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How to use init_a method in hypothesis

Best Python code snippet using hypothesis

lopt.py

Source:lopt.py

1from multiprocessing import Pool2from indigox.config import INIT_WITH_GA, NUM_PROCESSES3from indigox.exception import IndigoUnfeasibleComputation4from indigox.misc import (BondOrderAssignment, graph_to_dist_graph, electron_spots,5                   electrons_to_add, locs_sort, HashBitArray, graph_setup,6                   node_energy, bitarray_to_assignment)7class LocalOptimisation(BondOrderAssignment):8    def __init__(self, G):9        self.init_G = G10        11    def initialise(self):12        self.G = graph_to_dist_graph(self.init_G)13        self.target = electrons_to_add(self.init_G)14        self.locs = locs_sort(electron_spots(self.init_G), self.G)15        if INIT_WITH_GA:16            self.init_a = HashBitArray(len(self.locs))17            self.init_a.setall(False)18            base_energy = self.calc_energy(self.init_a)[1]19            all_locs = list(range(len(self.locs)))20            while self.init_a.count() < self.target:21                energy_diffs = {}22                for i in all_locs:23                    self.init_a[i] = True24                    energy_diffs[i] = (self.calc_energy(self.init_a)[1]25                                       - base_energy)26                    self.init_a[i] = False27                min_i = min(energy_diffs, key=lambda x: energy_diffs[x])28                self.init_a[min_i] = True29                base_energy += energy_diffs[min_i]30                all_locs.remove(min_i)31        else:32            self.init_a = HashBitArray(len(self.locs))33            self.init_a.setall(False)34            self.init_a[:self.target] = True35        if self.init_a.count() != self.target:36            raise IndigoUnfeasibleComputation('Can only optimised when all '37                                'electrons are placed in the initial guess.')38        39    def run(self):40        self.initialise()41        min_ene = self.calc_energy(self.init_a)[1]42        seen = {self.init_a : min_ene}43        current_min = [self.init_a]44        min_round = min_ene + 1         # So the while loop is entered, 45        round_mins = current_min[:]     # regardless of min_ene value.46        pool = Pool(processes=NUM_PROCESSES)47        while abs(min_round - min_ene) > 1e-10:48            min_ene = min_round49            current_min = round_mins[:]50            a = current_min[0]51            results = pool.imap_unordered(self.calc_energy, 52                                               (n for n in self.neighbours(a)53                                                if n not in seen),54                                               chunksize=8)55            for n, n_ene in results:56                seen[n] = n_ene57                if n_ene - min_round < -1e-10:58                    min_round = n_ene59                    round_mins = [n]60                elif -1e-10 < n_ene - min_round < 1e-10:61                    round_mins.append(n)62        pool.terminate()63        bitarray_to_assignment(self.init_G, current_min[0], self.locs)64        return self.init_G, seen[current_min[0]]65    def calc_energy(self, a):66        graph_setup(self.G, a, self.locs)67                68        ene = sum(node_energy(self.G, n) for n in self.G)69        return a, round(ene, 5)70    71    def neighbours(self, a):72        for source in set(self.locs):73            i = self.locs.index(source)74            i_count = self.locs.count(source)75            i_loc = i + a[i:i+i_count].count() - 176            if not a[i:i+i_count].count():77                continue78            for target in set(self.locs):79                if source == target:80                    continue81                j = self.locs.index(target)82                j_count = self.locs.count(target)83                j_loc = j + a[j:j+j_count].count()84                if j_count == a[j:j+j_count].count():85                    continue86                b = a.copy()87                b[i_loc] = False88                b[j_loc] = True89                yield b90                ...

dyrelu.py

Source:dyrelu.py

1import torch2import torch.nn as nn3import torch.nn.functional as F4def _make_divisible(v, divisor, min_value=None):5    if min_value is None:6        min_value = divisor7    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)8    # Make sure that round down does not go down by more than 10%.9    if new_v < 0.9 * v:10        new_v += divisor11    return new_v12class swish(nn.Module):13    def forward(self, x):14        return x * torch.sigmoid(x)15class h_swish(nn.Module):16    def __init__(self, inplace=False):17        super(h_swish, self).__init__()18        self.inplace = inplace19    def forward(self, x):20        return x * F.relu6(x + 3.0, inplace=self.inplace) / 6.021class h_sigmoid(nn.Module):22    def __init__(self, inplace=True, h_max=1):23        super(h_sigmoid, self).__init__()24        self.relu = nn.ReLU6(inplace=inplace)25        self.h_max = h_max26    def forward(self, x):27        return self.relu(x + 3) * self.h_max / 628class DYReLU(nn.Module):29    def __init__(self, inp, oup, reduction=4, lambda_a=1.0, K2=True, use_bias=True, use_spatial=False,30                 init_a=[1.0, 0.0], init_b=[0.0, 0.0]):31        super(DYReLU, self).__init__()32        self.oup = oup33        self.lambda_a = lambda_a * 234        self.K2 = K235        self.avg_pool = nn.AdaptiveAvgPool2d(1)36        self.use_bias = use_bias37        if K2:38            self.exp = 4 if use_bias else 239        else:40            self.exp = 2 if use_bias else 141        self.init_a = init_a42        self.init_b = init_b43        # determine squeeze44        if reduction == 4:45            squeeze = inp // reduction46        else:47            squeeze = _make_divisible(inp // reduction, 4)48        # print('reduction: {}, squeeze: {}/{}'.format(reduction, inp, squeeze))49        # print('init_a: {}, init_b: {}'.format(self.init_a, self.init_b))50        self.fc = nn.Sequential(51            nn.Linear(inp, squeeze),52            nn.ReLU(inplace=True),53            nn.Linear(squeeze, oup * self.exp),54            h_sigmoid()55        )56        if use_spatial:57            self.spa = nn.Sequential(58                nn.Conv2d(inp, 1, kernel_size=1),59                nn.BatchNorm2d(1),60            )61        else:62            self.spa = None63    def forward(self, x):64        if isinstance(x, list):65            x_in = x[0]66            x_out = x[1]67        else:68            x_in = x69            x_out = x70        b, c, h, w = x_in.size()71        y = self.avg_pool(x_in).view(b, c)72        y = self.fc(y).view(b, self.oup * self.exp, 1, 1)73        if self.exp == 4:74            a1, b1, a2, b2 = torch.split(y, self.oup, dim=1)75            a1 = (a1 - 0.5) * self.lambda_a + self.init_a[0]  # 1.076            a2 = (a2 - 0.5) * self.lambda_a + self.init_a[1]77            b1 = b1 - 0.5 + self.init_b[0]78            b2 = b2 - 0.5 + self.init_b[1]79            out = torch.max(x_out * a1 + b1, x_out * a2 + b2)80        elif self.exp == 2:81            if self.use_bias:  # bias but not PL82                a1, b1 = torch.split(y, self.oup, dim=1)83                a1 = (a1 - 0.5) * self.lambda_a + self.init_a[0]  # 1.084                b1 = b1 - 0.5 + self.init_b[0]85                out = x_out * a1 + b186            else:87                a1, a2 = torch.split(y, self.oup, dim=1)88                a1 = (a1 - 0.5) * self.lambda_a + self.init_a[0]  # 1.089                a2 = (a2 - 0.5) * self.lambda_a + self.init_a[1]90                out = torch.max(x_out * a1, x_out * a2)91        elif self.exp == 1:92            a1 = y93            a1 = (a1 - 0.5) * self.lambda_a + self.init_a[0]  # 1.094            out = x_out * a195        if self.spa:96            ys = self.spa(x_in).view(b, -1)97            ys = F.softmax(ys, dim=1).view(b, 1, h, w) * h * w98            ys = F.hardtanh(ys, 0, 3, inplace=True)/399            out = out * ys...

analyse_adder_heuristic_design_lsb.py

Source:analyse_adder_heuristic_design_lsb.py

1# Analyse heuristic design for adders2# ===================================3#4# Author: Petr Dvoracek <xdvora0n@stud.fit.vutbr.cz>5# Date: 24th October 20146# Filename: analyse_adder_heuristic_design.py7# Description: The script analyses an error bits when we use smaller adders8#              as a seed for a bigger adder. The output of this script is a table.9# todo check z liche sudou10import sys11MIN_OPERAND_SIZE = 312MAX_OPERAND_SIZE = 813def compute_error(init_a, init_b):14    error = 015    shift = init_a 16    sad = 0    17    maximal = (2**(init_a +init_b+1) * 2**(init_b+1))18    # Nemusime pocitat polovinu starych veci, jsou stejne jak minule.19    for a in xrange(2**init_a): 20        for b in xrange(2**init_b):21            error += bin(((a+b)<<1) ^ (b+ ((a << 1) | 1))).count('1')22            sad += abs(((a+b)<<1) - (b+ ((a << 1) | 1)))23            #print bin(a) + " "+ bin(b) + " => " +  bin(a+b) + " || " + bin(a | 1<< shift)  +" "+bin(b + (a | 1<< shift )) + " XORED " +bin((a+b) ^ (b+ (a | 1<< shift )))24    max_fitness = (init_a+2) * 2**(init_a + init_b + 1)25    init_fitness = max_fitness - error26    error_rate =  float(init_fitness) / float(max_fitness)27    result = str(init_a) + "+" + str(init_b) + " => "28    if init_a == init_b:29        result += str(init_a) + "+" + str(init_b+1) + " "30    else:31        result += str(init_a+1) + "+" + str(init_b) + " "32    result += str(max_fitness) +" "+ str(error) +" "+ str(init_fitness) + " " +str(error_rate)+ "    SAD ["+str(sad)+"] -> " + str(float(sad)/ float(maximal)) 33    return result34for operand_bits in xrange(MIN_OPERAND_SIZE, MAX_OPERAND_SIZE):35    print compute_error(operand_bits, operand_bits)...

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