fibonacci algorithm python

What the stack is, you can understand as a first-in-a-out data structure, a linear table with restricted operations. The following article mainly introduces the application of Python in the actual combat, the text gives a number of examples, the need for friends can reference, the following to see together. Stacks (Stack) Stack is called the stack is a special ordered table, its insertion and deletion operations at the top of the stack, and follow th

is independent of the operand. For example, the result of 2//3 is that the result of 0,-2//3 is -1,-2.0//3-1.0.In Python 3.0, X/y will only perform a true division, regardless of the operand; x//y performs floor division.If you need to use this in the 2.5 version of Python, you will need to include the declaration of the From __FUTURE__ Import division before the code. Such as: from __future__ Import Divis

6 Easy Steps to learn Naive Bayes algorithm (with code in Python) IntroductionHere's a situation you ' ve got into:You is working on a classification problem and you have generated your set of hypothesis, created features and discussed The importance of variables. Within an hour, stakeholders want to see the first cut of the model.What'll do? You are hunderds of thousands of data points and quite a few vari

This article mainly introduces the longest public substring algorithm in Python, and analyzes the skills of Python string operations. if you need it, refer to the example in this article to describe the longest public substring algorithm in Python. Share it with you for your

Do not feel that there have been six months have not written, time is really easy to neglect, this half a year after a spring festival, to Lhasa Travel. The job is also very busy, did not carry out systematic study and summary.This year began to calm down to start learning from the basic, mainly divided into three parts, algorithm, linear algebra, probability statistics.The first learning algorithm, in

Full code Download: Http://xiazai.bitsCN.com/201407/tools/python-migong.rar Recently, the generation algorithm of the maze was studied, and then a simple online maze game was done. The game address and the corresponding open source project address can be found through the links above. The open source project does not contain the server-side code, because the server-side code is simply too simple. The follo

This article mainly introduces the implementation and simple improvement of the insertion Sorting Algorithm in Python. The worst time complexity of the insertion sorting algorithm is O (n ^ 2 ), the optimal time complexity is O (n). There is a certain amount of optimization space. If you need it, you can refer to the following general example for implementing ins

=self.trace[:, 0]102y2 = self.trace[:, 1]103Plt.plot (x, y1,'R', label='Optimal Value')104Plt.plot (x, Y2,'g', label='Average value') thePlt.xlabel ("Iteration")106Plt.ylabel ("function Value")107Plt.title ("particle Swarm optimization algorithm for function optimization")108 plt.legend ()109Plt.show ()To run the program:1 if __name__ " __main__ " : 2 3 bound = Np.tile ([[ -600], [+]],4 PSO = PSO (max, bound, +, [0.7298, 1.4962, 1.4962

Today we'll talk about some of the classic sorting algorithms, and then use Python to do it, and finally compare the time of several algorithms with dataSelect sortSelect sort (Selection sort) is a simple and intuitive sorting algorithm. It works by selecting the smallest (or largest) element of the data element to be sorted each time, storing it at the beginning of the sequence until all the data elements

Two days before the Spring festival holiday I accidentally saw a * algorithm, it feels very interesting. Just before the holiday did not have anything, it took an afternoon to write the skeleton of the algorithm, and spent half a day to perfect the screen output details and debugging completed.This implementation is just a whim, without considering performance and scalability issues. A friend who is learnin

in this paper, a Python implementation decision tree algorithm is described. Share to everyone for your reference, as follows: From sklearn.feature_extraction import dictvectorizerimport csvfrom sklearn import treefrom sklearn Import Preprocessingfrom sklearn.externals.six Import stringio# reads the CSV data and stores the data and eigenvalues in the Dictionary and class label list allelectronicsdata = op

= new_x" the Print("the false optimal solution X is:", new_x)#It was originally written in new_x, so the result has been wrong . the Print("the false Optimal solution is:", new_y) the thexx = Np.linspace (l_boundary,r_boundary,300) -yy =cal_x2y (XX) inPlt.plot (xx, yy, label='Tuihuo') the #Plt.plot (x2, y2, label= ' Second line ') thePlt.xlabel ('X for Tuihuo')#Horizontal axis Title AboutPlt.ylabel ('Y for Tuihuo')#Ordinate title the #plt.title (' Interesting graph\ncheck it out ', loc= "right

of the parameter model: STEP4, repeat 2nd, 3 steps, until convergence. iii. python program examples This sample program randomly generates 500 2-D data from 4 Gaussian models, real parameters: Mixed term w=[0.1,0.2,0.3,0.4], mean u=[[5,35],[30,40],[20,20],[45,15], and covariance matrix ∑=[[30,0],[ 0,30]]. Then using these data as observation data, we estimate the above parameters according to the EM algorithm