How to use pop_values method in autotest

Best Python code snippet using autotest_python

diffev.py

Source:diffev.py

1"""Differential Evolution Optimization2:Author: Robert Kern3Copyright 2005 by Robert Kern.4"""5import numpy as np6# Licence:7# Copyright (c) 2001, 2002 Enthought, Inc.8# 9# All rights reserved.10# 11# Redistribution and use in source and binary forms, with or without12# modification, are permitted provided that the following conditions are met:13# 14#   a. Redistributions of source code must retain the above copyright notice,15#      this list of conditions and the following disclaimer.16#   b. Redistributions in binary form must reproduce the above copyright17#      notice, this list of conditions and the following disclaimer in the18#      documentation and/or other materials provided with the distribution.19#   c. Neither the name of the Enthought nor the names of its contributors20#      may be used to endorse or promote products derived from this software21#      without specific prior written permission.22# 23# 24# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"25# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE26# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE27# ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR28# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL29# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR30# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER31# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT32# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY33# OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH34# DAMAGE.35# Notes: for future modifications:36# Ali, M. M., and A. Toern. Topographical differential evolution using37# pre-calculated differentials. _Stochastic and Global Optimization_. 1--17.38#39#  A good scale value:40#    F = max(l_min, 1-min(abs(f_min/f_max), abs(f_max/f_min)))41#      ~ 0.3 <= l_min <= 0.442#      ~ f_min and f_max are the minimum and maximum values in the initial43#        population.44#45#  Pre-calculated differentials:46#    Keep a set of differentials A.47#    For each x_i of the population S:48#      Every M steps, randomly select 3 points x_r1, x_r2, x_r3 from S (not x_i).49#        Compute new x_i using x_r1 + F*(x_r2-x_r3).50#        Store differential in A.51#      Each other step:52#        Randomly select x_r1 from S and a differential vector from A.53#      Crossover.54#55#  Convergence criterion:56#    f_max - f_min < eps57#58#  Topographical DEPD:59#    Two populations S and Sa (auxiliary).60#    Phase counter t = 0 and array shift[:] = False.61#    Stopping condition: e.g. t >= 4.62#    g << N, number of nearest neighbors to search for graph minima.63#    Ng << N, number of points for graph.64#    For each x_i in S, do DEPD as described above to get y_i.65#    if f(y_i) < f(x_i):66#      if shift[i] is False:67#        shift[i] = True68#        S[i] = y_i69#      else:70#        Sa[i] = y_i71#      if alltrue(shift,axis=0):72#        Find graph minima of f(x) using the Ng best points in S.73#        Do local search from each minimum.74#        Replace worst Ng points in S with best Ng points in Sa.75#        If best from this phase is better than previous best, t=0.76#        Else: t += 1.77#        shift[:] = False78#    Next generation.79class DiffEvolver(object):80    """Minimize a function using differential evolution.81    Constructors82    ------------83    DiffEvolver(func, pop0, args=(), crossover_rate=0.5, scale=None,84        strategy=('rand', 2, 'bin'), eps=1e-6)85      func -- function to minimize86      pop0 -- sequence of initial vectors87      args -- additional arguments to apply to func88      crossover_rate -- crossover probability [0..1] usually 0.5 or so89      scale -- scaling factor to apply to differences [0..1] usually > 0.590        if None, then calculated from pop0 using a heuristic91      strategy -- tuple specifying the differencing/crossover strategy92        The first element is one of 'rand', 'best', 'rand-to-best' to specify93        how to obtain an initial trial vector.94        The second element is either 1 or 2 (or only 1 for 'rand-to-best') to95        specify the number of difference vectors to add to the initial trial.96        The third element is (currently) 'bin' to specify binomial crossover.97      eps -- if the maximum and minimum function values of a given generation are98        with eps of each other, convergence has been achieved.99      prng -- a RandomState instance. By default, this is the global100        numpy.random instance.101    DiffEvolver.frombounds(func, lbound, ubound, npop, crossover_rate=0.5,102        scale=None, strategy=('rand', 2, 'bin'), eps=1e-6)103      Randomly initialize the population within given rectangular bounds.104      lbound -- lower bound vector105      ubound -- upper bound vector106      npop -- size of population107    Public Methods108    --------------109    solve(newgens=100)110      Run the minimizer for newgens more generations. Return the best parameter111      vector from the whole run.112    Public Members113    --------------114    best_value -- lowest function value in the history115    best_vector -- minimizing vector116    best_val_history -- list of best_value's for each generation117    best_vec_history -- list of best_vector's for each generation118    population -- current population119    pop_values -- respective function values for each of the current population120    generations -- number of generations already computed121    func, args, crossover_rate, scale, strategy, eps -- from constructor122    """123    def __init__(self, func, pop0, args=(), crossover_rate=0.5, scale=None,124            strategy=('rand', 2, 'bin'), eps=1e-6, prng=np.random):125        self.func = func126        self.population = np.array(pop0)127        self.npop, self.ndim = self.population.shape128        self.args = args129        self.crossover_rate = crossover_rate130        self.strategy = strategy131        self.eps = eps132        self.prng = prng133        self.pop_values = [self.func(m, *args) for m in self.population]134        bestidx = np.argmin(self.pop_values)135        self.best_vector = self.population[bestidx]136        self.best_value = self.pop_values[bestidx]137        if scale is None:138            self.scale = self.calculate_scale()139        else:140            self.scale = scale141        self.generations = 0142        self.best_val_history = []143        self.best_vec_history = []144        self.bound = None145        self.jump_table = {146            ('rand', 1, 'bin'): (self.choose_rand, self.diff1, self.bin_crossover),147            ('rand', 2, 'bin'): (self.choose_rand, self.diff2, self.bin_crossover),148            ('best', 1, 'bin'): (self.choose_best, self.diff1, self.bin_crossover),149            ('best', 2, 'bin'): (self.choose_best, self.diff2, self.bin_crossover),150            ('rand-to-best', 1, 'bin'):151                (self.choose_rand_to_best, self.diff1, self.bin_crossover),152            }153    def clear(self):154        self.best_val_history = []155        self.best_vec_history = []156        self.generations = 0157        self.pop_values = [self.func(m, *self.args) for m in self.population]158    def frombounds(cls, func, lbound, ubound, npop, crossover_rate=0.5,159            scale=None, strategy=('rand', 2, 'bin'), eps=1e-6, prng=np.random):160        lbound = np.asarray(lbound)161        ubound = np.asarray(ubound)162        pop0 = prng.uniform(lbound, ubound, size=(npop, len(lbound)))163        return cls(func, pop0, crossover_rate=crossover_rate, scale=scale,164            strategy=strategy, eps=eps, prng=prng)165    frombounds = classmethod(frombounds)166    def set_boundaries(self, lbound, ubound, mode='mirror'):167        boundary_table = {'skip': None,168                          'reject': self.bound_reject,169                          'limit': self.bound_limit,170                          'mirror': self.bound_mirror,171                          'halfway': self.bound_halfway,172                          'old': self.bound_old173                          }174        self.bound = boundary_table[mode]175        self.lbound = lbound176        self.ubound = ubound177    def calculate_scale(self):178        rat = abs(max(self.pop_values)/self.best_value)179        rat = min(rat, 1./rat)180        return max(0.3, 1.-rat)181    def bin_crossover(self, oldgene, newgene):182        mask = self.prng.rand(self.ndim) < self.crossover_rate183        return np.where(mask, newgene, oldgene)184    def select_samples(self, candidate, nsamples):185        possibilities = list(range(self.npop))186        possibilities.remove(candidate)187        return self.prng.permutation(possibilities)[:nsamples]188    def diff1(self, candidate):189        i1, i2 = self.select_samples(candidate, 2)190        return self.scale * (self.population[i1] - self.population[i2])191    def diff2(self, candidate):192        i1, i2, i3, i4 = self.select_samples(candidate, 4)193        return self.scale * (self.population[i1] - self.population[i2] +194                             self.population[i3] - self.population[i4])195    def choose_best(self, candidate):196        return self.best_vector197    def choose_rand(self, candidate):198        i = self.select_samples(candidate, 1)[0]199        return self.population[i]200    def choose_rand_to_best(self, candidate):201        return ((1-self.scale) * self.population[candidate] +202                self.scale * self.best_vector)203    def bound_halfway(self, candidate, trial):204        trial = np.select([trial < self.lbound,205                           trial > self.ubound,206                           True],207                          [(self.population[candidate]+self.lbound)/2,208                           (self.population[candidate]+self.ubound)/2,209                           trial])210        return trial211    def bound_reject(self, candidate, trial):212        if np.any(trial < self.lbound) or np.any(trial > self.ubound):213            return None214        else:215            return trial216    def bound_old(self, candidate, trial):217        trial = np.select([trial < self.lbound,218                           trial > self.ubound,219                           True],220                          [self.population[candidate],221                           self.population[candidate],222                           trial])223        return trial224    def bound_limit(self, candidate, trial):225        trial = np.select([trial < self.lbound,226                           trial > self.ubound,227                           True],228                          [self.lbound,229                           self.ubound,230                           trial])231        return trial232    def bound_mirror(self, candidate, trial):233        trial = np.select([trial < self.lbound,234                           trial > self.ubound,235                           True],236                          [self.lbound + (self.lbound - trial),237                           self.ubound - (trial - self.ubound),238                           trial])239        return trial240    def get_trial(self, candidate):241        chooser, differ, crosser = self.jump_table[self.strategy]242        trial = crosser(self.population[candidate],243            chooser(candidate) + differ(candidate))244        return trial245    def converged(self):246        return max(self.pop_values) - min(self.pop_values) <= self.eps247    def solve(self, newgens=100):248        """Run for newgens more generations.249        Return best parameter vector from the entire run.250        """251        for gen in range(self.generations+1, self.generations+newgens+1):252            for candidate in range(self.npop):253                trial = self.get_trial(candidate)254                ## apply boundary function255                if self.bound:256                    trial = self.bound(candidate,trial)257                    ## check if we have abortet that trial258                    if len(trial) == 0:259                        print( ".", end="")260                        continue261                trial_value = self.func(trial, *self.args)262                if trial_value < self.pop_values[candidate]:263                    self.population[candidate] = trial264                    self.pop_values[candidate] = trial_value265                    if trial_value < self.best_value:266                        self.best_vector = trial267                        self.best_value = trial_value268            self.best_val_history.append(self.best_value)269            self.best_vec_history.append(self.best_vector)270            if self.converged():271                break272        self.generations = gen...

buffer.py

Source:buffer.py

...33                    nice_bytes.append(' 0x')34                    hex_seen = True35                nice_bytes.append('%02x' % b)36        return ''.join(nice_bytes)37    def pop_values(self, fmt):38        size = struct.calcsize(fmt)39        if len(self.buffer) < size:40            raise BufferUnderflowError(fmt, self.buffer)41        values = struct.unpack_from(fmt, self.buffer, 0)42        self.buffer = self.buffer[size:]43        return values44    def pop_int8(self):45        return self.pop_values('<b')[0]46    def pop_uint8(self):47        return self.pop_values('<B')[0]48    def pop_int16(self):49        return self.pop_values('<h')[0]50    def pop_uint16(self):51        return self.pop_values('<H')[0]52    def pop_int32(self):53        return self.pop_values('<i')[0]54    def pop_uint32(self):55        return self.pop_values('<I')[0]56    def pop_float32(self):57        return self.pop_values('<f')[0]58    def pop_float64(self):59        return self.pop_values('<d')[0]60    def pop_str16(self):61        l_name = []62        while 1:63            c = self.pop_uint16()64            if c == 0:65                break66            l_name.append(chr(c))67        return ''.join(l_name)68    def pop_str8(self):69        l_name = []70        while 1:71            c = self.pop_uint8()72            if c == 0:73                break...

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