python algorithms mastering basic algorithms in python language

:#Coding:utf-8#!/usr/bin/pythonImportRandomImportMath#randomly generate a value between 0~100defget_andomnumber (num): Lists=[] I=0 whileinum:lists.append (random.randint (0,100)) I+=1returnlists#Adjustment Heapdefadjust_heap (lists, I, size): Lchild= 2 * i + 1Rchild= 2 * i + 2Max=IifI : ifLchild andLists[lchild] >Lists[max]: Max=LchildifRchild andLists[rchild] >Lists[max]: Max=RchildifMax! =I:lists[max], Lists[i]=Lists[i], Lists[max] adjust_heap (lists, max, size)#Create Heapdefbuild_h

How to implement eight sorting algorithms with Python 1. Insert SortDescribeThe basic operation of inserting a sort is to insert a data into the ordered data that is already sorted, so as to get a new sequential data with a number plus one, the algorithm is suitable for the ordering of a small amount of data, and the time complexity is O (n^2). is a stable sort

, and then in the remaining number, find the minimum (or maximum) number of the 2nd position to exchange, and so on, until the first N-1 element (the penultimate number) and the nth element (the last number) are compared.Algorithm implementation:def selectionsort (seq): Length=len (seq) for I in range (length): Mini=min (seq[i:]) if Seq[i]>mini: J=seq.index (mini,i) seq[i],seq[j]=seq[j],seq[i]if __name__== ' __main__ ': seq=[3,4,5,9,3,1,5,7,90,-2 ,] selectionsort (seq) print (SEQ)Efficiency:In t

Bucket sortBarrel sequencing has been used since the 1956, and the basic idea of the algorithm is proposed by E.J.ISSAC and R.c.singleton.This algorithm is like having 11 barrels, numbered from 0~10. Each occurrence of a number, in the corresponding number of buckets put aA little flag, and then just count the few small flags in each bucket OK. For example, there are 1 small flags in bucket 2nd, indicating2 appeared once, and in the 3rd barrels there

Python data structures and algorithms-Data TypesWe will review the built-in atomic data types from the atomic data types. python has two main built-in data classes: int and float. standard Arithmetic Operations: +,-, *,/, and ** (Power Operation). You can use parentheses to change the operation Priority. other very useful operators are the remainder operation %,

= BoyerMooreHorspool (pattern, text)Print 'text: ', TextPrint 'pattern', PatternIf s>-1:Print 'pattern' + Pattern + '\ "found at position', s These two algorithms are mainly used for string matching. Online comments said that the BM performance is better than KMP, and I have not verified it. You can test it with cProfile another day. Ps: Today we use these two algorithms to find the last line of string

observers, that is, dependent objects, each time data changes, these 2 view will changeclassHexviewer (object):defUpdate (self, subject):Print 'hexviewer:subject%s has data 0x%x'%(Subject.name, Subject.data)classDecimalviewer (object):defUpdate (self, subject):Print 'decimalviewer:subject%s has data%d'%(Subject.name, Subject.data)if __name__=='__main__': Data1= Data ('Data 1') Data2= Data ('Data 2') View1=decimalviewer () view2=hexviewer () Data1.attach (view1) Data1.attach (view2) Data2.attach

direction is unchanged#Coding=utf-8classNode (object):#first gets to the node, the node has two functions, 1. A pointer (assuming) a point, #2,. is now the value of the element in this position . def __init__(self,val): Self.next=none#the next pointer, which is used to point to the content, set the initialization to null firstSelf.val=val#Val is its original value,classQueue (object):def __init__(self):#initializes two properties, representing first and last respectivelyse

first 1frequency = [0 forIinchRange (0, 10, 1)] I= 1#do factorial operations, from 1! , 2! , 3! , until 100! The Operation forNinchRange (1, 100, 1): I= n *i m=First_number (i) frequency[m]= Frequency[m] + 1PrintFrequencyResults:/users/liudaoqiang/pycharmprojects/numpy/venv/bin/python/users/liudaoqiang/project/python_project/bat_day17/ 7, 7, 7, 3, ten, 4]process finished with exit code 03. Ben Ford's Law:In life, the probability of the first dig

This section learns to use Sklearn for voting classification, see a specific example, the dataset uses the Iris DataSet, using only the sepal width and petal length two dimension features, Category we also only use two categories: Iris-versicolor and Iris-virginica, the standard uses ROC AUC.Python Machine learning Chinese catalog (http://www.aibbt.com/a/20787.html)Reprint please specify the source, Python machine learning (http://www.aibbt.com/a/pyth

for regression problems. I found that unlike other methods, random forests are very good at mixing categorical or categorical variables with continuous variables. 41 Simple Python examples From sklearn.datasets import load_irisfrom sklearn.ensemble import Randomforestclassifierimport pandas as Pdimport numpy As Npiris = Load_iris () df = PD. DataFrame (Iris.data, columns=iris.feature_names) df[' is_train '] = np.random.uniform (0, 1, len (DF)) Here

Sorting is one of the basic algorithms that computer language needs to implement, and the orderly data structure can bring about great improvement in efficiency.1. Insert SortInsert Sort By default the currently inserted sequence is ordered, the new element is inserted into the position that should be inserted, and the new sequence remains orderly.defInsertion_so

# stack# In fact, the list in Python can be used as a stack, with Collections.deque can also# 1. Into the stack list.append (item)# 2. Out Stack item = List.pop ()# 3. For the first element out of the stack, you can also item = List.pop (0) and the queue concept# 4. In fact, you can also take any element out of the Stack item = List.pop (i) is equivalent to deleting element i# Note that 3,4 is time-consuming.Stacks can be conveniently used to determin

,row) - forIndexinchRange (I,len (row)):#Gets the subscript for each row of elements, and each time the loop shrinks the extent of the adjustment -Temp=array[i][index]#Get -array[i][index]=Array[index][i] inarray[index][i]=Temp - forRinchArrayPrint(R) to Print("------One loop---------")3, bubble sort, the tuples in a group of lists are arranged from small to large, and each inner loop moves only one element1 #bubble sort, which arranges tuples in a set of lists in order from small

1 Sequence decompression: through * to pass the match*a, B = somelist, First, *mid, last = Somelist, a, *b = Somelist2 using bidirectional queues: From collections import dequeQ = deque (maxlen=5) can be fixed lengthQ = deque () can also be any lengthcan be inserted and removed from both ends, append, Appendleft, pop, popleft3 finding the largest or smallest n elements: using heapq (heap queue)HEAPQ. nlargest (N, Alist) heapq. nsmallest (n, alist) is suitable when n is relatively smallYou can al

(self) self.engine.accept (visitor) self.body.accept (Visito R) forWheelinchself.wheels:wheel.accept (visitor)#This is our visitor, and every change is here .classPrintvisitor:defVisitwheel (self, Wheel):Print "Visiting"+wheel.name+"Wheel" defVisitengine (self, Engine):Print "Visiting engine" defvisitbody (self, body):Print "Visiting Body" defVisitcar (self, car):Print "Visiting Car"if __name__=='__main__': Car=Car () visitor=printvisitor () car.accept (visitor)Learn some

does not support the comparison size.-priority the priority counter, ensuring that the smallest element function in the queue (the most important) is always placed in _queue[0]. heapq.heappush()and the first element is heapq.heappop() inserted and deleted separately on the queue, the _queue first element of the _queue is always the smallest, ensuring that the queue _queue the first element with the highest priority.Reference: "Python Cookbook" 3rd Ed

First of all, to collect ...This article is for the author after learning Zhou Zhihua Teacher's machine study material, writes after the class exercises the programming question. Previously placed in the answer post, now re-organized, will need to implement the code to take out the part of the individual, slowly accumulate. Want to write a machine learning algorithm implementation of the series.This article mainly includes:1, Logistics return2.Python

1 using while loop input 1 2 3 4 5 6 8 91 # CODING=GBK 2 count=13 while count:4 if count==7 : 5 Pass 6 Else : 7 Print (count) 8 Count=count+12 for all numbers of 1-100 and1 # CODING=GBK 2 count=13 sum=04 for in range (1,100):5 sum=sum+Count6print(sum)3 All odd numbers in the output 1-1001 i=12 for in range (1,101):3 if i%2!=0: 4 Print (i) 5 I=i+16 else:7 Pass4 All even numbers in the output 1-1001 # CODING=GBK 2 i=13 for in rang

classGraph:def __init__(self): self. V= []classVertex:def __init__(self, x): Self.key=x Self.color=' White'SELF.D= 10000SELF.F= 10000Self.pi=None Self.adj= []classSolution:defDfs (Self, G): forUinchG.v:u.color=' White'U.pi=NoneGlobal Time Time=0 forUinchG.V:ifU.color = =' White': List=[u] self. Dfsvisit (G, U, list)Print "'. Join ([I.key forIinchList]) defdfsvisit (self, G, U, list):Global Time Time= time + 1U.D=Time U.color='Gray' forVinchU.adj:ifV.color = =' White': List.append (v) V.p