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18,700 | data reshape(num_points labels reshape(num_points visualize and plot the input data set using the following commandsplt figure(plt scatter(datalabelsplt xlabel('dimension 'plt ylabel('dimension 'plt title('data-points'nowbuild the neural network having two hidden layers with neurolab with ten neurons in the first hidde... |
18,701 | now plot and visualization taskplt figure(plt plot(errorplt xlabel('number of epochs'plt ylabel('error'plt title('training error progress'now we will be plotting the actual versus predicted outputx_dense np linspace(min_valmax_valnum_points y_dense_pred=neural_net sim(x_dense reshape(x_dense size, )reshape(x_dense si z... |
18,702 | |
18,703 | ai with python reinforcement learning in this you will learn in detail about the concepts reinforcement learning in ai with python basics of reinforcement learning this type of learning is used to reinforce or strengthen the network based on critic information that isa network being trained under reinforcement learning... |
18,704 | agent terminology the following terms are more frequently used in reinforcement learning in aiperformance measure of agentit is the criteriawhich determines how successful an agent is behavior of agentit is the action that agent performs after any given sequence of percepts perceptit is agent' perceptual inputs at give... |
18,705 | episodes episodic environments are much simpler because the agent does not need to think ahead constructing an environment with python for building reinforcement learning agentwe will be using the openai gym package which can be installed with the help of the following commandpip install gym there are various environme... |
18,706 | you can construct other environments in similar way constructing learning agent with python for building reinforcement learning agentwe will be using the openai gym package as shownimport gym env gym make('cartpole- 'for in range( )observation env reset(for in range( )env render(print(observationaction env action_space... |
18,707 | ai with python genetic algorithms this discusses genetic algorithms of ai in detail what are genetic algorithmsgenetic algorithms (gasare search based algorithms based on the concepts of natural selection and genetics gas are subset of much larger branch of computation known as evolutionary computation gas were develop... |
18,708 | step nowif the stop condition is metreturn the solution with their best fitness value else go to step installing necessary packages for solving the problem by using genetic algorithms in pythonwe are going to use powerful package for ga called deap it is library of novel evolutionary computation framework for rapid pro... |
18,709 | toolbox register("individual"tools initrepeatcreator individualtoolbox attr_boolnum_bitstoolbox register("population"tools initrepeatlisttoolbox individualregister the evaluation operatortoolbox register("evaluate"eval_funcnowregister the crossover operatortoolbox register("mate"tools cxtwopointregister mutation operat... |
18,710 | selecting the next generation individualsoffspring toolbox select(populationlen(population)nowclone the selected individualsoffspring list(map(toolbox cloneoffspring)apply crossover and mutation on the offspringfor child child in zip(offspring[:: ]offspring[ :: ])if random random(probab_crossingtoolbox mate(child child... |
18,711 | sum sum( * for in fitsstd abs(sum length mean** )** print('min ='min(fits)'max ='max(fits)print('average ='round(mean )'standard deviation ='round(std )print("\nevolution ends"print the final outputbest_ind tools selbest(population )[ print('\nbest individual:\ 'best_indprint('\nnumber of ones:'sum(best_ind)following w... |
18,712 | symbol regression problem it is one of the best known problems in genetic programming all symbolic regression problems use an arbitrary data distributionand try to fit the most accurate data with symbolic formula usuallya measure like the rmse (root mean square erroris used to measure an individual' fitness it is class... |
18,713 | toolbox register("individual"tools inititeratecreator individualtoolbox exprtoolbox register("population",tools initrepeat,listtoolbox individualtoolbox register("compile"gp compilepset=psettoolbox register("evaluate"eval_funcpoints=[ / for in range( , )]toolbox register("select"tools seltournamenttournsize= toolbox re... |
18,714 | ai with python computer vision computer vision is concerned with modeling and replicating human vision using computer software and hardware in this you will learn in detail about this computer vision computer vision is discipline that studies how to reconstructinterrupt and understand scene from its imagesin terms of t... |
18,715 | medicine classification and detection ( lesion or cells classification and tumor detection / segmentation human organ reconstruction (mri or ultrasoundvision-guided robotics surgery security biometrics (irisfinger printface recognitionsurveillance-detecting certain suspicious activities or behaviors transportation auto... |
18,716 | readingwriting and displaying an image most of the cv applications need to get the images as input and produce the images as output in this sectionyou will learn how to read and write image file with the help of functions provided by opencv opencv functions for readingshowingwriting an image file opencv provides the fo... |
18,717 | nowwe can write the same image into the other formatsay png by using the imwrite(functioncv imwrite('image_flower png',imagethe output true means that the image has been successfully written as png file also in the same folder true notethe function destroyallwindows(simply destroys all the windows we created color spac... |
18,718 | cv imshow('bgr_penguins',imagenowuse cvtcolor(function to convert this image to grayscale image cv cvtcolor(image,cv color_bgr graycv imshow('gray_penguins',image |
18,719 | edge detection humansafter seeing rough sketchcan easily recognize many object types and their poses that is why edges play an important role in the life of humans as well as in the applications of computer vision opencv provides very simple and useful function called canny()for detecting the edges example the followin... |
18,720 | face detection face detection is one of the fascinating applications of computer vision which makes it more realistic as well as futuristic opencv has built-in facility to perform face detection we are going to use the haar cascade classifier for face detection haar cascade data we need data to use the haar cascade cla... |
18,721 | nowusing face_detection detectmultiscaleperform actual face detection faces face_detection detectmultiscale(gray nowdraw rectangle around the whole facefor ( , , ,hin facesimg cv rectangle(img,( , ),( +wy+ ),( , , ), cv imwrite('face_ab jpg',imgthis python program will create an image named face_ab jpg with face detect... |
18,722 | import opencv package as shownimport cv import numpy as np nowuse the haarcascadeclassifier for detecting faceeye_cascade cv cascadeclassifier(' :/programdata/cascadeclassifier/haarcascade_eye xml'nowfor reading particular imageuse the imread(functionimg cv imread('ab_eye jpg'nowconvert it into grayscale because it wou... |
18,723 | artificial neural network (annit is an efficient computing systemwhose central theme is borrowed from the analogy of biological neural networks neural networks are one type of model for machine learning in the mid- and early smuch important architectural advancements were made in neural networks in this you will learn ... |
18,724 | ignore the structure of input data and all the data is converted into - array before feeding it into the network this process suits the regular datahowever if the data contains imagesthe process may be cumbersome cnn solves this problem easily it takes the structure of the images into account when they process themwhic... |
18,725 | contents tabular factors graphical models example belief networks inference methods recursive conditioning variable elimination stochastic simulation sampling from discrete distribution sampling methods for belief network inference rejection sampling likelihood weighting particle filtering examples gibbs sampling plott... |
18,726 | learning testing -learning -leaning with experience replay model-based reinforcement learner reinforcement learning with features representing features feature-based rl learner experience replay multiagent systems minimax creating two-player game minimax and - pruning multiagent learning relational learning collaborati... |
18,727 | python for artificial intelligence why pythonwe use python because python programs can be close to pseudo-code it is designed for humans to read python is reasonably efficient efficiency is usually not problem for small examples if your python code is not efficient enougha general procedure to improve it is to find out... |
18,728 | python for artificial intelligence to upgrade matplotlib to the latest version (which you should do if you install new version of pythondopip install --upgrade matplotlib we recommend using the enhanced interactive python ipython (ipython org/to install ipython after you have installed python dopip install ipython runn... |
18,729 | in [ ]you can then interact at the last prompt there are many textbooks for python the best source of information about python is the rest of this is about what is special about the code for ai tools we will only use the standard python library and matplotlib all of the exercises can be done (and should be donewithout ... |
18,730 | python for artificial intelligence is an expression that evaluates to either true or false for each eand fe is an expression that will be evaluated for each value of for which cond returns true the result can go in list or used in another iterationor can be called directly using next the procedure next takes an iterato... |
18,731 | callednot the value of the variable when the function was defined (this is called "late binding"this means if you want to use the value variable has when the function is createdyou need to save the current value of that variable whereas python uses "late bindingby defaultthe alternative that newcomers often expect is "... |
18,732 | python for artificial intelligence and fun list are equivalent to the first two (except fun list uses different variableone of the advantages of using the embedded definitions (as in fun and fun aboveover the lambda is that is it possible to add __doc__ stringwhich is the standard for documenting functions in pythonto ... |
18,733 | the sequence of next( )and list(agives exactly the same results as the comprehension in section it is straightforward to write version of the built-in enumerate let' call it myenumeratepythondemo py -(continued def myenumerate(enum)for in range(len(enum))yield ,enum[iexercise write version of enumerate where the only i... |
18,734 | python for artificial intelligence plottingmatplotlib the standard plotting for python is matplotlib (will use the most basic plotting using the pyplot interface here is simple example that uses everything we will use pythondemo py -(continued import matplotlib pyplot as plt def myplot(minv,maxv,step,fun ,fun )plt ion(... |
18,735 | override the definition of display (but we leave it as projectthe method self display is used to trace the program any call self display(levelto print where the level is less than or equal to the value for max display level will be printed the to print can be anything that is accepted by the built-in print (including a... |
18,736 | python for artificial intelligence in order to implement more sophisticated visualizations of the algorithmwe add visualize "decoratorto the methods to be visualized the following code ignores the decoratordisplay py -(continued def visualize(func)""" decorator for algorithms that do interactive visualization ignored h... |
18,737 | argmax([ , , , , , , ] def argmaxd(dct)"""returns the arx max of dictionary dct""return argmaxe(dct items()tryarxmaxd({ : , : , : }exercise change argmax to have an optional argument that specifies whether you want the "first""lastor "randomindex of the maximum value returned if you want the first or the lastyou don' n... |
18,738 | python for artificial intelligence the following code tests argmax and dict_unionbut only when if utilities is loaded in the top-level if it is loaded in module the test code is not run in your code you should do more substantial testing than we do herein particular testing the boundary cases utilities py -(continued d... |
18,739 | agents and control this implements the controllers described in in this version the higher-levels call the lower-levels more sophisticated version may have them run concurrently (either as coroutines or in parallelthe higher-levels calling the lower-level works in simulated environments when there is single agentand wh... |
18,740 | agents and control """set up the agent""self env=env def go(self, )"""acts for time steps""raise notimplementederror("go"abstract method the environment implements do(actionmethod where action is variablevalue dictionary this returns perceptwhich is also variable-value dictionary the use of dictionaries allows for stru... |
18,741 | prices [ max_price_addon maximum of random value added to get price def __init__(self)"""paper buying agent""self time= self stock= self stock_history [memory of the stock history self price_history [memory of the price history def initial_percepts(self)"""return initial percepts""self stock_history append(self stockpr... |
18,742 | agents and control return it elseranreal -prob raise runtimeerror(str(item_prob_dist)+is not probability distribution"the agent the agent does not have access to the price model but can only observe the current price and the amount in stock it has to decide how much to buy the belief state of the agent is an estimate o... |
18,743 | plotting the following plots the price and number in stock historyagents py -(continued import matplotlib pyplot as plt class plot_prices(object)"""set up the plot for history of price and number in stock""def __init__(selfag,env)self ag ag self env env plt ion(plt xlabel("time"plt ylabel("number in stock price " def p... |
18,744 | agents and control import math from agents import environment class rob_env(environment)def __init__(self,walls {})"""walls is set of line segments where each line segment is of the form (( , ),( , )""self walls walls body the body defines everything about the agent body agentenv py -(continued import math from agents ... |
18,745 | direction is 'left''rightor 'straight""if self crashedreturn self percepts(direction action['steer'compass_deriv {'left': ,'straight': ,'right':- }[direction]*self turning_angle self rob_dir (self rob_dir compass_deriv + )% make in range [ , rob_x_new self rob_x math cos(self rob_dir*math pi/ rob_y_new self rob_y math ... |
18,746 | agents and control ""(( , ),( , )linea (( , ),( , )lineb dadb - ax - eaeb - ay - denom db*ea-eb*da if denom== line segments are parallel return false cb (da*( - )-ea*( - ))/denom position along line if cb return false ca (db*( - )-eb*( - ))/denom position along line return <=ca<= test casesassert line_segments_intersec... |
18,747 | ""if 'timeoutin actionremaining action['timeout'elseremaining - will never reach target_pos action['go_to'arrived self close_enough(target_poswhile not arrived and remaining ! self percepts self env do({"steer":self steer(target_pos)}remaining - arrived self close_enough(target_posreturn {'arrived':arrivedthis determin... |
18,748 | agents and control from agents import environment class rob_top_layer(environment)def __init__(selfmiddletimeout= locations {'mail':(- , )' ':( , )' ':( , ),'storage':( , ))"""middle is the middle layer timeout is the number of steps the middle layer goes before giving up locations is loc:pos dictionary where loc is na... |
18,749 | plt plot([ , ],[ , ],"- ",linewidth= for loc in top locations( ,ytop locations[locplt plot([ ],[ ]," <"plt text( + , + ,locprint the label above and to the right plt plot([body rob_x],[body rob_y],"go"plt draw( def plot_run(self)"""plots the history after the agent has finished this is typically only used if body plott... |
18,750 | agents and control pl=plot_env(trap_body,trap_toptrap_top do({'visit':['goal']}version june |
18,751 | searching for solutions representing search problems search problem consists ofa start node neighbors function that given nodereturns an enumeration of the arcs from the node specification of goal in terms of boolean function that takes node and returns true if the node is goal (optionalheuristic function thatgiven nod... |
18,752 | searching for solutions the methods must be overridden to define search problem "" def start_node(self)"""returns start node""raise notimplementederror("start_node"abstract method def is_goal(self,node)"""is true if node is goal""raise notimplementederror("is_goal"abstract method def neighbors(self,node)"""returns list... |
18,753 | list or set of nodes list or set of arcs start node list or set of goal nodes (optionallya dictionary that maps node to heuristic value for that node to define search problemwe need to define the start nodethe goal predicatethe neighbors function and the heuristic function searchproblem py -(continued class search_prob... |
18,754 | searching for solutions def heuristic(self,node)"""gives the heuristic value of node returns if not overridden in the hmap ""if node in self hmapreturn self hmap[nodeelsereturn def __repr__(self)"""returns string representation of the search problem""res="for arc in self arcsres +str(arc)+return res the following is us... |
18,755 | self arc=arc if arc is noneself cost= elseself cost initial cost+arc cost def end(self)"""returns the node at the end of the path""if self arc is nonereturn self initial elsereturn self arc to_node def nodes(self)"""enumerates the nodes for the path this starts at the end and enumerates nodes in the path backwards ""cu... |
18,756 | searching for solutions figure problem figure problem arc(' ',' ', )arc(' ',' ', )arc(' ',' ', )]start ' 'goals {' '}positions={' '( )' '( )' '( , )' '( , )' '( , )}the second search problem is one with nodes where many paths do not lead to the goal see figure searchproblem py -(continued problem search_problem_from_ex... |
18,757 | searchproblem py -(continued problem search_problem_from_explicit_graph{' ',' ',' ',' ',' ',' ',' ',' '}[]start ' 'goals {' ',' '}the acyclic delivery problem is the delivery problem described in example and shown in figure of the textbook searchproblem py -(continued acyclic_delivery_problem search_problem_from_explic... |
18,758 | searching for solutions ' 'storage the cyclic delivery problem is the delivery problem described in example and shown in figure of the textbook this is the same as acyclic delivery problembut almost every arc also has its inverse searchproblem py -(continued cyclic_delivery_problem search_problem_from_explicit_graph{'m... |
18,759 | 'storage generic searcher and variants to run the search demosin folder "aipython"load "searchgeneric pyusing ipython - searchgeneric pyand copy and paste the example queries at the bottom of that file this requires python searcher searcher for problem can be asked repeatedly for the next path to solve problemwe can co... |
18,760 | searching for solutions returns none if no path exists ""while not self empty_frontier()path self frontier pop(self display( "expanding:",path,"(cost:",path cost,")"self num_expanded + if self problem is_goal(path end())solution found self display( self num_expanded"paths have been expanded and"len(self frontier)"paths... |
18,761 | the variable frontier index is the total number of elements of the frontier that have been created as well as being used as unique indexit is useful for statisticsparticularly in conjunction with the current size of the frontier searchgeneric py -(continued import heapq part of the python standard library from searchpr... |
18,762 | searching for solutions def __len__(self)"""length of the frontier""return len(self frontierpq def __iter__(self)"""iterate through the paths in the frontier""for ( , ,pathin self frontierpqyield path asearch for an asearch the frontier is implemented using the frontierpq class searchgeneric py -(continued class astars... |
18,763 | print("path found:",path assert path is not none"no path is found in problem assert list(path nodes()in solutions"shortest path not found in problem print("passed unit test" if __name__ ="__main__"#test(searchertest(astarsearcher example queriessearcher searcher(searchproblem acyclic_delivery_problemdfs searcher search... |
18,764 | searching for solutions class searchermpp(astarsearcher)"""returns searcher for problem paths can be found by repeatedly calling search(""def __init__(selfproblem)super(__init__(problemself explored set( @visualize def search(self)"""returns next path from an element of problem' start nodes to goal node returns none if... |
18,765 | pruning and cycle pruning for the cyclic delivery problem which works better in terms of number of paths expandedcomputational time or space branch-and-bound search to run the demoin folder "aipython"load "searchbranchandbound py"and copy and paste the example queries at the bottom of that file depth-first search metho... |
18,766 | searching for solutions self display( ,"expanding:",path,"cost:",path costself num_expanded + if self problem is_goal(path end())self best_path path self bound path cost self display( ,"new best path:",path,cost:",path costelseneighs self problem neighbors(path end()self display( ,"neighbors are"neighsfor arc in revers... |
18,767 | from searchgeneric import searcherastarsearcher from searchbranchandbound import df_branch_and_bound from searchmpp import searchermpp df_branch_and_bound max_display_level searcher max_display_level def run(problem,name)print("\ \ *******",name print("\na*:"asearcher astarsearcher(problemprint("path found:",asearcher ... |
18,768 | searching for solutions also test some graphs with cyclesand some with multiple least-cost paths version june |
18,769 | reasoning with constraints constraint satisfaction problems variables variable consists of namea domain and an optional ( ,yposition (for displayingthe domain of variable is list or tupleas the ordering will matter in the representation of constraints cspproblem py -representations of constraint satisfaction problem im... |
18,770 | reasoning with constraints def __str__(self)return self name def __repr__(self)return self name "variable({self name})constraints constraint consists ofa tuple (or listof variables is called the scope condition is boolean function that takes the same number of arguments as there are variables in the scope the condition... |
18,771 | not all variables in the scope of con are assigned in the assignment it ignores variables in assignment that are not in the scope of the constraint in pythonthe notation is used for unpacking tuple for examplef(*( )is the same as ( so if has value ( )then (*tis the same as ( cspproblem py -(continued def can_evaluate(s... |
18,772 | reasoning with constraints ""self title title self variables variables self constraints constraints self var_to_const {var:set(for var in self variablesfor con in constraintsfor var in con scopeself var_to_const[varadd(con def __str__(self)"""string representation of csp""return str(self title def __repr__(self)"""more... |
18,773 | for in range( )bbox dict(boxstyle="square,pad= ",color="green"for var in con scopeax annotate(con stringvar positionxytext=con positionarrowprops={'arrowstyle':'-'},bbox=con_bboxha='center'for var in self variablesx, var position plt text( , ,var name,bbox=var_bbox,ha='center'examples in the following code ne when give... |
18,774 | reasoning with constraints csp ! < < figure csp show( variable(' '{ , , }csp csp("csp "{ , , }constraint([ , ],lt)constraint([ , ],lt)]the cspcsp has variables ab and ceach with domain { the constraints are bb and this is slightly more interesting than csp as it has more solutions this example is used in the unit tests... |
18,775 | csp ! = ! ! > ! > < > > ! figure csp show( constraint([ , ]ne" ! ")constraint([ , ]ne" ! ")constraint([ , ]lt" ")constraint([ , ]eq" ")constraint([ , ]gt" ")constraint([ , ]gt" ")constraint([ , ]gt" ")constraint([ , ]gt" ")constraint([ , ]ne" ! ")]the following example is another scheduling problem (but with multiple a... |
18,776 | reasoning with constraints csp ! < - is odd < < ! ! figure csp show(the following example is another abstract scheduling problem what are the solutionscspexamples py -(continued def adjacent( , )"""true when and are adjacent numbers""return abs( - = csp csp("csp "{ , , , , }[constraint([ , ]adjacent"adjacent( , )")cons... |
18,777 | csp adjacent( ,bb ! ! adjacent( ,cadjacent( ,dadjacent( ,ec ! figure csp show( wordsantbigbuscarhasbookbuysholdlaneyeargingersearchsymbolsyntax figure crossword crossword puzzle to be solved version june |
18,778 | reasoning with constraints crossword one_across meet_at( , )[one_acrossone_downmeet_at( , )[one_acrosstwo_downtwo_down one_down meet_at( , )[three_acrossone_downmeet_at( , )[three_acrosstwo_downmeet_at( , )[four_acrosstwo_downthree_across four_across figure crossword show(means that the third letter (at position of the... |
18,779 | crossword is_word[ is_word[ is_word[ is_word[ is_word[ figure crossword show( crossword csp("crossword "{one_acrossone_downtwo_downthree_acrossfour_across}[constraint([one_across,one_down]meet_at( , ))constraint([one_across,two_down]meet_at( , ))constraint([three_across,two_down]meet_at( , ))constraint([three_across,on... |
18,780 | reasoning with constraints " " pij is the variable representing the letter from the left and down (starting from variable(' 'lettersposition=( , ) variable(' 'lettersposition=( , ) variable(' 'lettersposition=( , ) variable(' 'lettersposition=( , ) variable(' 'lettersposition=( , ) variable(' 'lettersposition=( , ) var... |
18,781 | here is representation of the -queens problemwhere the variables are the columns and the values are the rows in which the queen is placed the original queens problem on standard ( chess board is n_queens( cspexamples py -(continued def queens(ri,rj)"""ri and rj are different rowsreturn the condition that the queens can... |
18,782 | reasoning with constraints exercise propose test that is appropriate for csps with no solutions assume that the test designer knows there are no solutions consider what csp solver should return if there are no solutions to the csp exercise write unit test that checks whether all solutions ( for the search algorithms th... |
18,783 | return next(genexcept stopiterationreturn none if __name__ ="__main__"test_csp(dfs_solve #trydfs_solve_all(csp dfs_solve_all(csp dfs_solve_all(crossword dfs_solve_all(crossword dwarningmay take *verylong timeexercise instead of testing all constraints at every nodechange it so each constraint is only tested when all of... |
18,784 | reasoning with constraints cspsearch py -representations of search problem from csp from cspproblem import cspconstraint from searchproblem import arcsearch_problem from utilities import dict_union class search_from_csp(search_problem)""" search problem directly from the csp node is variable:value dictionary""def __ini... |
18,785 | from searchgeneric import searcher def solver_from_searcher(csp)"""depth-first search solver""path searcher(search_from_csp(csp)search(if path is not nonereturn path end(elsereturn none if __name__ ="__main__"test_csp(solver_from_searcher #test solving csps with searchsearcher searcher(search_from_csp(csp )#print(searc... |
18,786 | reasoning with constraints kwargs is the keyword arguments for displayable superclass ""self csp csp super(__init__(**kwargsor displayable __init__(self,**kwargsthe following implementation of arc consistency maintains the set to do of (variableconstraintpairs that are to be checked it takes in domain dictionary and re... |
18,787 | variable var in constraint const ""return {(nvarnconstfor nconst in self csp var_to_const[varif nconst !const for nvar in nconst scope if nvar !varthe following selects an arc any element of to do can be selected the selected element needs to be removed from to do the default implementation just selects which ever elem... |
18,788 | reasoning with constraints def any_holds(selfdomainsconstenvother_varsind= )"""returns true if constraint const holds for an assignment that extends env with the variables in other_vars[ind:env is dictionary warningthis has side effects and changes the elements of env ""if ind =len(other_vars)return const holds(envelse... |
18,789 | def select_var(selfiter_vars)"""return the next variable to split""return select(iter_vars def partition_domain(dom)"""partitions domain dom into two ""split len(dom/ dom set(list(dom)[:split]dom dom dom return dom dom the domains are implemented as dictionary that maps each variables to its domain assigning value in p... |
18,790 | reasoning with constraints cspconsistency py -(continued from cspexamples import test_csp def ac_solver(csp)"arc consistency (solve_one)return con_solver(cspsolve_one( if __name__ ="__main__"test_csp(ac_solverdomain splitting as an interface to graph searching an alternative implementation is to implement domain splitt... |
18,791 | self display( ,",var,"in",dom,"has no solution"return neighs exercise when splitting domainthis code splits the domain into halfapproximately in half (without any effort to make sensible choicedoes it work better to split one element from domainunit testcspconsistency py -(continued from cspexamples import test_csp fro... |
18,792 | reasoning with constraints given cspthe stochastic local searcher (slsearchercreates the data structuresvariables to select is the set of all of the variables with domain-size greater than one for variable not in this setwe cannot pick another value from that variable var to constraints maps from variable into the set ... |
18,793 | it must create one note thatwhen counting stepsa restart is counted as one step this method selects one of two implementations the argument pob best is the probability of selecting best variable (one involving the most conflictswhen the value of prob best is positivethe algorithm needs to maintain priority queue of var... |
18,794 | reasoning with constraints steps (corresponding to existing max stepsand the maximum number of restartsand returns the total number of steps for the first solution found (as in searchthe solution found can be extracted from the variable self current assignmentany-conflict if the probability of picking best variable is ... |
18,795 | the number of conflicts by the most have parameter that specifies the probability that the best value is chosen two-stage choice this is the top-level searching algorithm that maintains priority queue of variables ordered by (the negative ofthe number of conflictsso that the variable with the most conflicts is selected... |
18,796 | reasoning with constraints var_differential[vvar_differential get( , )- elseif varcon not in self conflictswas consisnot now self display( ,"became inconsistent",varconself conflicts add(varconfor in varcon scopev is in one more conflicts var_differential[vvar_differential get( , )+ self variable_pq update_each_priorit... |
18,797 | exercise this makes no attempt to find the best alternative value for variable modify the code so that after selecting variable it selects value the reduces the number of conflicts by the most have parameter that specifies the probability that the best value is chosen exercise these implementations always select value ... |
18,798 | reasoning with constraints def remove(self,elt)"""remove the element from the priority queue""if elt in self elt_mapself elt_map[elt][ self removed del self elt_map[elt def update_each_priority(self,update_dict)"""update values in the priority queue by subtracting the values in update_dict from the priority of those el... |
18,799 | plotting runtime distributions runtime distribution uses matplotlib to plot runtime distributions here the runtime is misnomer as we are only plotting the number of stepsnot the time computing the runtime is non-trivial as many of the runs have very short runtime to compute the time accurately would require running the... |
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