How to use get_possible_values method in avocado

Best Python code snippet using avocado_python

gibbs_fuzzy.py

Source:gibbs_fuzzy.py

...40    if obj_index % 2 == 0:41        cl = [obj_index, obj_index+1]42    else:43        cl = [obj_index-1, obj_index]44    possible_values = get_possible_values(obj_index=obj_index, data=data, g_values=g_values)[0]45    n = len(possible_values) - 146    for v_ind, v in enumerate(possible_values):47        l_p.append(1.)48        for obj in cl:49            obj_s = data[obj][0]50            psi_list = data[obj][1]51            for s, psi_val in zip(obj_s, psi_list):52                accuracy = accuracy_list[s]53                psi_ind = obj_s.index(s)54                g = g_values[obj][1][psi_ind]55                if (obj == obj_index and g == 1) or (obj != obj_index and g == 0):56                    if psi_val == v:57                        l_p[v_ind] *= accuracy58                    else:59                        l_p[v_ind] *= (1-accuracy)/n60    norm_const = sum(l_p)61    for v_ind in range(len(possible_values)):62        l_p[v_ind] /= norm_const63    mult_trial = list(np.random.multinomial(1, l_p, size=1)[0])64    v_new_ind = mult_trial.index(1)65    v_new = possible_values[v_new_ind]66    obj_values.update({obj_index: v_new})67    prob.update({obj_index: l_p})68    for obj in cl:69        obj_s = data[obj][0]70        psi_list = data[obj][1]71        for s, psi_val in zip(obj_s, psi_list):72            psi_ind = obj_s.index(s)73            g = g_values[obj][1][psi_ind]74            if (obj == obj_index and g == 1) or (obj != obj_index and g == 0):75                c_prev = counts[obj][1][psi_ind]76                c_new = 1 if psi_val == v_new else 077                if c_new != c_prev:78                    counts[obj][1][psi_ind] = c_new79def update_g(s, obj_index, g_values, pi_prob, obj_values, accuracy, counts, data):80    l_p = []81    possible_values = [0, 1]82    cluster = obj_index/283    psi_index = data[obj_index][0].index(s)84    psi_val = data[obj_index][1][psi_index]85    g_prev = g_values[obj_index][1][psi_index]86    for g in possible_values:87        pr_pi = pi_prob[cluster]**g*(1-pi_prob[cluster])**(1-g)88        if g == 1:89            g_values[obj_index][1][psi_index] = 190            n = len(get_possible_values(obj_index=obj_index, data=data, g_values=g_values)[0]) - 191            if psi_val == obj_values[obj_index]:92                pr_pi *= accuracy93            else:94                if n == 0:95                    pr_pi *= 0.96                else:97                    pr_pi *= (1-accuracy)/n98        else:99            g_values[obj_index][1][psi_index] = 0100            if obj_index % 2 == 0:101                n = len(get_possible_values(obj_index=obj_index+1, data=data, g_values=g_values)[0]) - 1102                if psi_val == obj_values[obj_index+1]:103                    pr_pi *= accuracy104                else:105                    if n == 0:106                        pr_pi *= 0.107                    else:108                        pr_pi *= (1-accuracy)/n109            else:110                n = len(get_possible_values(obj_index=obj_index-1, data=data, g_values=g_values)[0]) - 1111                if psi_val == obj_values[obj_index-1]:112                    pr_pi *= accuracy113                else:114                    if n == 0:115                        pr_pi *= 0.116                    else:117                        pr_pi *= (1-accuracy)/n118        l_p.append(pr_pi)119    norm_const = sum(l_p)120    if l_p[0] == l_p[1]:121        g_new = np.random.binomial(1, pi_prob[cluster], 1)[0]122    else:123        l_p[0] /= norm_const124        l_p[1] /= norm_const125        g_new = np.random.binomial(1, l_p[1], 1)[0]126    if g_new != g_prev:127        g_values[obj_index][1][psi_index] = g_new128        if g_new == 1:129            if psi_val == obj_values[obj_index]:130                counts[obj_index][1][psi_index] = 1131            else:132                counts[obj_index][1][psi_index] = 0133        else:134            if obj_index % 2 == 0:135                if psi_val == obj_values[obj_index+1]:136                    counts[obj_index][1][psi_index] = 1137                else:138                    counts[obj_index][1][psi_index] = 0139            else:140                if psi_val == obj_values[obj_index-1]:141                    counts[obj_index][1][psi_index] = 1142                else:143                    counts[obj_index][1][psi_index] = 0144    else:145        g_values[obj_index][1][psi_index] = g_prev146def get_pi(cl_ind, g_values, data):147    cl = [cl_ind*2, cl_ind*2+1]148    count_p, count_m = 0, 0149    for obj in cl:150        for g in g_values[obj][1]:151            if g == 1:152                count_p += 1153            else:154                count_m += 1155    pi_new = beta.rvs(count_p + gamma1, count_m + gamma2, size=1)[0]156    return pi_new157def get_a(counts, s_ind):158    count_p, count_m = 0, 0159    for obj in counts.keys():160        sources = counts[obj][0]161        if s_ind not in sources:162            continue163        c_ind = sources.index(s_ind)164        c = counts[obj][1][c_ind]165        if c == 1:166            count_p += 1167        else:168            count_m += 1169    a_new = beta.rvs(count_p + alpha1, count_m + alpha2, size=1)[0]170    return a_new171def get_possible_values(obj_index, data, g_values):172    if obj_index % 2 == 0:173        cl = [obj_index, obj_index+1]174    else:175        cl = [obj_index-1, obj_index]176    possible_values = []177    values = []178    for obj in cl:179        obj_obs = data[obj][1]180        obj_g_values = g_values[obj][1]181        for val, g in zip(obj_obs, obj_g_values):182            if (g == 1 and obj == obj_index) \183                    or (g == 0 and obj != obj_index):184                if val not in possible_values:185                    possible_values.append(val)186                values.append(val)187    possible_values = sorted(possible_values)188    return [possible_values, values]189def get_metrics(data, gt, prob, g_values):190    dist = 0.191    gt_objects = gt.keys()192    norm_const = len(gt_objects)193    pres_count = 0.194    for obj in gt_objects:195        possible_values = get_possible_values(obj_index=obj, data=data, g_values=g_values)[0]196        # this 'if' only for getting accuracy197        if len(possible_values) == 1:198            norm_const -= 1199            continue200        try:201            gt_val_ind = possible_values.index(gt[obj])202        except ValueError:203            norm_const -= 1204            continue205        obj_prob = prob[obj]206        dist += obj_prob[gt_val_ind]207        obj_ind = obj_prob.index(max(obj_prob))208        if gt_val_ind == obj_ind:209            pres_count += 1...

sudokuHS.py

Source:sudokuHS.py

...32                row_values += '%.2f  ' % cell['value']33            print row_values34            row_values = ""35        print ""36def get_possible_values(x,y):37    values = [1,2,3,4,5,6,7,8,9]38    #print values39    # Row40    for cell in sudoku[x]:41        42        if not cell['final']:43            continue44        current_value = cell['value']45        46        if current_value != 0 and current_value in values:47            ##print "index:" + str(values.index(current_value))48            values.remove(current_value)49    #print values50    # Column51    for i in range(9):52        if not sudoku[i][y]['final']:53            continue54        current_value = sudoku[i][y]['value']55        56        if current_value != 0 and current_value in values:57            ##print "index:" + str(values.index(current_value))58            values.remove(current_value)59    60    # Sector61    sector_index_x = x / 3 * 362    sector_index_y = y / 3 * 363    #print values64    for i in range(sector_index_x, sector_index_x + 3):65        for j in range(sector_index_y, sector_index_y + 3):66            67            if not sudoku[i][j]['final']:68                continue69            current_value = sudoku[i][j]['value']70            if current_value != 0 and current_value in values:71                values.remove(current_value)72    #print values73    return values74def choose_cell_randomly():75    """ Get the first available cell """76    cell = {'x': 0, 'y': 0}77    for i in range(9):78        for j in range(9):79            if not sudoku[i][j]["final"]:80                return {'x': i, 'y': j}81    return cell82# Value for first iteration83def calc_value(cell):84    if len(cell['possible_values']) == 1:85        cell['value'] = cell['possible_values'][0]86        cell['final'] = True87        cell['possible_values'] = []88        return cell89    else:90        cell['value'] = min(cell['possible_values']) + HMRC * (max(cell['possible_values']) - min(cell['possible_values']))91        #cell['final'] = False92        #cell['possible_values'] = 93        return cell94def fill_up():95    possible_values = []96    for i in range(len(sudoku)):97        for j in range(len(sudoku[i])):98            if not sudoku[i][j]['final']:99                sudoku[i][j]['possible_values'] = get_possible_values(i, j)100                #print "{0} {1}".format(i, j)101                #print possible_values102                sudoku[i][j] = calc_value(sudoku[i][j])103def new_value(cell, iterations):104    values = [cell['value'], cell['value']]105    values[0] += HMRC * PAR * 0.5 * iterations106    values[1] -= HMRC * PAR * 0.5 * iterations107    if int(values[0]) in cell['possible_values']:108        cell['value'] = values[0]109        return cell110    elif int(values[1]) in cell['possible_values']:111        cell['value'] = values[1]112        return cell113    return cell114# number of iteration over a cell value115def num_iterations(cell):116    return (cell['value'] - cell['possible_values'][0]) / ( HMRC * PAR * .5 )117# Get next value for all cells118def iterate():119    possible_values = []120    for i in range(len(sudoku)):121        for j in range(len(sudoku[i])):122            if not sudoku[i][j]['final']:123                #possible_values = get_possible_values(i, j)124                iterations = num_iterations(sudoku[i][j])125                print sudoku[i][j]['value']126                print iterations127                sudoku[i][j] = new_value(sudoku[i][j], iterations)128                #sudoku[i][j]['possible_values'] = get_possible_values(i, j)129    # Mark new final values130    for i in range(len(sudoku)):131        for j in range(len(sudoku[i])):132            sudoku[i][j]['possible_values'] = get_possible_values(i, j)133def solve():134    display()135    fill_up()136    display()137    iterate()138    display()139def main():140    #display(sudoku)141    ##print get_possible_values(7,0)    142    solve()143if __name__ == '__main__':...

day3p2.py

Source:day3p2.py

...56    """57    Converts binary strings to decimal58    """59    return int(binary_str, 2)60def get_possible_values(filename, bit_str):61    """62    Creates a returns of all the possible lists from the 63    given bit string64    """65    possible_strings = []66    with open(filename) as file:67        for line in file:68            if line.strip()[:len(bit_str)] == bit_str:69                possible_strings.append(line.strip())70    return possible_strings71def oxygen_generator_rating(filename):72    """73    Calculates the oxygen generator rating74    """75    bit_str = ""76    bit_index = 077    bit = get_popular_bit_file(filename, bit_index)78    bit_str += str(bit)79    bit_index += 180    possible_values = get_possible_values(filename, bit_str)81    while len(possible_values) != 1:82        bit = get_popular_bit_list(possible_values, bit_index)83        bit_str += str(bit)84        bit_index += 185        possible_values = get_possible_values(filename, bit_str)86    return possible_values[0]87def co2_scrubber_rating(filename):88    """89    Calculates the CO2 scrubber rating90    """91    bit_str = ""92    bit_index = 093    bit = 094    if get_popular_bit_file(filename, bit_index) == 0:95        bit = 196    bit_str += str(bit)97    bit_index += 198    possible_values = get_possible_values(filename, bit_str)99    while len(possible_values) != 1:100        bit = get_least_popular_bit_list(possible_values, bit_index)101        bit_str += str(bit)102        bit_index += 1103        possible_values = get_possible_values(filename, bit_str)104    return possible_values[0]105def life_support_rating(filename):106    """107    Determines the life support rating after converting the 108    oxygen rating and CO2 scrubber rating from binary to 109    decimal and returns it110    """111    oxygen_rating = binary_to_decimal(oxygen_generator_rating(filename))112    co2_rating = binary_to_decimal(co2_scrubber_rating(filename))113    return oxygen_rating * co2_rating114def main():115    # print(get_popular_bit("day3small.txt", 1)) 116    # print(get_gamma_binary("day3small.txt"))117    # print(get_epsilon_binary("day3small.txt"))118    # print(binary_to_decimal('10110'))119    # print(binary_to_decimal('01001'))120    # print(power_consumption("day3small.txt"))121    # print(power_consumption("day3.txt"))122    # print(get_possible_values("day3small.txt", "10111"))123    # print(oxygen_generator_rating("day3small.txt"))124    # print(co2_scrubber_rating("day3small.txt"))125    print(life_support_rating("day3.txt"))126if __name__ == "__main__":...

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