How to use initialize_c method in hypothesis

Best Python code snippet using hypothesis

driver.py

Source:driver.py

1import os2import sys3import numpy as np4import queue5from copy import deepcopy6from itertools import product7class Sudoku:8    def __init__(self, initial_config):9        self.board = np.asarray([int(i) for i in initial_config])10        # board = board.reshape(9, 9)11        self.rows = 'ABCDEFGHI'12        self.cols = '123456789'13        self.digits = '123456789'14        self.variables = {}15        # coordinates of cells in grid16        self.grid_coord = product(self.rows, self.cols)17        self.sudoku_board = dict(zip(self.grid_coord, self.board))18        self.rows_all_diff = [p for row in self.rows for p in product(row, self.cols)]19        self.cols_all_diff = [p for col in self.cols for p in product(self.rows, col)]20        self.block_add_diff = [p21                               for i in range(0, 9, 3)22                               for j in range(0, 9, 3)23                               for row in self.rows[i:i + 3]24                               for col in self.cols[j:j + 3]25                               for p in product(row, col)26                               ]27        self.tot_constr = set(self.rows_all_diff + self.cols_all_diff + self.block_add_diff)28        self.units = dict((pos, [u for u in self.tot_constr if u in self.tot_constr]) for pos in self.grid_coord)29class CSP:30    def __init__(self):31        self.V = {}32        self.D = {}33        self.C = {}34        self.A_partial = {}35        self.rows = 'ABCDEFGHI'36        self.cols = '123456789'37        self.digits = '123456789'38        self.grid = ["%s%s" % (row, col) for row, col in product(self.rows, self.cols)]39        self.rows_all_diff = [["%s%s" % (x, y) for x, y in product(row, self.cols)]40                              for row in self.rows]41        self.cols_all_diff = [["%s%s" % (x, y) for x, y in product(self.rows, col)]42                              for col in self.cols]43        self.block_all_diff = [["%s%s" % (row, col) for row in self.rows[i:i + 3] for col in self.cols[j:j + 3]]44                               for j in range(0, 9, 3)45                               for i in range(0, 9, 3)]46        self.total_diff = self.rows_all_diff + self.cols_all_diff + self.block_all_diff47    def add_constraint(self, variable1_key, variable2_key):48        self.C[(variable1_key, variable2_key)] = lambda x, y: x != y49    def get_constraint(self, variable1_key, variable2_key):50        return self.C[(variable1_key, variable2_key)]51    def add_variable(self, variable_key):52        if variable_key not in self.V.keys():53            self.V[variable_key] = set()54            self.D[variable_key] = []55    def add_domain(self, variable, domain: str):56        self.D[variable].append(domain)57    def get_domain(self, variable):58        return self.D[variable]59    def remove_domain(self, variable, domain):60        self.D[variable].remove(domain)61    def get_neighbour(self, Xi: str):  # ,Xj: str):62        for xi, xj in self.C.keys():63            if Xi == xi and xj not in self.V[Xi]:  # and xj not in self.V[Xi]:64                self.V[Xi] = self.V[Xi].union({xj})65        return self.V[Xi]66    def initial_config(self, config):67        for value, variable in zip(config, self.grid):68            self.add_variable(variable)69            self.A_partial[variable] = value70            if value == "0":71                for d in self.digits:72                    self.add_domain(variable, d)73            else:74                self.add_domain(variable, value)75    def initialize_C(self):76        count = 077        if self.V and self.D:78            for i in range(9):79                for j in range(1, 9):80                    for constr in self.total_diff:81                        if self.grid[count] in constr:82                            for c in constr:83                                if c != self.grid[count] \84                                        and (self.grid[count], c) not in self.C.keys() \85                                        and (c, self.grid[count]) not in self.C.keys():86                                    # print("Adding Constraint")87                                    self.add_constraint(self.grid[count], c)88                                    self.add_constraint(c, self.grid[count])89                    count += 190                count += 191    def ac3(self, D):92        q = queue.Queue()93        for arc in self.C.keys():94            q.put(arc)95        i = 096        while not q.empty():97            i += 198            Xi, Xj = q.get()99            # print(Xi, Xj)100            # print("%s before %s"%(Xi, D[Xi]))101            if self.revise(D, Xi, Xj):102                #print("%s after %s"%(Xi, D[Xi]))103                if len(D[Xi]) == 0:104                    return False105                for Xk in (self.get_neighbour(Xi) - {Xj}):106                    q.put((Xk, Xi))107        return True108    def revise(self, D: dict, Xi: str, Xj: str) -> bool:109        revised = False110        for x in D[Xi]:111            consistency = [self.C[(Xi, Xj)](x, y) for y in D[Xj]]112            if not any(consistency):113                D[Xi].remove(x)114                revised = True115        return revised116def back_tracking_search(csp):117    csp_copy = deepcopy(csp)118    csp_copy.ac3(csp_copy.D)119    return recursive_back_tracking({}, csp_copy, 0)120def recursive_back_tracking(assignment, csp, i):121    #print("Backtracking iter %i" % i)122    if test_goal(csp):123        return assignment, csp124    var = select_unassigned(csp.A_partial, csp.D, strategy="mrv")125    for value in csp.D[var]:126        A_ = deepcopy(csp.A_partial)127        D_ = deepcopy(csp.D)128        A_[var] = value129        D_[var] = [value]130        csp2 = deepcopy(csp)131        csp2.A_partial = A_132        csp2.D = D_133        if csp.ac3(D_):134            assignment[var] = value135            result = recursive_back_tracking(assignment, csp2, i + 1)136            if result:137                return result138            assignment[var] = ""139    return False140def select_unassigned(A_partial, D:dict, strategy="mrv"):141    if strategy == "mrv":142        mrv = None143        mrv_val = 10144        for key in A_partial:145            if A_partial[key] == "0":146                if len(D[key]) < mrv_val:147                    mrv = key148        return mrv149    else:150        for key in A_partial:151            if A_partial[key] == "0":152                return key153def complete(A):154    for a in A.keys():155        if A[a] == "0":156            return False157        else:158            return True159def test_goal(csp):160    for c in csp.D.keys():161        if len(csp.D[c]) != 1:162            return False163    return True164def check_sudokus():165    trues = []166    trues_bts = []167    with open("sudokus_start.txt", "r") as file:168        with open("sudokus_finish.txt", "r") as file_output:169            i = 0170            line = file.readline()171            while line:172                line_output = file_output.readline()173                print(i)174                print(line)175                i +=1176                csp = CSP()177                csp_bts = CSP()178                # print(line)179                csp.initial_config(line)180                csp_bts.initial_config(line)181                # print(csp.D)182                csp.initialize_C()183                csp_bts.initialize_C()184                # print(csp.C.keys())185                ac3 = csp.ac3(csp.D)186                all_ones = all([1 if len(csp.D[key]) == 1 else 0 for key in csp.D])187                trues.append(all_ones)188                assignment, csp_bts = back_tracking_search(csp_bts)189                bts_res = [False if int(output)!=int(*d) else True for output, d in zip(line_output, csp_bts.D.values())]190                if all(bts_res):191                    trues_bts.append(True)192                else:193                    trues_bts.append(False)194                print(trues)195                print(trues_bts)196                line = file.readline()197#check_sudokus()198sudoku_input = sys.argv[1]199csp = CSP()200csp.initial_config(sudoku_input)201csp.initialize_C()202csp_ac3 = deepcopy(csp)203ac3 = csp_ac3.ac3(csp_ac3.D)204all_ones = all([1 if len(csp_ac3.D[key]) == 1 else 0 for key in csp_ac3.D])205if os.path.exists("output.txt"):206    print("path exists")207    os.remove("output.txt")208if all_ones:209    with open("output.txt", "a") as file:210        solution = ""211        for value in csp_ac3.D.values():212            solution += "%i"%int(*value)213        solution += " AC3 \n"214        file.write(solution)215assignment, csp = back_tracking_search(csp=csp)216with open("output.txt", "a") as file:217    output = ""218    for o in csp.D.values():219        output += "%i" % int(*o)220    output += " BTS"...

solvers.py

Source:solvers.py

...66        self.__xmax = xmax67        self.__dx = dx68        # +2 to have ghost cells for the boundary conditions69        self.__space_steps = int((self.xmax - self.xmin) // self.dx + 2)70    def initialize_c(self):71        self.__c = np.zeros(shape=(self.space_steps, self.time_steps))72        self.__c[0, 0] = 173    def _create_matrices(self, t_step=0):74        r = self.dt / 2 / self.dx ** 275        self.__r = r76        self.__A = np.zeros(shape=(self.space_steps, self.space_steps))77        self.__B = np.zeros(shape=(self.space_steps, self.space_steps))78        # these two lines set the BC.79        # TODO: Verify these conditions. At the moment should be Dirichelet80        self.__A[0, 0] = 181        self.__A[self.space_steps - 1, self.space_steps - 1] = 182        for i in range(1, self.space_steps - 1):83            d_plus = (self.diff_coeff(self.c[i + 1, t_step]) + self.diff_coeff(self.c[i, t_step])) / 284            d_minus = (self.diff_coeff(self.c[i, t_step]) + self.diff_coeff(self.c[i - 1, t_step])) / 285            self.__A[i, i - 1] = -r * d_minus86            self.__A[i, i] = 1 + r * (d_plus + d_minus)87            self.__A[i, i + 1] = -r * d_plus88        self.__B[0, 0] = 189        self.__B[self.space_steps - 1, self.space_steps - 1] = 190        for i in range(1, self.space_steps - 1):91            d_plus = (self.diff_coeff(self.c[i + 1, t_step]) + self.diff_coeff(self.c[i, t_step])) / 292            d_minus = (self.diff_coeff(self.c[i, t_step]) + self.diff_coeff(self.c[i - 1, t_step])) / 293            self.__B[i, i - 1] = r * d_minus94            self.__B[i, i] = 1 - r * (d_plus + d_minus)95            self.__B[i, i + 1] = r * d_plus96        return self.__A, self.__B97    def solve_diffusion(self):98        for t in tqdm(range(1, self.time_steps), ncols=100):99            A, B = self._create_matrices()100            b = np.dot(B, self.c[:, t - 1])101            c = np.linalg.solve(A, b)102            self.__c[:, t] = c103        return self.c104if __name__ == '__main__':105    sim = Solver1D()106    sim.initialize_space(0, 10, 0.01)107    sim.initialize_time(0, 1, 0.002)108    sim.initialize_c()109    sim.diff_coeff = lambda x: 0.1 * np.exp(-0.1 * x)110    A, B = sim._create_matrices()111    print(sim.r)112    c = sim.solve_diffusion()113    plt.plot(c[1:, ::10])...

pgel_sat.py

Source:pgel_sat.py

...8def is_satisfiable(kb):9    return solve(kb)['satisfiable']10def solve(kb):11    C = initialize_C(kb)12    c = initialize_c(kb)13    d = initialize_d(kb)14    signs = initialize_signs(kb)15    trace(f'C:\n {C}')16    trace(f'c: {c}')17    trace(f'd: {d}')18    trace(f'signs: {signs}')19    lp = linprog.solve(c, C, d, signs)20    trace(str_lp(lp))21    i = 022    iteration_times = []23    while not is_min_cost_zero(lp):24        start = time.time()25        trace(f'\n\niteration: {i}')26        result = generate_column(kb, lp)27        if not result['success']:28            return {'satisfiable': False, 'iterations': i,29                    'iteration_times': iteration_times}30        trace(f'column {result["column"]}')31        column = result['column']32        C = np.column_stack((C, column))33        c = np.append(c, 0)34        lp = linprog.solve(c, C, d, signs)35        trace(str_lp(lp))36        i += 137        end = time.time()38        iteration_times += [end - start]39    assert_result(C @ lp.x, signs, d)40    return {'satisfiable': True, 'lp': lp, 'iterations': i,41            'iteration_times': iteration_times}42def initialize_C(kb):43    C_left = np.identity(kb.n + kb.k + 1)44    C_right = np.vstack((45        - np.identity(kb.n),46        kb.A,47        np.zeros(kb.n)48    ))49    return np.hstack((C_left, C_right))50def initialize_c(kb):51    c_left = np.ones(kb.n + kb.k + 1)52    c_right = np.zeros(kb.n)53    return np.hstack((c_left, c_right))54def initialize_d(kb):55    return np.hstack((np.zeros(kb.n), kb.b, 1))56def initialize_signs(kb):57    return ['==']*kb.n + kb.signs + ['==']58def is_min_cost_zero(lp):59    return isclose(lp.cost, 0, abs_tol=EPSILON)60def get_weights(lp):61    return np.array(lp.y)62def generate_column(kb, lp):63    weights = get_weights(lp)64    trace(f'weights {weights}')...

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