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Created August 22, 2016 08:52
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cs231n python tutorial
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<type 'int'>\n",
"3\n",
"4\n",
"2\n",
"6\n",
"9\n",
"4\n",
"8\n",
"<type 'float'>\n",
"2.5 3.5 5.0 6.25\n"
]
}
],
"source": [
"\"\"\" Basic Data type\"\"\"\n",
"# 파이썬의 List는 Array와 비슷한 개념임.\n",
"# 하지만 List의 원소는 여러가지 타입이 들어갈 수 있음.\n",
"# 또한 Resizable 하다.\n",
"x = 3\n",
"print type(x) # Prints \"<type 'int'>\"\n",
"print x # Prints \"3\"\n",
"print x + 1 # Addition; prints \"4\"\n",
"print x - 1 # Subtraction; prints \"2\"\n",
"print x * 2 # Multiplication; prints \"6\"\n",
"print x ** 2 # Exponentiation; prints \"9\"\n",
"x += 1\n",
"print x # Prints \"4\"\n",
"x *= 2\n",
"print x # Prints \"8\"\n",
"y = 2.5\n",
"print type(y) # Prints \"<type 'float'>\"\n",
"print y, y + 1, y * 2, y ** 2 # Prints \"2.5 3.5 5.0 6.25\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<type 'bool'>\n",
"False\n",
"True\n",
"False\n",
"True\n"
]
}
],
"source": [
"t = True\n",
"f = False\n",
"print type(t) # Prints \"<type 'bool'>\"\n",
"print t and f # Logical AND; prints \"False\"\n",
"print t or f # Logical OR; prints \"True\"\n",
"print not t # Logical NOT; prints \"False\"\n",
"print t != f # Logical XOR; prints \"True\" "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello\n",
"5\n",
"hello world\n",
"hello world 12\n"
]
}
],
"source": [
"hello = 'hello' # String literals can use single quotes\n",
"world = \"world\" # or double quotes; it does not matter.\n",
"print hello # Prints \"hello\"\n",
"print len(hello) # String length; prints \"5\"\n",
"hw = hello + ' ' + world # String concatenation\n",
"print hw # prints \"hello world\"\n",
"hw12 = '%s %s %d' % (hello, world, 12) # sprintf style string formatting\n",
"print hw12 # prints \"hello world 12\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello\n",
"HELLO\n",
" hello\n",
" hello \n",
"he(ell)(ell)o\n",
"world\n"
]
}
],
"source": [
"s = \"hello\"\n",
"print s.capitalize() # Capitalize a string; prints \"Hello\"\n",
"print s.upper() # Convert a string to uppercase; prints \"HELLO\"\n",
"print s.rjust(7) # Right-justify a string, padding with spaces; prints \" hello\"\n",
"print s.center(7) # Center a string, padding with spaces; prints \" hello \"\n",
"print s.replace('l', '(ell)') # Replace all instances of one substring with another;\n",
" # prints \"he(ell)(ell)o\"\n",
"print ' world '.strip() # Strip leading and trailing whitespace; prints \"world\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3, 1, 2] 2\n",
"2\n",
"[3, 1, 'foo']\n",
"[3, 1, 'foo', 'bar']\n",
"bar [3, 1, 'foo']\n"
]
}
],
"source": [
"\"\"\" Containers \"\"\"\n",
"xs = [3, 1, 2] # Create a list\n",
"print xs, xs[2] # Prints \"[3, 1, 2] 2\"\n",
"print xs[-1] # Negative indices count from the end of the list; prints \"2\"\n",
"xs[2] = 'foo' # Lists can contain elements of different types\n",
"print xs # Prints \"[3, 1, 'foo']\"\n",
"xs.append('bar') # Add a new element to the end of the list\n",
"print xs # Prints \"[3, 1, 'foo', 'bar']\"\n",
"x = xs.pop() # Remove and return the last element of the list\n",
"print x, xs # Prints \"bar [3, 1, 'foo']\""
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 1, 2, 3, 4]\n",
"[2, 3]\n",
"[2, 3, 4]\n",
"[0, 1]\n",
"[0, 1, 2, 3, 4]\n",
"[0, 1, 2, 3]\n",
"[0, 1, 8, 9, 4]\n"
]
}
],
"source": [
"nums = range(5) # range is a built-in function that creates a list of integers\n",
"print nums # Prints \"[0, 1, 2, 3, 4]\"\n",
"print nums[2:4] # Get a slice from index 2 to 4 (exclusive); prints \"[2, 3]\"\n",
"print nums[2:] # Get a slice from index 2 to the end; prints \"[2, 3, 4]\"\n",
"print nums[:2] # Get a slice from the start to index 2 (exclusive); prints \"[0, 1]\"\n",
"print nums[:] # Get a slice of the whole list; prints [\"0, 1, 2, 3, 4]\"\n",
"print nums[:-1] # Slice indices can be negative; prints [\"0, 1, 2, 3]\"\n",
"nums[2:4] = [8, 9] # Assign a new sublist to a slice\n",
"print nums # Prints \"[0, 1, 8, 9, 4]\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cat\n",
"dog\n",
"monkey\n"
]
}
],
"source": [
"animals = ['cat', 'dog', 'monkey']\n",
"for animal in animals:\n",
" print animal\n",
"# Prints \"cat\", \"dog\", \"monkey\", each on its own line."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"#1: cat\n",
"#2: dog\n",
"#3: monkey\n",
"<enumerate object at 0x1055aaa00>\n"
]
}
],
"source": [
"animals = ['cat', 'dog', 'monkey']\n",
"for idx, animal in enumerate(animals):\n",
" print '#%d: %s' % (idx + 1, animal)\n",
"# Prints \"#1: cat\", \"#2: dog\", \"#3: monkey\", each on its own line\n",
"print enumerate(animals)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 1, 4, 9, 16]\n"
]
}
],
"source": [
"\"\"\" List Comprehension \"\"\"\n",
"# 어떠한 데이터 타입을 새로운 데이터 타입으로 변환할 때 씀.\n",
"# 일반적인 Language의 경우 다음과 같이 처리함.\n",
"nums = [0, 1, 2, 3, 4]\n",
"squares = []\n",
"for x in nums:\n",
" squares.append(x ** 2)\n",
"print squares # Prints [0, 1, 4, 9, 16]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 1, 4, 9, 16]\n"
]
}
],
"source": [
"# Python의 List Comprehension\n",
"squares = [x**2 for x in nums]\n",
"print squares"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 4, 16]\n"
]
}
],
"source": [
"nums = [0, 1, 2, 3, 4]\n",
"even_squares = [x ** 2 for x in nums if x % 2 == 0]\n",
"print even_squares # Prints \"[0, 4, 16]\""
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cute\n",
"True\n",
"wet\n",
"N/A\n",
"wet\n",
"N/A\n"
]
}
],
"source": [
"# Dictionary\n",
"d = {'cat': 'cute', 'dog': 'furry'} # Create a new dictionary with some data\n",
"print d['cat'] # Get an entry from a dictionary; prints \"cute\"\n",
"print 'cat' in d # Check if a dictionary has a given key; prints \"True\"\n",
"d['fish'] = 'wet' # Set an entry in a dictionary\n",
"print d['fish'] # Prints \"wet\"\n",
"# print d['monkey'] # KeyError: 'monkey' not a key of d\n",
"print d.get('monkey', 'N/A') # Get an element with a default; prints \"N/A\"\n",
"print d.get('fish', 'N/A') # Get an element with a default; prints \"wet\"\n",
"del d['fish'] # Remove an element from a dictionary\n",
"print d.get('fish', 'N/A') # \"fish\" is no longer a key; prints \"N/A\""
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"A person has 2 legs\n",
"A spider has 8 legs\n",
"A cat has 4 legs\n"
]
}
],
"source": [
"d = {'person': 2, 'cat': 4, 'spider': 8}\n",
"for animal in d:\n",
" legs = d[animal]\n",
" print 'A %s has %d legs' % (animal, legs)\n",
"# Prints \"A person has 2 legs\", \"A spider has 8 legs\", \"A cat has 4 legs\""
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"A person has 2 legs\n",
"A spider has 8 legs\n",
"A cat has 4 legs\n"
]
}
],
"source": [
"d = {'person': 2, 'cat': 4, 'spider': 8}\n",
"for animal, legs in d.iteritems():\n",
" print 'A %s has %d legs' % (animal, legs)\n",
"# Prints \"A person has 2 legs\", \"A spider has 8 legs\", \"A cat has 4 legs\""
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{0: 0, 2: 4, 4: 16}\n"
]
}
],
"source": [
"nums = [0, 1, 2, 3, 4]\n",
"even_num_to_square = {x: x ** 2 for x in nums if x % 2 == 0}\n",
"print even_num_to_square # Prints \"{0: 0, 2: 4, 4: 16}\""
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n",
"False\n",
"True\n",
"3\n",
"3\n",
"2\n"
]
}
],
"source": [
"# Sets\n",
"animals = {'cat', 'dog'}\n",
"print 'cat' in animals # Check if an element is in a set; prints \"True\"\n",
"print 'fish' in animals # prints \"False\"\n",
"animals.add('fish') # Add an element to a set\n",
"print 'fish' in animals # Prints \"True\"\n",
"print len(animals) # Number of elements in a set; prints \"3\"\n",
"animals.add('cat') # Adding an element that is already in the set does nothing\n",
"print len(animals) # Prints \"3\"\n",
"animals.remove('cat') # Remove an element from a set\n",
"print len(animals) # Prints \"2\""
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"#1: fish\n",
"#2: dog\n",
"#3: cat\n"
]
}
],
"source": [
"animals = {'cat', 'dog', 'fish'}\n",
"for idx, animal in enumerate(animals):\n",
" print '#%d: %s' % (idx + 1, animal)\n",
"# Prints \"#1: fish\", \"#2: dog\", \"#3: cat\""
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"set([0, 1, 2, 3, 4, 5])\n"
]
}
],
"source": [
"from math import sqrt\n",
"nums = {int(sqrt(x)) for x in range(30)}\n",
"print nums # Prints \"set([0, 1, 2, 3, 4, 5])\""
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<type 'tuple'>\n",
"5\n",
"1\n"
]
}
],
"source": [
"# Tuples\n",
"# Tuple은 List와 비슷하지만 Dictionary의 Key로 쓰일 수 있다는 점이 다르다.\n",
"# A tuple is an (immutable) ordered list of values. \n",
"# A tuple is in many ways similar to a list; \n",
"# one of the most important differences is that tuples can be used as keys in dictionaries \n",
"# and as elements of sets, while lists cannot. Here is a trivial example:\n",
"\n",
"d = {(x, x + 1): x for x in range(10)} # Create a dictionary with tuple keys\n",
"t = (5, 6) # Create a tuple\n",
"print type(t) # Prints \"<type 'tuple'>\"\n",
"print d[t] # Prints \"5\"\n",
"print d[(1, 2)] # Prints \"1\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\" Function \"\"\"\n",
"def sign(x):\n",
" if x > 0:\n",
" return 'positive'\n",
" elif x < 0:\n",
" return 'negative'\n",
" else:\n",
" return 'zero'\n",
"\n",
"for x in [-1, 0, 1]:\n",
" print sign(x)\n",
"# Prints \"negative\", \"zero\", \"positive\""
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello, Bob\n",
"HELLO, FRED!\n"
]
}
],
"source": [
"def hello(name, loud=False):\n",
" if loud:\n",
" print 'HELLO, %s!' % name.upper()\n",
" else:\n",
" print 'Hello, %s' % name\n",
"\n",
"hello('Bob') # Prints \"Hello, Bob\"\n",
"hello('Fred', loud=True) # Prints \"HELLO, FRED!\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\" Classes \"\"\"\n",
"class Greeter(object):\n",
" \n",
" # Constructor\n",
" def __init__(self, name):\n",
" self.name = name # Create an instance variable\n",
" \n",
" # Instance method\n",
" def greet(self, loud=False):\n",
" if loud:\n",
" print 'HELLO, %s!' % self.name.upper()\n",
" else:\n",
" print 'Hello, %s' % self.name\n",
" \n",
"g = Greeter('Fred') # Construct an instance of the Greeter class\n",
"g.greet() # Call an instance method; prints \"Hello, Fred\"\n",
"g.greet(loud=True) # Call an instance method; prints \"HELLO, FRED!\""
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<type 'numpy.ndarray'>\n",
"(3,)\n",
"1 2 3\n",
"[5 2 3]\n",
"(2, 3)\n",
"1 2 4\n"
]
}
],
"source": [
"\"\"\" Numpy \"\"\"\n",
"# numpy handles multidimensional array !! \n",
"# Numpy는 다차원 배열을 다루는 패키지이다.\n",
"import numpy as np\n",
"\n",
"a = np.array([1, 2, 3]) # Create a rank 1 array\n",
"print type(a) # Prints \"<type 'numpy.ndarray'>\"\n",
"print a.shape # Prints \"(3,)\"\n",
"print a[0], a[1], a[2] # Prints \"1 2 3\"\n",
"a[0] = 5 # Change an element of the array\n",
"print a # Prints \"[5, 2, 3]\"\n",
"\n",
"b = np.array([[1,2,3],[4,5,6]]) # Create a rank 2 array\n",
"print b.shape # Prints \"(2, 3)\"\n",
"print b[0, 0], b[0, 1], b[1, 0] # Prints \"1 2 4\""
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0. 0.]\n",
" [ 0. 0.]]\n",
"[[ 1. 1.]]\n",
"[[ 7. 7.]\n",
" [ 7. 7.]]\n",
"[[ 1. 0.]\n",
" [ 0. 1.]]\n",
"[[ 0.91137441 0.36538478]\n",
" [ 0.6558997 0.87953913]]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/ahn/anaconda/envs/tensorflow/lib/python2.7/site-packages/numpy/core/numeric.py:301: FutureWarning: in the future, full((2, 2), 7) will return an array of dtype('int64')\n",
" format(shape, fill_value, array(fill_value).dtype), FutureWarning)\n"
]
}
],
"source": [
"# initializing multidimensional array\n",
"a = np.zeros((2,2)) # Create an array of all zeros\n",
"print a # Prints \"[[ 0. 0.]\n",
" # [ 0. 0.]]\"\n",
" \n",
"b = np.ones((1,2)) # Create an array of all ones\n",
"print b # Prints \"[[ 1. 1.]]\"\n",
"\n",
"c = np.full((2,2), 7) # Create a constant array\n",
"print c # Prints \"[[ 7. 7.]\n",
" # [ 7. 7.]]\"\n",
"\n",
"d = np.eye(2) # Create a 2x2 identity matrix\n",
"print d # Prints \"[[ 1. 0.]\n",
" # [ 0. 1.]]\"\n",
" \n",
"e = np.random.random((2,2)) # Create an array filled with random values\n",
"print e # Might print \"[[ 0.91940167 0.08143941]\n",
" # [ 0.68744134 0.87236687]]\""
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 1 2 3 4]\n",
" [ 5 6 7 8]\n",
" [ 9 10 11 12]]\n",
"2\n",
"[[77 3]\n",
" [ 6 7]]\n",
"[[ 1 77 3 4]\n",
" [ 5 6 7 8]\n",
" [ 9 10 11 12]]\n",
"77\n"
]
}
],
"source": [
"# Create the following rank 2 array with shape (3, 4)\n",
"# [[ 1 2 3 4]\n",
"# [ 5 6 7 8]\n",
"# [ 9 10 11 12]]\n",
"a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
"\n",
"# Use slicing to pull out the subarray consisting of the first 2 rows\n",
"# and columns 1 and 2; b is the following array of shape (2, 2):\n",
"# [[2 3]\n",
"# [6 7]]\n",
"b = a[:2, 1:3]\n",
"\n",
"print a\n",
"\n",
"# A slice of an array is a view into the same data, so modifying it\n",
"# will modify the original array.\n",
"print a[0, 1] # Prints \"2\"\n",
"b[0, 0] = 77 # b[0, 0] is the same piece of data as a[0, 1]\n",
"\n",
"print b\n",
"\n",
"print a\n",
"\n",
"print a[0, 1] # Prints \"77\""
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[5 6 7 8] (4,)\n",
"[[5 6 7 8]] (1, 4)\n",
"[ 2 6 10] (3,)\n",
"[[ 2]\n",
" [ 6]\n",
" [10]] (3, 1)\n"
]
}
],
"source": [
"# Create the following rank 2 array with shape (3, 4)\n",
"# [[ 1 2 3 4]\n",
"# [ 5 6 7 8]\n",
"# [ 9 10 11 12]]\n",
"a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
"\n",
"# Two ways of accessing the data in the middle row of the array.\n",
"# Mixing integer indexing with slices yields an array of lower rank,\n",
"# while using only slices yields an array of the same rank as the\n",
"# original array:\n",
"row_r1 = a[1, :] # Rank 1 view of the second row of a \n",
"row_r2 = a[1:2, :] # Rank 2 (return same rank) view of the second row of a\n",
"print row_r1, row_r1.shape # Prints \"[5 6 7 8] (4,)\"\n",
"print row_r2, row_r2.shape # Prints \"[[5 6 7 8]] (1, 4)\"\n",
"\n",
"# We can make the same distinction when accessing columns of an array:\n",
"col_r1 = a[:, 1]\n",
"col_r2 = a[:, 1:2]\n",
"print col_r1, col_r1.shape # Prints \"[ 2 6 10] (3,)\"\n",
"print col_r2, col_r2.shape # Prints \"[[ 2]\n",
" # [ 6]\n",
" # [10]] (3, 1)\""
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 2]\n",
" [3 4]\n",
" [5 6]]\n",
"[1 4 5]\n",
"[1 4 5]\n",
"[[1 2]\n",
" [3 4]\n",
" [5 6]]\n",
"[2 2]\n",
"[2 2]\n"
]
}
],
"source": [
"import numpy as np\n",
"a = np.array([[1,2], [3, 4], [5, 6]])\n",
"# An example of integer array indexing.\n",
"# The returned array will have shape (3,) and \n",
"print a\n",
"print a[[0, 1, 2], [0, 1, 0]] # Prints \"[1 4 5]\"\n",
"\n",
"# The above example of integer array indexing is equivalent to this:\n",
"print np.array([a[0, 0], a[1, 1], a[2, 0]]) # Prints \"[1 4 5]\"\n",
"\n",
"# When using integer array indexing, you can reuse the same\n",
"# element from the source array:\n",
"print a\n",
"print a[[0, 0], [1, 1]] # Prints \"[2 2]\"\n",
"\n",
"# Equivalent to the previous integer array indexing example\n",
"print np.array([a[0, 1], a[0, 1]]) # Prints \"[2 2]\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"# Create a new array from which we will select elements\n",
"a = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n",
"\n",
"print a # prints \"array([[ 1, 2, 3],\n",
" # [ 4, 5, 6],\n",
" # [ 7, 8, 9],\n",
" # [10, 11, 12]])\"\n",
"\n",
"# Create an array of indices\n",
"b = np.array([0, 2, 0, 1])\n",
"\n",
"# Select one element from each row of a using the indices in b\n",
"print a[np.arange(4), b] # Prints \"[ 1 6 7 11]\"\n",
"\n",
"# Mutate one element from each row of a using the indices in b\n",
"a[np.arange(4), b] += 10\n",
"\n",
"print a # prints \"array([[11, 2, 3],\n",
" # [ 4, 5, 16],\n",
" # [17, 8, 9],\n",
" # [10, 21, 12]])"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[False False]\n",
" [ True True]\n",
" [ True True]]\n",
"[3 4 5 6]\n",
"[3 4 5 6]\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"a = np.array([[1,2], [3, 4], [5, 6]])\n",
"\n",
"bool_idx = (a > 2) # Find the elements of a that are bigger than 2;\n",
" # this returns a numpy array of Booleans of the same\n",
" # shape as a, where each slot of bool_idx tells\n",
" # whether that element of a is > 2.\n",
" \n",
"print bool_idx # Prints \"[[False False]\n",
" # [ True True]\n",
" # [ True True]]\"\n",
"\n",
"# We use boolean array indexing to construct a rank 1 array\n",
"# consisting of the elements of a corresponding to the True values\n",
"# of bool_idx\n",
"print a[bool_idx] # Prints \"[3 4 5 6]\"\n",
"\n",
"# We can do all of the above in a single concise statement:\n",
"print a[a > 2] # Prints \"[3 4 5 6]\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"int64\n",
"int64\n",
"float64\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"x = np.array([1, 2]) # Let numpy choose the datatype\n",
"print x.dtype # Prints \"int64\"\n",
"\n",
"x = np.array([1.0, 2.0], dtype=np.int64) # Let numpy choose the datatype\n",
"print x.dtype # Prints \"float64\"\n",
"\n",
"x = np.array([1, 2], dtype=np.float64) # Force a particular datatype\n",
"print x.dtype # Prints \"int64\""
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 6. 8.]\n",
" [ 10. 12.]]\n",
"[[ 6. 8.]\n",
" [ 10. 12.]]\n",
"[[-4. -4.]\n",
" [-4. -4.]]\n",
"[[-4. -4.]\n",
" [-4. -4.]]\n",
"[[ 5. 12.]\n",
" [ 21. 32.]]\n",
"[[ 5. 12.]\n",
" [ 21. 32.]]\n",
"[[ 0.2 0.33333333]\n",
" [ 0.42857143 0.5 ]]\n",
"[[ 0.2 0.33333333]\n",
" [ 0.42857143 0.5 ]]\n",
"[[ 1. 1.41421356]\n",
" [ 1.73205081 2. ]]\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"x = np.array([[1,2],[3,4]], dtype=np.float64)\n",
"y = np.array([[5,6],[7,8]], dtype=np.float64)\n",
"\n",
"# Elementwise sum; both produce the array\n",
"# [[ 6.0 8.0]\n",
"# [10.0 12.0]]\n",
"print x + y\n",
"print np.add(x, y)\n",
"\n",
"# Elementwise difference; both produce the array\n",
"# [[-4.0 -4.0]\n",
"# [-4.0 -4.0]]\n",
"print x - y\n",
"print np.subtract(x, y)\n",
"\n",
"# Elementwise product; both produce the array\n",
"# [[ 5.0 12.0]\n",
"# [21.0 32.0]]\n",
"print x * y\n",
"print np.multiply(x, y)\n",
"\n",
"# Elementwise division; both produce the array\n",
"# [[ 0.2 0.33333333]\n",
"# [ 0.42857143 0.5 ]]\n",
"print x / y\n",
"print np.divide(x, y)\n",
"\n",
"# Elementwise square root; produces the array\n",
"# [[ 1. 1.41421356]\n",
"# [ 1.73205081 2. ]]\n",
"print np.sqrt(x)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"219\n",
"219\n",
"[29 67]\n",
"[29 67]\n",
"[[19 22]\n",
" [43 50]]\n",
"[[19 22]\n",
" [43 50]]\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"x = np.array([[1,2],[3,4]])\n",
"y = np.array([[5,6],[7,8]])\n",
"\n",
"v = np.array([9,10])\n",
"w = np.array([11, 12])\n",
"\n",
"# Inner product of vectors; both produce 219\n",
"print v.dot(w)\n",
"print np.dot(v, w)\n",
"\n",
"# Matrix / vector product; both produce the rank 1 array [29 67]\n",
"print x.dot(v)\n",
"print np.dot(x, v)\n",
"\n",
"# Matrix / matrix product; both produce the rank 2 array\n",
"# [[19 22]\n",
"# [43 50]]\n",
"print x.dot(y)\n",
"print np.dot(x, y)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n",
"[4 6]\n",
"[3 7]\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"x = np.array([[1,2],[3,4]])\n",
"\n",
"print np.sum(x) # Compute sum of all elements; prints \"10\"\n",
"print np.sum(x, axis=0) # Compute sum of each column; prints \"[4 6]\"\n",
"print np.sum(x, axis=1) # Compute sum of each row; prints \"[3 7]\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 2]\n",
" [3 4]]\n",
"[[1 3]\n",
" [2 4]]\n",
"[1 2 3]\n",
"[1 2 3]\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"x = np.array([[1,2], [3,4]])\n",
"print x # Prints \"[[1 2]\n",
" # [3 4]]\"\n",
"print x.T # Prints \"[[1 3]\n",
" # [2 4]]\"\n",
"\n",
"# Note that taking the transpose of a rank 1 array does nothing:\n",
"v = np.array([1,2,3])\n",
"print v # Prints \"[1 2 3]\"\n",
"print v.T # Prints \"[1 2 3]\"\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 1 2 3]\n",
" [ 4 5 6]\n",
" [ 7 8 9]\n",
" [10 11 12]]\n",
"[1 0 1]\n",
"y\n",
"[[ 1 2 3]\n",
" [ 4 5 6]\n",
" [ 7 8 9]\n",
" [10 11 12]]\n",
"y finished\n",
"[[ 2 2 4]\n",
" [ 5 5 7]\n",
" [ 8 8 10]\n",
" [11 11 13]]\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"# We will add the vector v to each row of the matrix x,\n",
"# storing the result in the matrix y\n",
"x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n",
"v = np.array([1, 0, 1])\n",
"y = np.empty_like(x) # Create an empty matrix with the same shape as x\n",
"\n",
"print x\n",
"print v\n",
"print 'y'\n",
"print y\n",
"print 'y finished'\n",
"\n",
"# Add the vector v to each row of the matrix x with an explicit loop\n",
"for i in range(4):\n",
" y[i, :] = x[i, :] + v\n",
"\n",
"# Now y is the following\n",
"# [[ 2 2 4]\n",
"# [ 5 5 7]\n",
"# [ 8 8 10]\n",
"# [11 11 13]]\n",
"print y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"# We will add the vector v to each row of the matrix x,\n",
"# storing the result in the matrix y\n",
"x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n",
"v = np.array([1, 0, 1])\n",
"vv = np.tile(v, (4, 1)) # Stack 4 copies of v on top of each other\n",
"print vv # Prints \"[[1 0 1]\n",
" # [1 0 1]\n",
" # [1 0 1]\n",
" # [1 0 1]]\"\n",
"y = x + vv # Add x and vv elementwise\n",
"print y # Prints \"[[ 2 2 4\n",
" # [ 5 5 7]\n",
" # [ 8 8 10]\n",
" # [11 11 13]]\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"# We will add the vector v to each row of the matrix x,\n",
"# storing the result in the matrix y\n",
"x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n",
"v = np.array([1, 0, 1])\n",
"y = x + v # Add v to each row of x using broadcasting\n",
"print y # Prints \"[[ 2 2 4]\n",
" # [ 5 5 7]\n",
" # [ 8 8 10]\n",
" # [11 11 13]]\""
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 4 5]\n",
" [ 8 10]\n",
" [12 15]]\n",
"[[2 4 6]\n",
" [5 7 9]]\n",
"[[ 5 6 7]\n",
" [ 9 10 11]]\n",
"[[ 5 6 7]\n",
" [ 9 10 11]]\n",
"[[ 2 4 6]\n",
" [ 8 10 12]]\n"
]
}
],
"source": [
"# Broad Casting Example\n",
"import numpy as np\n",
"\n",
"# Compute outer product of vectors\n",
"v = np.array([1,2,3]) # v has shape (3,)\n",
"w = np.array([4,5]) # w has shape (2,)\n",
"# To compute an outer product, we first reshape v to be a column\n",
"# vector of shape (3, 1); we can then broadcast it against w to yield\n",
"# an output of shape (3, 2), which is the outer product of v and w:\n",
"# [[ 4 5]\n",
"# [ 8 10]\n",
"# [12 15]]\n",
"print np.reshape(v, (3, 1)) * w\n",
"\n",
"# Add a vector to each row of a matrix\n",
"x = np.array([[1,2,3], [4,5,6]])\n",
"# x has shape (2, 3) and v has shape (3,) so they broadcast to (2, 3),\n",
"# giving the following matrix:\n",
"# [[2 4 6]\n",
"# [5 7 9]]\n",
"print x + v\n",
"\n",
"# Add a vector to each column of a matrix\n",
"# x has shape (2, 3) and w has shape (2,).\n",
"# If we transpose x then it has shape (3, 2) and can be broadcast\n",
"# against w to yield a result of shape (3, 2); transposing this result\n",
"# yields the final result of shape (2, 3) which is the matrix x with\n",
"# the vector w added to each column. Gives the following matrix:\n",
"# [[ 5 6 7]\n",
"# [ 9 10 11]]\n",
"print (x.T + w).T\n",
"# Another solution is to reshape w to be a row vector of shape (2, 1);\n",
"# we can then broadcast it directly against x to produce the same\n",
"# output.\n",
"print x + np.reshape(w, (2, 1))\n",
"\n",
"# Multiply a matrix by a constant:\n",
"# x has shape (2, 3). Numpy treats scalars as arrays of shape ();\n",
"# these can be broadcast together to shape (2, 3), producing the\n",
"# following array:\n",
"# [[ 2 4 6]\n",
"# [ 8 10 12]]\n",
"print x * 2"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"uint8 (400, 248, 3)\n"
]
}
],
"source": [
"\"\"\" Image Operation \"\"\"\n",
"from scipy.misc import imread, imsave, imresize\n",
"\n",
"# Read an JPEG image into a numpy array\n",
"img = imread('cat.jpg')\n",
"print img.dtype, img.shape # Prints \"uint8 (400, 248, 3)\"\n",
"\n",
"# We can tint the image by scaling each of the color channels\n",
"# by a different scalar constant. The image has shape (400, 248, 3);\n",
"# we multiply it by the array [1, 0.95, 0.9] of shape (3,);\n",
"# numpy broadcasting means that this leaves the red channel unchanged,\n",
"# and multiplies the green and blue channels by 0.95 and 0.9\n",
"# respectively.\n",
"img_tinted = img * [1, 0.95, 0.9]\n",
"\n",
"# Resize the tinted image to be 300 by 300 pixels.\n",
"img_tinted = imresize(img_tinted, (300, 300))\n",
"\n",
"# Write the tinted image back to disk\n",
"imsave('cat_tinted.jpg', img_tinted)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. 1.1 1.2 1.3 1.4\n",
" 1.5 1.6 1.7 1.8 1.9 2. 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9\n",
" 3. 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4. 4.1 4.2 4.3 4.4\n",
" 4.5 4.6 4.7 4.8 4.9 5. 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9\n",
" 6. 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7. 7.1 7.2 7.3 7.4\n",
" 7.5 7.6 7.7 7.8 7.9 8. 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9\n",
" 9. 9.1 9.2 9.3 9.4]\n"
]
},
{
"data": {
"image/png": 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xl2Dq1fOjfbLe+r//frj44tApJGtU/CWo8q6f7dtDJwnjtddgwwZ/s1ekkFT8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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f914c90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\"\"\" Matplotlib \"\"\"\n",
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Compute the x and y coordinates for points on a sine curve\n",
"x = np.arange(0, 3 * np.pi, 0.1) # What is diffrence between np.arange and np.linspace?\n",
"y = np.sin(x)\n",
"\n",
"# Plot the points using matplotlib\n",
"plt.plot(x, y)\n",
"plt.show() # You must call plt.show() to make graphics appear."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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A1665byPzgrcKhQpBWBhMn65aiXWZf3A+jco2orx3FomcXJDcuqkyaVK+CWky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\ncTjnnEvKfwF295HY8GZeGAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10fa1ead0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Compute the x and y coordinates for points on sine and cosine curves\n",
"x = np.arange(0, 3 * np.pi, 0.1)\n",
"y_sin = np.sin(x)\n",
"y_cos = np.cos(x)\n",
"\n",
"# Plot the points using matplotlib\n",
"plt.plot(x, y_sin)\n",
"plt.plot(x, y_cos)\n",
"plt.xlabel('x axis label')\n",
"plt.ylabel('y axis label')\n",
"plt.title('Sine and Cosine')\n",
"plt.legend(['Sine', 'Cosine'])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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icnjsVL9g6y9yd/DSebfj04BfROTOWBdFGRGpKiInxet3dK6kklHP37lgVPUJ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\nLgP9PxtVdeslZU8eAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110120890>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Subplot \n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Compute the x and y coordinates for points on sine and cosine curves\n",
"x = np.arange(0, 3 * np.pi, 0.1)\n",
"y_sin = np.sin(x)\n",
"y_cos = np.cos(x)\n",
"\n",
"# Set up a subplot grid that has height 2 and width 1,\n",
"# and set the first such subplot as active.\n",
"plt.subplot(2, 1, 1)\n",
"\n",
"# Make the first plot\n",
"plt.plot(x, y_sin)\n",
"plt.title('Sine')\n",
"\n",
"# Set the second subplot as active, and make the second plot.\n",
"plt.subplot(2, 1, 2)\n",
"plt.plot(x, y_cos)\n",
"plt.title('Cosine')\n",
"\n",
"# Show the figure.\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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3Zg5pBcsL07gqyayshdNvn+exRx9leaXMwx95iL3796AUrFcDJqdmuXJlmTCM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9/m6puvgaqoywxdlSyXcxbJEul6KGPN3Wvrm6ysbrC1+wTjyRI/aOEIj/FogsFw9twua+ureE0K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Yzb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\nXVP8VqhVAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f7388d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Image plotting \n",
"import numpy as np\n",
"from scipy.misc import imread, imresize\n",
"import matplotlib.pyplot as plt\n",
"\n",
"img = imread('cat.jpg')\n",
"img_tinted = img * [1, 0.95, 0.9] # Change RGB Color little\n",
"\n",
"# Show the original image\n",
"plt.subplot(1, 2, 1)\n",
"plt.imshow(img)\n",
"\n",
"# Show the tinted image\n",
"plt.subplot(1, 2, 2)\n",
"\n",
"# A slight gotcha with imshow is that it might give strange results\n",
"# if presented with data that is not uint8. To work around this, we\n",
"# explicitly cast the image to uint8 before displaying it.\n",
"plt.imshow(np.uint8(img_tinted))\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "tensorflow",
"language": "python",
"name": "tensorflow"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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