How to use compute_row_using_hamming_distance method in avocado

Best Python code snippet using avocado_python

Cit.py

Source:Cit.py

...35        while self.combination_matrix.total_uncovered != 0:36            if self.combination_matrix.total_uncovered < self.combination_matrix.total_covered_more_than_ones:37                new_row = self.compute_row()38            else:39                new_row = self.compute_row_using_hamming_distance()40            self.combination_matrix.cover_solution_row(new_row)41            self.final_matrix.append(new_row)42        return self.final_matrix43    def compute(self):44        """45        Searching for the best solution. It creates one solution and from that,46        it tries to create smaller solution. This searching process is limited47        by ITERATIONS_SIZE. When ITERATIONS_SIZE is 0 the last found solution is48        the best solution.49        :return: The best solution50        """51        self.final_matrix = self.final_matrix_init()52        matrix = [x[:] for x in self.final_matrix]53        iterations = ITERATIONS_SIZE54        step_size = 155        deleted_rows = []56        while step_size != 0:57            for i in range(step_size):58                delete_row = matrix.pop(random.randint(0, len(matrix) - 1))59                self.combination_matrix.uncover_solution_row(delete_row)60                deleted_rows.append(delete_row)61            LOG.debug("I'm trying solution with size %s and %s iterations",62                      len(matrix), iterations)63            matrix, is_better_solution = self.find_better_solution(iterations, matrix)64            if is_better_solution:65                self.final_matrix = matrix[:]66                deleted_rows = []67                step_size *= 268                LOG.debug("-----solution with size %s was found-----\n",69                          len(matrix))70                iterations = ITERATIONS_SIZE71            else:72                LOG.debug("-----solution with size %s was not found-----\n",73                          len(matrix))74                for i in range(step_size):75                    self.combination_matrix.cover_solution_row(deleted_rows[i])76                    matrix.append(deleted_rows[i])77                if step_size > 1:78                    step_size = 179                else:80                    step_size = 081        return self.final_matrix82    def find_better_solution(self, counter, matrix):83        """84        Changing the matrix to cover all combinations85        :param counter: maximum number of changes in the matrix86        :param matrix: matrix to be changed87        :return: new matrix and is changes have been successful?88        """89        while self.combination_matrix.total_uncovered != 0:90            LOG.debug(self.__throbber.render(), extra={"skip_newline": True})91            solution, row_index, _ = self.use_random_algorithm(matrix)92            if len(solution) != 0:93                self.combination_matrix.uncover_solution_row(matrix[row_index])94                self.combination_matrix.cover_solution_row(solution)95                matrix[row_index] = solution96            if counter == 0:97                return matrix, False98            counter -= 199        return matrix, True100    def use_random_algorithm(self, matrix):101        """102        Applies one of these algorithms to the matrix.103        It chooses algorithm by random in proportion 1:1:8104        :param matrix: matrix to be changed105        :return: new row of matrix, index of row inside matrix and parameters which has been changed106        """107        switch = random.randint(0, 9)108        if switch == 0:109            solution, row_index, parameters = self.change_one_value(matrix)110        elif switch == 1:111            solution, row_index, parameters = self.change_one_column(matrix)112        else:113            solution, row_index, parameters = self.cover_missing_combination(matrix)114        return solution, row_index, parameters115    def compute_row(self):116        """117        Computation of one row which covers most of combinations118        :return: new solution row119        """120        is_valid_row = False121        while not is_valid_row:122            possible_parameters = list(self.combination_matrix.uncovered_rows)123            row = [-1] * len(self.data)124            while len(possible_parameters) != 0:125                # finding uncovered combination126                combination_parameters_index = random.randint(0, len(possible_parameters) - 1)127                combination_parameters = possible_parameters[combination_parameters_index]128                del possible_parameters[combination_parameters_index]129                combination_row = self.combination_matrix.get_row(combination_parameters)130                possible_combinations = list(combination_row.get_all_uncovered_combinations())131                combination_index = random.randint(0, len(possible_combinations) - 1)132                combination = possible_combinations[combination_index]133                is_parameter_used = False134                # Are parameters already used in row?135                for i in combination_parameters:136                    if row[i] != -1:137                        is_parameter_used = True138                        break139                if is_parameter_used:140                    continue141                row_copy = row.copy()142                # Is combination matches the constraints?143                for index, parameter in enumerate(combination_parameters):144                    row_copy[parameter] = combination[index]145                if self.combination_matrix.is_valid_solution(row_copy):146                    row = row_copy147            # Filling in of free spaces inside the row148            for index, r in enumerate(row):149                if r == -1:150                    is_valid = False151                    while not is_valid:152                        row[index] = random.randint(0, self.data[index] - 1)153                        is_valid = self.combination_matrix.is_valid_solution(row)154            is_valid_row = self.combination_matrix.is_valid_solution(row)155        return row156    def cover_missing_combination(self, matrix):157        """158        Randomly finds one missing combination. This combination puts into each159        row of the matrix. The row with the best coverage is the solution160        :param matrix: matrix to be changed161        :return: solution, index of solution inside matrix and parameters which has been changed162        """163        parameters, combination = self.get_missing_combination_random()164        best_uncover = float("inf")165        best_solution = []166        best_row_index = 0167        for row_index in range(len(matrix)):168            solution = [x for x in matrix[row_index]]169            for index, item in enumerate(parameters):170                solution[item] = combination[index]171            if self.combination_matrix.is_valid_combination(solution, parameters):172                self.combination_matrix.uncover_combination(matrix[row_index], parameters)173                self.combination_matrix.cover_combination(solution, parameters)174                if self.combination_matrix.total_uncovered < best_uncover:175                    best_uncover = self.combination_matrix.total_uncovered176                    best_solution = solution177                    best_row_index = row_index178                self.combination_matrix.uncover_combination(solution, parameters)179                self.combination_matrix.cover_combination(matrix[row_index], parameters)180                if best_uncover == 0:181                    break182        return best_solution, best_row_index, parameters183    def get_missing_combination_random(self):184        """185        Randomly finds one missing combination.186        :return: parameter of combination and values of combination187        """188        possible_parameters = list(self.combination_matrix.uncovered_rows)189        combination_parameters_index = random.randint(0, len(possible_parameters) - 1)190        combination_parameters = possible_parameters[combination_parameters_index]191        combination_row = self.combination_matrix.get_row(combination_parameters)192        possible_combinations = list(combination_row.get_all_uncovered_combinations())193        combination_index = random.randint(0, len(possible_combinations) - 1)194        combination = possible_combinations[combination_index]195        return combination_parameters, combination196    def change_one_column(self, matrix):197        """198        Randomly choose one column of the matrix. In each cell of this column199        changes value. The row with the best coverage is the solution.200        :param matrix: matrix to be changed201        :return: solution, index of solution inside matrix and parameters which has been changed202        """203        column_index = random.randint(0, len(self.data) - 1)204        best_uncover = float("inf")205        best_solution = []206        best_row_index = 0207        for row_index in range(len(matrix)):208            try:209                solution, row_index, parameters = self.change_one_value(matrix, row_index, column_index)210            except ValueError:211                continue212            self.combination_matrix.uncover_combination(matrix[row_index], parameters)213            self.combination_matrix.cover_combination(solution, parameters)214            if self.combination_matrix.total_uncovered < best_uncover:215                best_uncover = self.combination_matrix.total_uncovered216                best_solution = solution217                best_row_index = row_index218            self.combination_matrix.uncover_combination(solution, parameters)219            self.combination_matrix.cover_combination(matrix[row_index], parameters)220            if best_uncover == 0:221                break222        return best_solution, best_row_index, [column_index]223    def change_one_value(self, matrix, row_index=None, column_index=None):224        """225        Change one cell inside the matrix226        :param matrix: matrix to be changed227        :param row_index: row inside matrix. If it's None it is chosen randomly228        :param column_index: column inside matrix. If it's None it is chosen randomly229        :return: solution, index of solution inside matrix and parameters which has been changed230        """231        is_cell_chosen = True232        if row_index is None:233            is_cell_chosen = False234            row_index = random.randint(0, len(matrix) - 1)235        row = [x for x in matrix[row_index]]236        if column_index is None:237            is_cell_chosen = False238            column_index = random.randint(0, len(row) - 1)239        possible_numbers = list(range(0, row[column_index])) + list(240            range(row[column_index] + 1, self.data[column_index]))241        row[column_index] = random.choice(possible_numbers)242        while not self.combination_matrix.is_valid_combination(row, [column_index]):243            possible_numbers.remove(row[column_index])244            if len(possible_numbers) == 0:245                if is_cell_chosen:246                    raise ValueError("Selected cell can't be changed")247                column_index = random.randint(0, len(row) - 1)248                row_index = random.randint(0, len(matrix) - 1)249                row = [x for x in matrix[row_index]]250                possible_numbers = list(range(0, row[column_index])) + list(251                    range(row[column_index] + 1, self.data[column_index]))252            row[column_index] = random.choice(possible_numbers)253        return row, row_index, [column_index]254    def compute_row_using_hamming_distance(self):255        """256        :return: row with the biggest hamming distance from final matrix257        """258        row_1 = self.create_random_row_with_constraints()259        row_2 = self.create_random_row_with_constraints()260        if self.compute_hamming_distance(row_1) >= self.compute_hamming_distance(row_2):261            return row_1262        else:263            return row_2264    def compute_hamming_distance(self, row):265        """266        :return: hamming distance of row from final matrix267        """268        distance = 0...

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