... Browse other questions tagged python traveling-salesman or ask your own ... Function to print command-line usage for a program. Mutation is similar to swap operation implemented earlier. This solution is illustrated below and can be shown to be an optimal solution to this problem. The amoeba is known to maximize nutrient acquisition efficiently by deforming its body. The steps required to solve this problem are the same as those used to solve any optimization problem in mlrose. › Python Programming: Using Problem Solving Approach. A common way to visualise searching for solutions in an optimisation problem, such as the TSP, is to think of the solutions existing within a “landscape”. The next code snippet implements the above 2-OPT approximation algorithm. In such a situation, a solution can be represented by a vector of n integers, each in the range 0 to n-1, specifying the order in which the cities should be visited. However, it is also possible to manually define the fitness function object, if so desired. The order in which the cities is specified does not matter (i.e., the distance between cities 1 and 2 is assumed to be the same as the distance between cities 2 and 1), and so each pair of cities need only be included in the list once. Simulated annealing is a local search algorithm that uses decreasing temperature according to a schedule in order to go from more random solutions to more improved solutions. This is the second part in my series on the “travelling salesman problem” (TSP). He is looking for the shortest route going from the origin through all points before going back to the origin city again. Wikipedia conveniently lists the top x biggest cities in the US, so we’ll focus on just the top 25. The aim of this problem is to find the shortest tour of the 8 cities. For example, increasing the maximum number of attempts per step to 100 and increasing the mutation probability to 0.2, yields a tour with a total length of 17.343 units. . The salesman has to travel every city exactly once and return to his own land. #!/usr/bin/env python This Python code is based on Java code by Lee Jacobson found in an article entitled "Applying a genetic algorithm to the travelling salesman problem" The following figure shows the Dynamic programming subproblems, the recurrence relation and the algorithm for TSP with DP. However, by defining the problem this way, we would end up potentially considering invalid “solutions”, which involve us visiting some cities more than once and some not at all. , n}, it will be helpful to notice that there is a natural one-to-one correspondence between integers in the range from 0 and 2^n − 1 and subsets of {0, . In the case of our example, if we choose to specify a list of coordinates, in place of a fitness function object, we can initialize our optimization problem object as: As with manually defining the fitness function object, if both a list of coordinates and a list of distances are specified in initializing the optimization problem object, then the distance list will be ignored. What is the traveling salesman problem? The mutation probability to be used is 0.1. We will use this alternative approach to solve the TSP example given above. import tsp t = tsp.tsp ( [ (0,0), (0,1), (1,0), (1,1)]) print (t) # distance, node index list >>> (4, [0, 1, 3, 2]) mat = [ [ 0, 1, 1, 1.5], [ 1, 0, 1.5, 1], [ 1, 1.5, 0, 1], [1.5, 1, 1, 0]] # Distance Matrix r = range (len (mat)) # Dictionary of distance dist = { (i, j): mat [i] [j] for i in r for j in r} print (tsp.tsp (r, dist)) >>> (4, [0, 1, 3, 2]) If we choose to specify the coordinates, then these should be input as an ordered list of pairs (where pair i specifies the coordinates of city i), as follows: Alternatively, if we choose to specify the distances, then these should be input as a list of triples giving the distances, d, between all pairs of cities, u and v, for which travel is possible, with each triple in the form (u, v, d). The following animation shows how the DP table is computed and the optimal path for Bitonic TSP is constructed. Take a look. Make learning your daily ritual. Here in the following implementation of the above algorithm we shall have the following assumptions: The following animation shows the TSP path computed with GA for 100 points in 2D. 3. (TSP) Consider a salesman who leaves any given location (we’ll say Chicago) and must stop at x other cities before returning home. (TSP) Consider a salesman who leaves any given location (we’ll say Chicago) and must stop at x other cities before returning home. Solving TSP with Integer Linear Program. 3. However, this is not the shortest tour of these cities. The construction heuristics: Nearest-Neighbor, MST, Clarke-Wright, Christofides. Step-by-step modeling and solution of the Traveling Salesman Problem using Python and Pyomo. 5. Notice that in order to represent C(S,i) from the algorithm, the vertices that belong to the set S are colored with red circles, the vertex i where the path that traverses through all the nodes in S ends at is marked with a red double-circle. I love to code in python, because its simply powerful. January 24, 2020 This tutorial will show you how to implement a simulated annealing search algorithm in Python, to find a solution to the traveling salesman problem. What is the shortest possible route that he visits each city exactly once and returns to the origin city? from mip import Model, xsum, minimize, BINARY, # binary variables indicating if arc (i,j) is used, # continuous variable to prevent subtours: each city will have a, # objective function: minimize the distance, Coding in the Abstract: Abstraction and Interface in Java, How to encrypt, password-protect and set restricted permissions on a PDF in Java. Using the distance approach, the fitness function object can be initialized as follows: If both a list of coordinates and a list of distances are specified in initializing the fitness function object, then the distance list will be ignored. If we use the fitness_coords fitness function defined above, we can define an optimization problem object as follows: Alternatively, if we had not previously defined a fitness function (and we wish to use the TravellingSales() class to define the fitness function), then this can be done as part of the optimization problem object initialization step by specifying either a list of coordinates or a list of distances, instead of a fitness function object, similar to what was done when manually initializing the fitness function object. . We will discuss how mlrose can be used to solve this problem next, in our third and final tutorial, which can be found here. If you don't know, The travelling salesman problem is an optimisation problem. Antonio is a fan of Frankenstein, so he … Travelling salesman problem using genetic algorithm in C++. Consider the following map containing 8 cities, numbered 0 to 7. Traveling Salesman Solution. 4. Traveling Salesman Problem(in python) Debugging The code for the traveling salesman problem which is an optimization problem is available on the geek4geek site and works perfectly and prints the least distance possible. In the TSP a salesman is given a list of cities, and the distance between each pair. Travelling Salesman Problem with Code Given a set of cities(nodes), find a minimum weight Hamiltonian Cycle/Tour. This is the second in a series of three tutorials about using mlrose to solve randomized optimization problems. Write python code to solve the following 1. The Traveling Salesman Problem (TSP) is possibly the classic discrete optimization problem. The following python code shows the implementation of the above algorithm with the above assumptions. 6. The travelling salesperson problem (TSP) is a classic optimization problem where the goal is to determine the shortest tour of a collection of n “cities” (i.e. The traveling salesman and 10 lines of Python Update (21 May 18): It turns out this post is one of the top hits on google for “python travelling salesmen”!That means a lot of people who want to solve the travelling salesmen problem in python end up here. Step-by-step modeling and solution of the Traveling Salesman Problem using Python and Pyomo. A preview : How is the TSP problem defined? I have implemented both a brute-force and a heuristic algorithm to solve the travelling salesman problem. nodes), starting and ending in the same city and visiting all of the other cities exactly once. Create the data. Solving the travelling salesman problem using a genetic algorithm. Specificially: Before starting with the example, you will need to import the mlrose and Numpy Python packages. Hence, we want to minimize the value of the fitness function — i.e., less the value of a chromosome, more fit is it to survive. This section presents an example that shows how to solve the Traveling Salesman Problem (TSP) for the locations shown on the map below. Part 1 can be found here and Part 3 can be found here. In this tutorial we introduced the travelling salesperson problem, and discussed how mlrose can be used to efficiently solve this problem. In the TSP a salesman is given a list of cities, and the distance between each pair. , n − 1}: k ↔ {i : i -th bit of k is 1}. This tutorial will show you how to implement a simulated annealing search algorithm in Python, to find a solution to the traveling salesman problem. We shall assume the crossover rate is 1.0, i.e., all individuals in a population participate in crossover. The transposed DP table is shown in the next animation, here the columns correspond to the subset of the vertices and rows correspond to the vertex the TSP ends at. Travelling Salesman Problem (TSP) Using Dynamic Programming Example Problem. The fitness function will be the cost of the TSP path represented by each chromosome. the number of cities to be visited on the tour) and whether our problem is a maximization or a minimization problem. If a fitness function has already been manually defined, as demonstrated in the previous step, then the only additional information required to initialize a TSPOpt() object are the length of the problem (i.e. With each crossover operation between two parent chromosomes, couple of children are generated, cant just swap portions of parents chromosomes, need to be careful to make sure that the offspring represents valid TSP path. Last Updated: 04-11-2020. 3. He wishes to travel keeping the distance as low as possible, so that he could minimize the cost and time factor simultaneously.” The problem seems very interesting. Update (21 May 18): It turns out this post is one of the top hits on google for “python travelling salesmen”! 6. For example, k = 1 (binary 001) corresponds to the set {0}, where k = 5 (binary 101) corresponds to the set {0,2}, In order to find out the integer corresponding to S − {j} (for j ∈ S), we need to flip the j-th bit of k (from 1 to 0). For this, in turn, we can compute a bitwise XOR of k and 2^j (that has 1 only in j-th position). Define an optimization problem object. Edges weights correspond to the cost (e.g., time) to get from one vertex to another one. I have implemented both a brute-force and a heuristic algorithm to solve the travelling salesman problem. For the TSP in the example, the goal is to find the shortest tour of the eight cities. The following sections present programs in Python, C++, Java, and C# that solve the TSP using OR-Tools. [Recall that a discrete-state optimization problem is one where each element of the state vector can only take on a discrete set of values. To learn more about mlrose, visit the GitHub repository for this package, available here. Let us learn how to implement and solve travelling salesman problem in C programming with its explanation, output, disadvantages and much more. As a result, the fitness function should calculate the total length of a given tour. Create the data. Given a graph with weighted edges, you need to find the shortest cycle visiting each vertex exactly once. Travelling Salesman Problem with Code Given a set of cities(nodes), find a minimum weight Hamiltonian Cycle/Tour. What is the traveling salesman problem? I love to code in python, because its simply powerful. The construction heuristics: Nearest-Neighbor, MST, Clarke-Wright, Christofides. The next animation also shows how the DP table gets updated. He is looking for the shortest route going from the origin through all points before going back to the origin city again. DURGESH I Love python, so I like machine learning a Lot and on the other hand, I like building apps and fun games I post blogs on my website for Tech enthusiast to learn and Share Information With The World. In order to complete this code, I made a small program to extract long/lat co-ords from a text file and fill in an adjacency matrix with the cost for each point. The MST is computed with Prim’s algorithm. The steps required to solve this problem are the same as those used to solve any optimization problem in mlrose. In mlrose, these values are assumed to be integers in the range 0 to (max_val -1), where max_val is defined at initialization.]. As a result, if the TravellingSales() class is to be used to define the fitness function object, then this step can be skipped. Classic AI Problems Three of the classic AI problems which will be referred to in this section is the Traveling Salesman problem and the Towers of Hanoi problem and the 8 puzzle. As in the 8-Queens example given in the previous tutorial, this solution can potentially be improved on by tuning the parameters of the optimization algorithm. I preferred to use python as my coding language. . What we know about the problem: NP-Completeness. That means a lot of people who want to solve the travelling salesmen problem in python end up here. Vertices correspond to cities. A traveler needs to visit all the cities from a list, where distances between all the cities are known and each city should be visited just once. The following python code snippet implements the above DP algorithm. tsp is a package for Traveling Salesman Problem for Python. In order to iterate through all subsets of {1, . Code Issues Pull requests Some lecture notes of Operations Research (usually taught in Junior year of BS) can be found in this repository along with some Python programming codes to solve numerous problems of Optimization including Travelling Salesman, Minimum Spanning Tree and so on. Genetic Algorithm; Simulated Annealing; PSO: Particle Swarm Optimization; Divide and conquer; Dynamic Programming; Greedy; Brute Force; When the solution is found it is plotted using Matplotlib and for some algorithms you can see the intermediate results. Part one covered defining the TSP and utility code that will be used for the various optimisation algorithms I shall discuss.. solution landscapes. As mentioned previously, the most efficient approach to solving a TSP in mlrose is to define the optimization problem object using the TSPOpt() optimization problem class. Travelling Salesman Problem (TSP): Given a set of cities and distance between every pair of cities, the problem is to find the shortest p ossible route that visits every city exactly once and returns to the starting point. For each generation we shall keep a constant k=20 (or 30) chromosomes (representing candidate solutions for TSP). The following animation shows how the least cost solution cycle is computed with the DP for a graph with 4 vertices. Given a list of n points, you need to calculate the shortest distance … Press J to jump to the feed. The solution tour found by the algorithm is pictured below and has a total length of 18.896 units. In this post 1, we will go through one of the most famous Operations Research problem, the Traveling Salesman Problem (TSP). Welcome ... Prolog program of Water Jug Problem start with state(0,0) and end with(2,0) ... Python Perl Oracle Software Engineering RDBMS Terms AJAX Framework Design Pattern UML WPF WCF This section presents an example that shows how to solve the Traveling Salesman Problem (TSP) for the locations shown on the map below. . coords_list = [(1, 1), (4, 2), (5, 2), (6, 4), (4, 4), (3, 6). The travelling salesman problem was mathematically formulated in the 1800s by the Irish mathematician W.R. Hamilton and by the British mathematician Thomas Kirkman.Hamilton's icosian game was a recreational puzzle based on finding a Hamiltonian cycle. This is the fitness definition used in mlrose’s pre-defined TravellingSales() class. Skip to main content Switch to mobile version Help the Python Software Foundation raise $60,000 USD by December 31st! Traveling Salesman Planet Edition. The following animations show how the algorithm works: The following animation shows the TSP path computed with SA for 100 points in 2D. ... Browse other questions tagged python traveling-salesman or ask your own ... Function to print command-line usage for a program. I preferred to use python as my coding language. The following animation shows the TSP path computed with the above approximation algorithm and compares with the OPT path computed using ILP for 20 points on 2D plane. Select and run a randomized optimization algorithm. Python & Machine Learning (ML) Projects for ₹1500 - ₹12500. To initialize a fitness function object for the TravellingSales() class, it is necessary to specify either the (x, y) coordinates of all the cities or the distances between each pair of cities for which travel is possible. Solving the travelling salesman problem using a genetic algorithm. Furthermore, if a fitness function object is specified in addition to a list of coordinates and/or a list of distances, then the list of coordinates/distances will be ignored. It also shows the final optimal path. The Traveling Salesman Problem (TSP) is a popular problem and has applications is logistics. Last week, Antonio S. Chinchón made an interesting post showing how to create a traveling salesman portrait in R. Essentially, the idea is to sample a bunch of dark pixels in an image, solve the well-known traveling salesman problem for those pixels, then draw the optimized route between the pixels to create a unique portrait from the image. Here we shall use dynamic programming to solve TSP: instead of solving one problem we will solve a collection of (overlapping) subproblems. Python implementation for TSP using Genetic Algorithms, Simulated Annealing, PSO (Particle Swarm Optimization), Dynamic Programming, Brute Force, Greedy and Divide and Conquer Topics particle-swarm-optimization genetic-algorithms pso tsp algorithms visualizations travelling-salesman-problem simulated-annealing A subproblem refers to a partial solution, A reasonable partial solution in case of TSP is the initial part of a cycle, To continue building a cycle, we need to know the last vertex as well as the set of already visited vertices. prolog travelling salesman problem, Search on prolog travelling salesman problem. The code below creates the data for the problem. We shall use rank selection, i.e., after crossover and mutation, only the top k fittest offspring (i.e., with least fitness function value) will survive for the next generation. We can observe that cost matrix is symmetric that means distance between village 2 to 3 is same as distance between village 3 to 2. Solving with the mip package using the following python … Example visualizations Like any problem, which can be optimized, there must be a cost function. The traveling salesman and 10 lines of Python Update (21 May 18): It turns out this post is one of the top hits on google for “python travelling salesmen”!That means a lot of people who want to solve the travelling salesmen problem in python end up here. What is Travelling Salesman Problem? A salesperson would like to travel to each of these cities, starting and ending in the same city and visiting each of the other cities exactly once. Ask Question Asked 2 years ago. Genetic algorithm for Traveling Salesman. TSP is an NP-hard problem, meaning that, for larger values of n, it is not feasible to evaluate every possible problem solution within a reasonable period of time. The following animation / figure shows the TSP optimal path is computed for increasing number of nodes (where the weights for the input graphs are randomly generated) and the exponential increase in the time taken. Related. Given the solution to the TSP can be represented by a vector of integers in the range 0 to n-1, we could define a discrete-state optimization problem object and use one of mlrose’s randomized optimization algorithms to solve it, as we did for the 8-Queens problem in the previous tutorial. Note the difference between Hamiltonian Cycle and TSP. This time, suppose we wish to use a genetic algorithm with the default parameter settings of a population size (pop_size) of 200, a mutation probability (mutation_prob) of 0.1, a maximum of 10 attempts per step (max_attempts) and no limit on the maximum total number of iteration of the algorithm (max_iters). Solving the Travelling Salesman Problem in Python - GitHub Travelling Salesman problem using GA, mutation, and crossover. The DP table for a graph with 4 nodes will be of size 2⁴ X 4, since there are 2⁴=16 subsets of the vertex set V={0,1,2,3} and a path going through a subset of the vertices in V may end in any of the 4 vertex. problem_no_fit = mlrose.TSPOpt(length = 8, coords = coords_list, The best state found is: [1 3 4 5 6 7 0 2], The fitness at the best state is: 18.8958046604, The best state found is: [7 6 5 4 3 2 1 0], The fitness at the best state is: 17.3426175477, Noam Chomsky on the Future of Deep Learning, An end-to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku, Ten Deep Learning Concepts You Should Know for Data Science Interviews, Kubernetes is deprecating Docker in the upcoming release, Python Alone Won’t Get You a Data Science Job, Top 10 Python GUI Frameworks for Developers. What we know about the problem: NP-Completeness. Simulated annealing is a local search algorithm that uses decreasing temperature according to a schedule in order to go from more random solutions to more improved solutions. The following animation shows how the least cost solution cycle is computed with the DP for a graph with 5 nodes. This is a much more efficient approach to solving TSPs and can be implemented in mlrose using the TSPOpt() optimization problem class. Related. Travelling Salesman Problem (TSP) : Given a set of cities and distances between every pair of cities, the problem is to find the shortest possible route that visits every city exactly once and returns to the starting point. Note the difference between Hamiltonian Cycle and TSP. Solving the Travelling Salesman Problem in Python Implemented techniques. The code below creates the data for the problem. It has shown to find an approximate solution to the traveling salesman problem (TSP), i.e., given a map of a certain number of cities, the problem is to find the shortest route for visiting each city exactly once and returning to the starting city. 2. In order to compute the optimal path along with the cost, we need to maintain back-pointers to store the path. That is, the problem of finding the optimal weights for machine learning models such as neural networks and regression models. The following python code snippet shows how to implement the Simulated Annealing to solve TSP, here G represents the adjacency matrix of the input graph. This is an example of how mlrose caters to solving one very specific type of optimization problem. It will be convenient to assume that vertices are integers from 1 to n and that the salesman starts his trip in (and also returns back to) vertex 1. While I tried to do a good job explaining a simple algorithm for this, it was for a challenge to make a progam in 10 lines of code or fewer. An alternative is to define an optimization problem object that only allows us to consider valid tours of the n cities as potential solutions. The following python code shows an implementation of the above algorithm. The travelling salesman problem follows the approach of the branch and bound algorithm that is one of the different types of algorithms in data structures . Few of the problems discussed here appeared as programming assignments in the Coursera course Advanced Algorithms and Complexity and some of the problem statements are taken from the course. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Wikipedia conveniently lists the top x biggest cities in the US, so we’ll focus on just the top 25. 3 Eight Puzzle Problem Using Python 14. The salesman has to travel every city exactly once and return to his own land. 2-opt algorithm to solve the Travelling Salesman Problem in Python. `tsp` is a package for Traveling Salesman Problem for Python. In this tutorial, we will discuss what is meant by the travelling salesperson problem and step through an example of how mlrose can be used to solve it. Another very specific type of optimization problem mlrose caters to solving is the machine learning weight optimization problem. He wishes to travel keeping the distance as low as possible, so that he could minimize the cost and time factor simultaneously.” The problem seems very interesting. 8. A preview : How is the TSP problem defined? One possible tour of the cities is illustrated below, and could be represented by the solution vector x = [0, 4, 2, 6, 5, 3, 7, 1] (assuming the tour starts and ends at City 0). The Traveling Salesman Problem (TSP) is possibly the classic discrete optimization problem. The following sections present programs in Python, C++, Java, and C# that solve the TSP using OR-Tools. Solving with the mip package using the following python code, produces the output shown by the following animation, for a graph with randomly generated edge-weights. Some vertices may not be connected by an edge in the general case. Like any problem, which can be optimized, there must be a cost function. Select and run a randomized optimization algorithm. Once the optimization object is defined, all that is left to do is to select a randomized optimization algorithm and use it to solve our problem. The Traveling Salesman Problem (TSP) is a popular problem and has applications is logistics. problem_fit = mlrose.TSPOpt(length = 8, fitness_fn = fitness_coords. mlrose provides functionality for implementing some of the most popular randomization and search algorithms, and applying them to a range of different optimization problem domains. Above we can see a complete directed graph and cost matrix which includes distance between each village. If the former is specified, then it is assumed that travel between each pair of cities is possible and that the distance between the pairs of cities is the Euclidean distance. In this blog we shall discuss on the Travelling Salesman Problem (TSP) — a very famous NP-hard problem and will take a few attempts to solve it (either by considering special cases such as Bitonic TSP and solving it efficiently or by using algorithms to improve runtime, e.g., using Dynamic programming, or by using approximation algorithms, e.g., for Metric TSP and heuristics, to obtain not necessarily optimal but good enough solutions, e.g., with Simulated Annealing and Genetic Algorithms) and work on the corresponding python implementations. C++ Program to Solve Travelling Salesman Problem for Unweighted Graph C++ Server Side Programming Programming Travelling Salesman Problem use to calculate the shortest route to cover all the cities and return back to the origin city. In this problem we shall deal with a classical NP-complete problem called Traveling Salesman Problem. Consequently, TSPs are well suited to solving using randomized optimization algorithms. Remark underneath on the off chance that you found any data off base or have questions in regards to Traveling Salesman Problem calculation. Before starting with the example, you will need to import the mlrose and Numpy Python packages. The constraint to prevent the subtours to appear in the solution is necessary, if we run without the constraint, we get a solution with subtours instead of a single cycle going through all the nodes, as shown below: Comparing with Dynamic programming based solution, we can see that ILP is much more efficient for higher n values. 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Implements the above DP algorithm problem defined, find a minimum weight Hamiltonian Cycle/Tour problem in Python GitHub! Shortest route going from the origin city again weights for machine learning models such neural... Specificially: before starting with the DP for a program however, it is also possible to manually the... And ending in the US, so we ’ ll focus on just the top x biggest cities in TSP... Shall discuss.. solution landscapes solving using randomized optimization problems set of cities nodes... The total length of 18.896 units is possibly the classic discrete optimization problem mlrose, the! Traveling Salesman problem calculation in our example, we want to solve a minimization problem an optimisation problem learning ML! To solve randomized optimization algorithms command-line usage for a graph with 5 nodes use this alternative approach solve... Object that only allows US to consider valid tours of the Traveling Salesman problem ( )., n − 1 }: k ↔ { i: i -th bit of k is 1 } k! The TSP using OR-Tools coding language if there exists a tour that every. And solution of the TSP problem defined before going back to the cost ( e.g., time ) to from! Approach to solve the travelling Salesman problem ( TSP ) using Dynamic programming subproblems, the travelling Salesman,! Dp for a program = mlrose.TSPOpt ( length = 8, fitness_fn = fitness_coords like any problem, Search prolog! Output, disadvantages and much more and much more efficient approach to solve the travelling salesmen problem Python! To learn more about mlrose, visit the GitHub repository for this package available... Known to maximize nutrient acquisition efficiently by deforming its body who want to solve randomized optimization problems vertices may be... Mlrose to solve the travelling Salesman problem using Python and Pyomo shortest cycle visiting each vertex once. Solution landscapes above assumptions, mutation, and cutting-edge techniques delivered Monday to Thursday or )... This package, available here cities to be an optimal solution to this problem cities the... In Python, C++, Java, and C # that solve the travelling problem. Number of cities, and C # that solve the TSP problem defined a! Object that only allows US to consider valid tours of the Traveling Salesman.. 2-Opt algorithm to solve this problem cost, we want travelling salesman problem program in python solve the travelling problem! Problem of length 8 up here the total length of a given tour: k ↔ { i: -th. I: i -th bit of k is 1 } k ↔ { i: i -th of... Or 30 ) chromosomes ( representing candidate solutions for TSP ) using Dynamic programming subproblems, goal! From the origin city again with its explanation, output, disadvantages and much more this the... Need to import the mlrose and Numpy Python packages version Help the Python Software raise.

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