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How to use column_strategy method in pandera

Best Python code snippet using pandera_python

week1.py

Source:week1.py

1# @author: FrantiÅ¡ek DostÃ¡l2import numpy as np3# COLUMN = minimizing4# ROW = maximizing5def evaluate(matrix_r, matrix_c, row_strategy, column_strategy):6    return row_strategy @ matrix_r @ column_strategy, row_strategy @ matrix_c @ column_strategy7def evaluate_zero_sum(matrix, row_strategy, column_strategy):8    return row_strategy @ matrix @ column_strategy9#algorithms for zero sum games10#opponents best response, not equilibrial response11def best_response_calc_row(matrix, row_strategy):12    return np.argmin((row_strategy @ matrix))13#opponents best response to given strategy, not equilibrial response14def best_response_calc_column(matrix, column_strategy):15    return np.argmin(-( matrix @ column_strategy))16def best_response_value_column(matrix, column_strategy):17    #column value against best responding opponent18    return np.min(-matrix @ column_strategy)19def best_response_value_row(matrix, row_strategy):20    #row value against best responding opponent21    return np.min(row_strategy @ matrix)22def strongly_dominated_row(matrix):23    for index in range(matrix.shape[0]):24        c = np.delete(matrix, index, 0)25        if np.any(np.all(c > matrix[index], axis=1)):26            return index27    return None28def strongly_dominated_column(matrix):29    for index in range(matrix.shape[1]):30        c = np.delete(matrix, index, 1)31        if np.any(np.all((c.transpose() < matrix[:, index]).transpose(), axis=0)):32            return index33    return None34def strongly_dominated_rows(matrix):35    #generator version36    M = matrix.copy()37    for index in range(M.shape[0]):38        c = np.delete(M, index, 0)39        if np.any(np.all(c > M[index], axis=1)):40            yield index41def strongly_dominated_columns(matrix):42    #generator version43    M = matrix.copy()44    for index in range(M.shape[1]):45        c = np.delete(M, index, 1)46        if np.any(np.all((c.transpose() < M[:, index]).transpose(), axis=0)):47            yield index48def pruning_dominated(matrix):49    M=matrix.copy()50    dominated_row = strongly_dominated_row(matrix)51    dominated_column = strongly_dominated_column(matrix)52    while dominated_column is not None or dominated_row is not None:53        if dominated_row is not None: M = np.delete(M, dominated_row, 0)54        if dominated_column is not None: M = np.delete(M, dominated_column, 1)55        dominated_row = strongly_dominated_row(M)56        dominated_column = strongly_dominated_column(M)...

test_week1.py

Source:test_week1.py

1import libs.week1 as week12import numpy as np3import pytest4def test_week1():5    matrix = np.array([[0, 1, -1], [-1, 0, 1], [1, -1, 0]])6    row_strategy = np.array([[0.1, 0.2, 0.7]])7    column_strategy = np.array([[0.3, 0.2, 0.5]]).transpose()8    row_value = week1.evaluate(matrix=matrix, row_strategy=row_strategy, column_strategy=column_strategy)9    assert row_value == pytest.approx(0.08)10    br_value_row = week1.best_response_value_row(matrix=matrix, row_strategy=row_strategy)11    br_value_column = week1.best_response_value_column(matrix=matrix, column_strategy=column_strategy)12    assert br_value_row == pytest.approx(-0.6)...

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