fibonacci algorithm python

Python implements the PageRank algorithm, purely using the Python native module, without using NumPy, scipy. The implementation of this program is relatively primitive, can be optimized more places.#-*-coding:utf-8-*-import randomn = 8 #八个网页d = 0.85 #阻尼因子为0.85delt = 0.00001# Iteration control variable # two matrix multiply def matrix_multi (A, b): Resul t = [[0]*

: Network Disk DownloadThis book introduces the basic knowledge of data structure and algorithm based on Python language, including abstract data type and Python object-oriented programming, linear table, string, stack and queue, binary tree and tree, set, sort and algorithm basic knowledge. This book continues to solv

K-means clustering algorithm introduction and python-based sample code, k-meanspython Clustering Today we will talk about K-means clustering algorithms, but we must first understand the differences between clustering and classification. Many business personnel are not very rigorous in their daily analysis. In fact, they are essentially different. CategoryIt is actually a process of mining patterns from spec

hash value is found in the database.Select a hashing algorithm The cryptographic hash algorithm is a common hashing algorithm. Similar to md5,sha1,sha256 this can be found in any language can be called a standard library, they are very effective for simple use cases. For example, to import a hashlib module in Python

This article mainly introduces the Python standard algorithm to achieve array full arrangement of the method, example analysis of the full arrangement of the principle and Python implementation techniques, the need for friends can refer to the This article describes the Python standard

I. Overview Recently, I've been using Python to learn the classic 8 sorting algorithms to consolidate the basics while strengthening my python skills. Under the face of quick sort do a brief introduction. Quick sort: Set the array to be sorted is a[0] ... A[n-1], first select any data (usually the first number of the array) as the key data, and then put all the smaller than it before it, all the number lar

("a", "->", "B ") move (1, "c", "a", "B") n = 1: print ("c", "->", "B ") // here the n-1 above column a is the movement of two plates // start to move the last plate on column a to column c (1, "a", "B ", "c") n = 1: print ("a", "->", "c ") // move the last plate on the column to the c column here (2, "B", "a", "c") n = 2: // start the second recursion of n = 2, that is, the current B ('B') plate (n-1) through a ('A ') move to c ('C') and move (1, "B", "c", "a") n = 1: // n = 2. The second recu

the development efficiency in practice, standardize the development process. 1, the front-end self-study and improve 2, the production and delivery of resumes 3. The skills that should be mastered in front-end interview work 4, front-end interview some problems set 5, the enterprise needs the front-end recruitment of talent, etc. This article is from the "Technology-aware" blog, so be sure to keep this source http://liuzhiying.blog.51cto.com/5850988/1924582JavaScri

Needleman-wunsch algorithm is a sequence alignment algorithm based on dynamic programming algorithm.Sheng Xin class to learn the algorithm, the class is free to work, with Python implementation.defintroduce ():Print("*********************************************") Print("Welcome to use short sequence alignment tool!

BP algorithm is the foundation and the most important part of neural network. The loss function needs to be adjusted because the gradient disappears or explodes during the reverse propagation of the error. In the lstm, through the sigmoid to achieve three doors to solve the memory problem, in the process of tensorflow implementation, the need for gradient pruning operations to prevent gradient explosion. RNN's BPTT

recursive scenario, the size of the sequence is 0 or one, which is always sorted. Although it is always recursive, the algorithm always exits, because in each iteration (iteration), it will at least put an element to its last position.#!/usr/bin/env python#-*-coding:utf-8-*-def func (L): if not L or len (l) = = 1: return l base = l[0] lef t_l, right_l = [], [] for I in L[1:]: If I

Apriori algorithm is the originator of data mining if pattern mining, since the 60 's began to popular, its algorithm is very simple and naïve, first digging out the length of the frequent pattern of 1, and then k=2Merging these frequent patterns into frequent patterns of length k, calculating their frequent occurrences, and ensuring that subsets of all k-1 lengths are frequent, it is worth noting that in o

'}) >>> ctree. discard (2) # if the deleted key does not exist, return None >>> ctree. discard (3) >>> ctree. _ delitem _ (3) # however ,. _ delitem _ (key) is different. if the key does not exist, an error is returned. Traceback (most recent call last): File" ", Line 1, in File "/usr/local/lib/python2.7/site-packages/bintrees/abctree. py ", line 264, in _ delitem _ self. remove (key) File "/usr/local/lib/python2.7/site-packages/bintrees/bintree. py ", line 124, in remove raise KeyE

have the data, it's stored in the places.txt file, and the fourth and fifth columns in the file are the data we need. Now we will find five clusters in the current data according to the Bikmeans algorithm , and display the resultsThe results of the program are as follows:Eight SummarizeClustering algorithm is an unsupervised algorithm, and the common clustering

,time#Decorative DevicedefCal_time (func):defWrapper (*args,**Kwargs): T1=time.time () ret= Func (*args,**Kwargs) T2=time.time ()Print('Time cost :%s --\nfunc from%s'% (T2-t1,func.__name__)) returnfuncreturnWrapper@cal_timedefSelect_sort (LI): forIinchRange (Len (LI)-1): Min_loc=I forJinchRange (i+1, Len (LI)):ifLI[J] Li[min_loc]: Min_loc=J Li[i],li[min_loc]= Li[min_loc],li[i]View Code12. Insert Sortdef Insert_sort (LI): for in range (1, Len (LI)): = Li[i] = i-1 while

-dimensional (more than 3-dimensional), it is generally difficult to directly use the original characteristics of the cluster results display. Tsne provides an efficient way to reduce the dimensionality of data, allowing us to display clustering results in 2-D or 3-dimensional spaces.Example code: Next to the sample code above#-*-coding:utf-8-*-#Pick k_means.py fromSklearn.manifoldImportTsnetsne=Tsne () tsne.fit_transform (Data_zs)#make data dimension reductionTsne = PD. DataFrame (tsne.embeddin

Summed up the common centralized sorting algorithm Merge sort Merge sort is also called merging sort, which is a typical application of partition method. The idea of divide-and-conquer is to decompose each problem into small problems, resolve each small problem, and then merge. The specific merge sort is to decompose a group of unordered numbers into N/2 recursively, one element is already ordered. " These ordered child elements are then merged.

Python cookbook (data structure and algorithm) dictionary-related computing example, pythoncookbook This example describes the computation problems related to the Python cookbook (data structure and algorithm) dictionary. We will share this with you for your reference. The details are as follows: Problem:Perform Variou

This article mainly introduces the principle and specific usage skills of the Python Clustering Algorithm for clustering hierarchical clustering, which has some reference value, for more information about Python clustering, see the following example. We will share this with you for your reference. The details are as follows: Hierarchical Clustering:The so-called

# KNN Algorithm Ideas:#-----------------------------------------------------##step1: Read-in data, stored as a linked list#step2: Data preprocessing, including missing value processing, normalization, etc.#step3: Set K value#step4: Calculates the distance between the sample to be tested and all samples (binary, ordinal, continuous)#step5: Voting determines the type of sample to be tested#step6: Test the correct rate with a test set#-------------------