,  2,  3],
         #                [ 4,  5, 16],
         #                [17,  8,  9],
         #                [10, 21, 12]])


a = np.array([[1,2], [3, 4], [5, 6]])

bool_idx = (a > 2)  # Find the elements of a that are bigger than 2;
                    # this returns a numpy array of Booleans of the same
                    # shape as a, where each slot of bool_idx tells
                    # whether that element of a is > 2.

print bool_idx      # Prints "[[False False]
                    #          [ True  True]
                    #          [ True  True]]"

# We use boolean array indexing to construct a rank 1 array
# consisting of the elements of a corresponding to the True values
# of bool_idx
print a[bool_idx]  # Prints "[3 4 5 6]"

# We can do all of the above in a single concise statement:
print a[a > 2]     # Prints "[3 4 5 6]"
print '\n\n'


#Datatypes
import numpy as np

x = np.array([1, 2])  # Let numpy choose the datatype
print x.dtype         # Prints "int64"

x = np.array([1.0, 2.0])  # Let numpy choose the datatype
print x.dtype             # Prints "float64"

x = np.array([1, 2], dtype=np.int64)  # Force a particular datatype
print x.dtype                         # Prints "int64"


#Array math
import numpy as np

x = np.array([[1,2],[3,4]], dtype=np.float64)
y = np.array([[5,6],[7,8]], dtype=np.float64)

# Elementwise sum; both produce the array
# [[ 6.0  8.0]
#  [10.0 12.0]]
print x + y
print np.add(x, y)

# Elementwise difference; both produce the array
# [[-4.0 -4.0]
#  [-4.0 -4.0]]
print x - y
print np.subtract(x, y)

# Elementwise product; both produce the array
# [[ 5.0 12.0]
#  [21.0 32.0]]
print x * y
print np.multiply(x, y)

# Elementwise division; both produce the array
# [[ 0.2         0.33333333]
#  [ 0.42857143  0.5       ]]
print x / y
print np.divide(x, y)

# Elementwise square root; produces the array
# [[ 1.          1.41421356]
#  [ 1.73205081  2.        ]]
print np.sqrt(x)


import numpy as np

x = np.array([[1,2],[3,4]])
y = np.array([[5,6],[7,8]])

v = np.array([9,10])
w = np.array([11, 12])

# Inner product of vectors; both produce 219
print v.dot(w)
print np.dot(v, w)

# Matrix / vector product; both produce the rank 1 array [29 67]
print x.dot(v)
print np.dot(x, v)

# Matrix / matrix product; both produce the rank 2 array
# [[19 22]
#  [43 50]]
print x.dot(y)
print np.dot(x, y)



import numpy as np

x = np.array([[1,2],[3,4]])

print np.sum(x)  # Compute sum of all elements; prints "10"
print np.sum(x, axis=0)  # Compute sum of each column; prints "[4 6]"
print np.sum(x, axis=1)  # Compute sum of each row; prints "[3 7]"



import numpy as np

x = np.array([[1,2], [3,4]])
print x    # Prints "[[1 2]
           #          [3 4]]"
print x.T  # Prints "[[1 3]
           #          [2 4]]"

# Note that taking the transpose of a rank 1 array does nothing:
v = np.array([1,2,3])
print v    # Prints "[1 2 3]"
print v.T  # Prints "[1 2 3]"

#Broadcasting
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 = np.empty_like(x)   # Create an empty matrix with the same shape as x

# Add the vector v to each row of the matrix x with an explicit loop
for i in range(4):
    y[i, :] = x[i, :] + v

# Now y is the following
# [[ 2  2  4]
#  [ 5  5  7]
#  [ 8  8 10]
#  [11 11 13]]
print y


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])
vv = np.tile(v, (4, 1))  # Stack 4 copies of v on top of each other
print vv                 # Prints "[[1 0 1]
                         #          [1 0 1]
                         #          [1 0 1]
                         #          [1 0 1]]"
y = x + vv  # Add x and vv elementwise
print y  # Prints "[[ 2  2  4
         #          [ 5  5  7]
         #          [ 8  8 10]
         #          [11 11 13]]"


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]]"


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
print '\n\n'

#Numpy Documentation
#Image operations
from scipy.misc import imread, imsave, imresize

# Read an JPEG image into a numpy array
img = imread('cat.jpg')
print img.dtype, img.shape  # Prints "uint8 (400, 248, 3)"

# We can tint the image by scaling each of the color channels
# by a different scalar constant. The image has shape (400, 248, 3);
# we multiply it by the array [1, 0.95, 0.9] of shape (3,);
# numpy broadcasting means that this leaves the red channel unchanged,
# and multiplies the green and blue channels by 0.95 and 0.9
# respectively.
img_tinted = img * [1, 0.95, 0.9]

# Resize the tinted image to be 300 by 300 pixels.
img_tinted = imresize(img_tinted, (300, 300))

# Write the tinted image back to disk
imsave('cat_tinted.jpg', img_tinted)
print '\n\n'

#MATLAB files
#Distance between points
import numpy as np
from scipy.spatial.distance import pdist, squareform

# Create the following array where each row is a point in 2D space:
# [[0 1]
#  [1 0]
#  [2 0]]
x = np.array([[0, 1], [1, 0], [2, 0]])
print x

# Compute the Euclidean distance between all rows of x.
# d[i, j] is the Euclidean distance between x[i, :] and x[j, :],
# and d is the following array:
# [[ 0.          1.41421356  2.23606798]
#  [ 1.41421356  0.          1.        ]
#  [ 2.23606798  1.          0.        ]]
d = squareform(pdist(x, 'euclidean'))
print d

#Matplotlib
#Plotting
import numpy as np
import matplotlib.pyplot as plt

# Compute the x and y coordinates for points on a sine curve
x = np.arange(0, 3 * np.pi, 0.1)
y = np.sin(x)

# Plot the points using matplotlib
plt.plot(x, y)
plt.show()  # You must call plt.show() to make graphics appear.

import numpy as np
import matplotlib.pyplot as plt

# Compute the x and y coordinates for points on sine and cosine curves
x = np.arange(0, 3 * np.pi, 0.1)
y_sin = np.sin(x)
y_cos = np.cos(x)

# Plot the points using matplotlib
plt.plot(x, y_sin)
plt.plot(x, y_cos)
plt.xlabel('x axis label')
plt.ylabel('y axis label')
plt.title('Sine and Cosine')
plt.legend(['Sine', 'Cosine'])
plt.show()


#Subplots
import numpy as np
import matplotlib.pyplot as plt

# Compute the x and y coordinates for points on sine and cosine curves
x = np.arange(0, 3 * np.pi, 0.1)
y_sin = np.sin(x)
y_cos = np.cos(x)

# Set up a subplot grid that has height 2 and width 1,
# and set the first such subplot as active.
plt.subplot(2, 1, 1)

# Make the first plot
plt.plot(x, y_sin)
plt.title('Sine')

# Set the second subplot as active, and make the second plot.
plt.subplot(2, 1, 2)
plt.plot(x, y_cos)
plt.title('Cosine')

# Show the figure.
plt.show()

print'\n\n'


#Images
import numpy as np
from scipy.misc import imread, imresize
import matplotlib.pyplot as plt

img = imread('cat.jpg')
img_tinted = img * [1, 0.95, 0.9]

# Show the original image
plt.subplot(1, 2, 1)
plt.imshow(img)

# Show the tinted image
plt.subplot(1, 2, 2)

# A slight gotcha with imshow is that it might give strange results
# if presented with data that is not uint8. To work around this, we
# explicitly cast the image to uint8 before displaying it.
plt.imshow(np.uint8(img_tinted))
plt.show()

运行结果:

<type 'int'>
3
15
9
228
<type 'bool'>
False
True
False
True



hello
5
hello world
hello world 12
Hello
HELLO
  hello
 hello 
he(ell)(ell)o
world



[3, 1, 2]
([3, 1, 2], 2)
3
[3, 1, 'foo']
[3, 1, 'foo', 'bar']
('bar', [3, 1, 'foo'])
1
[2, 3]
[2, 3, 4]
[0, 1]
[0, 1, 2, 3, 4]
[0, 1, 2, 3]
[0, 1, 8, 9, 4]



cat
dog
monkey
#1: cat
#2: dog
#3: monkey



[0, 1, 4, 9, 16]
[0, 1, 4, 9, 16]
[0, 4, 16]



cute
True
wet
N/A
wet
N/A



A person has 2 legs
A spider has 8 legs
A cat has 4 legs



{0: 0, 2: 4, 4: 16}



True
False
True
3
3
2


#1: fish
#2: dog
#3: cat


set([0, 1, 2, 3, 4, 5])



<type 'tuple'>
{(0, 1): 0, (1, 2): 1, (6, 7): 6, (5, 6): 5, (7, 8): 7, (8, 9): 8, (4, 5): 4, (2, 3): 2, (9, 10): 9, (3, 4): 3}
5
1



negative
zero
positive


Hello, Bob
HELLO, FRED!



Hello, Fred
HELLO, FRED!



<type 'numpy.ndarray'>
(3L,)
1 2 3
[5 2 3]
(2L, 3L)
1 2 4


[[ 0.  0.]
 [ 0.  0.]]
[[ 1.  1.]]
[[ 7.  7.]
 [ 7.  7.]]
[[ 1.  0.]
 [ 0.  1.]]
[[ 0.17549336  0.2399925 ]
 [ 0.5394008   0.44558024]]



[[ 1  2  3  4]
 [ 5  6  7  8]
 [ 9 10 11 12]]
[[2 3]
 [6 7]]
2
77



[5 6 7 8] (4L,)
[[5 6 7 8]] (1L, 4L)
[ 2  6 10] (3L,)
[[ 2]
 [ 6]
 [10]] (3L, 1L)





[1 4 5]
[1 4 5]
[2 2]
[2 2]
[[ 1  2  3]
 [ 4  5  6]
 [ 7  8  9]
 [10 11 12]]
[ 1  6  7 11]
[[11  2  3]
 [ 4  5 16]
 [17  8  9]
 [10 21 12]]
[[False False]
 [ True  True]
 [ True  True]]
[3 4 5 6]
[3 4 5 6]



int32
float64
int64
[[  6.   8.]
 [ 10.  12.]]
[[  6.   8.]
 [ 10.  12.]]
[[-4. -4.]
 [-4. -4.]]
[[-4. -4.]
 [-4. -4.]]
[[  5.  12.]
 [ 21.  32.]]
[[  5.  12.]
 [ 21.  32.]]
[[ 0.2         0.33333333]
 [ 0.42857143  0.5       ]]
[[ 0.2         0.33333333]
 [ 0.42857143  0.5       ]]
[[ 1.          1.41421356]
 [ 1.73205081  2.        ]]
219
219
[29 67]
[29 67]
[[19 22]
 [43 50]]
[[19 22]
 [43 50]]
10
[4 6]
[3 7]
[[1 2]
 [3 4]]
[[1 3]
 [2 4]]
[1 2 3]
[1 2 3]
[[ 2  2  4]
 [ 5  5  7]
 [ 8  8 10]
 [11 11 13]]
[[1 0 1]
 [1 0 1]
 [1 0 1]
 [1 0 1]]
[[ 2  2  4]
 [ 5  5  7]
 [ 8  8 10]
 [11 11 13]]
[[ 2  2  4]
 [ 5  5  7]
 [ 8  8 10]
 [11 11 13]]
[[ 4  5]
 [ 8 10]
 [12 15]]
[[2 4 6]
 [5 7 9]]
[[ 5  6  7]
 [ 9 10 11]]
[[ 5  6  7]
 [ 9 10 11]]
[[ 2  4  6]
 [ 8 10 12]]



uint8 (334L, 500L, 3L)



[[0 1]
 [1 0]
 [2 0]]
[[ 0.          1.41421356  2.23606798]
 [ 1.41421356  0.          1.        ]
 [ 2.23606798  1.          0.        ]]
 ![这里写图片描述](//img-blog.csdn.net/20160405213404851)
 ![这里写图片描述](//img-blog.csdn.net/20160405213420179)
 ![这里写图片描述](//img-blog.csdn.net/20160405213434460)
 ![这里写图片描述](//img-blog.csdn.net/20160405213447976)
posted @ 2016-04-05 21:30  geekvc  阅读(247)  评论(0编辑  收藏  举报