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Commit581313c

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2 parentsf9e9ebc +ba6c1d5 commit581313c

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28 files changed

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-78
lines changed

28 files changed

+3655
-78
lines changed

‎README.md‎

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@@ -11,6 +11,8 @@ Linear Regression
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Random Forests
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Neural Networks
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Deep Reinforcement Learning
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Support Vector Machine
@@ -39,10 +41,6 @@ Anomaly Detection
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Bayesian Networks
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Neural Networks
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Probabilistic Programming (Sparse Data)
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Probabilistic Graphical Models
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AGI

‎data.json‎

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‎data/bank_data.csv‎

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‎python_algorithms/.DS_Store‎

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‎python_algorithms/collections/arrays/array.py‎renamed to ‎python_algorithms/collections/arrays/arrays.py‎

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# One extremely notable aspect of NumPy is the manner in which it extends
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# Python’s list indexing functionality—especially with multidimensional arrays.
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# To illustrate, make a simple two-dimensional array and try some experiments:
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# To illustrate, make a simple two-dimensional array and try some experiments:\
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a=np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
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print(a)
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#=> array([[ 1, 2, 3, 4], [ 5, 6, 7, 8], [ 9, 10, 11, 12]])
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#=> array([[ 1, 2, 3, 4],
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# python array
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#import array as arr
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#a = arr.array("I",[1,2,3])
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importarrayasarr
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a=arr.array("I",[1,2,3])
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# Type codeC TypePython TypeMinimum size in bytes
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# 'b'signed charint1

‎python_algorithms/collections/collections.py‎

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# counter
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# ChainMap
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# deque
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# sequence types:
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# tuple, list, string, range
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# unpacking sequences
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p='ray',0x30,'python'
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# no:
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name=p[0]
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hex=p[1]
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# yes:
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name,hex=p

‎python_algorithms/collections/dictionaries/combine_dict.py‎

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print(c['y'])
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# Outputs 2 (from b)
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print(c['z'])
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# by hand based on rules
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fromitertoolsimportgroupby
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mylist= [
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{'id':1,'name':'ray' },
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{'id':1,'email':'ray@hotmail.com' },
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{'id':2,'name':'liz' },
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{'id':2,'email':'liz@hotmail.com' }
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]
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fromfunctoolsimportreduce
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# map name, email to id
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res= [dict(
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reduce(lambday,z:y|z,# entries union
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map(lambdax:x.items(),v)# x is a grouper items object generator
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)
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)fork,vingroupby(mylist,key=lambdax:x['id']) ]
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print(res)

‎python_algorithms/collections/dictionaries/dict.py‎

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# to convert a list of tuples to a dictionary using the first value as the key.
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# Looked like this (making a database of comic book files):
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# Construct a dictionary from pairs
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# izip returns iterables
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# xrange is better than range
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# Loop over dict keys and values
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d= {'a':1,'b':2}
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# creates many items in mem
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fork,vind.items():
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print(k,v)
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# better in space
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fork,vind.iteritems():
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print(k,v)
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names= []
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colors= []
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fromitertoolsimportizip
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# faster than zip
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d=dict(izip(names,colors))
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# izip_longest fills in None's for shorter collection
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res=zip('abcd','wxyz')
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print(dict(res))
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print(dict([(k*3,v)fork,vinres]))
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# counting with dicts
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# ok:
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d= {}
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forcolorincolors:
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ifcolornotind:
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d[color]=0
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d[color]+=1
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# best:
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forcolorincolors:
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d[color]=d.get(color,0)+1
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# grouping with dicts
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# ok:
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names= ['fred','jim']
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d= {}
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fornameinnames:
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key=len(name)
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ifkeynotind:
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d[key]= []
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d[key].append(name)
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# better:
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d= {}
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fornameinnames:
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key=len(name)
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d.setdefault(key, []).append(name)
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# most best:
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d=defaultdict(list)
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fornameinnames:
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key=len(name)
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d[key].append(name)
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# to convert a list of tuples to a dictionary using the first value as the key.
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# Looked like this (making a database of comic book files):
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data= [((31), ("Amazing Spider-Man"), (481), ("Marvel")),
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((28), ("The Incredible Hulk"), (612), ("Marvel"))]# etc, hundreds more like this.
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‎python_algorithms/collections/lists/list.py‎

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frompprintimportpprint
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importitertools
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data= ['OCN',50,91.1, (2012,12,21) ]
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name,shares,price,date=data
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list.reverse(list_num)
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print(list_num)
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# flattening
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a_list= [[1,2], [3,4], [5,6]]
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print(list(itertools.chain.from_iterable(a_list)))
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# Output: [1, 2, 3, 4, 5, 6]
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# or
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print(list(itertools.chain(*a_list)))
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# Output: [1, 2, 3, 4, 5, 6]

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