How to use expand_classic method in autotest

Best Python code snippet using autotest_python

kernelexpand.py

Source:kernelexpand.py

...101        if not len(new_patches):102            new_patches.append(component[-1])103        new_components.append(new_patches)104    return new_components105def expand_classic(kernel, mirrors):106    components = decompose_kernel(kernel)107    if mirrors:108        components = mirror_kernel_components(mirrors, components)109    components = select_kernel_components(components)110    patches = []111    for component in components:112        patches.append(component[0])113    return patches114if __name__ == '__main__':115    from optparse import OptionParser116    parser = OptionParser()117    parser.add_option("-m", "--mirror", type="string", dest="mirror",118            action="append", nargs=2, help="mirror prefix")119    parser.add_option("-v", "--no-validate", dest="validate",...

nelder_mead.py

Source:nelder_mead.py

1import numpy as np2EXPAND_MINIMIZE, EXPAND_CLASSIC = 0, 13class NelderMead:    4    #static5    def init_simplex(self, N, x0=None, simplex_edge=1):6        if x0 is None:7            x0 = np.zeros(N)8        S = [x0]9        for i in range(N):10            S.append(x0 + simplex_edge * np.array([int(j == i) for j in range(N)]))11        return np.array(S)12            13    def __init__(self, Î±=1, Î²=0.5, Î³=2, Î´=0.5, expand=EXPAND_MINIMIZE, eps_x=1e-5, eps_f=1e-5, timeout=100):14        """15        EXPAND_MINIMIZE: x_new = argmin{f_e, f_r};16        EXPAND_CLASSIC: x_new = x_e if f_e < f_best17        """18        self.expand = expand19        self.Î±, self.Î², self.Î³, self.Î´ = Î±, Î², Î³, Î´20        self.eps_x, self.eps_f = eps_x, eps_f21        self.timeout = timeout22    def _eval_vertices(self):23        """Return sorted list of (v_i, f(v_i))"""24        return sorted(enumerate(self.f(self.S[:,0], self.S[:,1])), key=lambda x:x[1])25    def _centroid(self):26        # sum everything except the worst point27        c = self.S.sum(axis=0) - self.S[self.fS[-1][0]]28        return c / self.N  # mean of (N+1)-1 vertices29    30    def _step(self):31        self.steps += 132        (best_i, best_f), (worst_i, worst_f), (worst2_i, worst2_f) = self.fS[0], self.fS[-1], self.fS[-2]33        c = self._centroid()34        35        action = 'reflected'36        37        # reflect38        x_r = c + self.Î± * (c - self.S[worst_i])39        f_r = self.f(*x_r)40        41        if f_r < best_f:42            # expand43            x_e = c + self.Î³ * (x_r - c)44            f_e = self.f(*x_e)45            46            if self.expand == EXPAND_MINIMIZE:47                if f_e < f_r:48                    self.S[worst_i] = x_e49                    action = 'expanded_m'50                else:51                    self.S[worst_i] = x_r52                    action = 'reflected'53            elif self.expand == EXPAND_CLASSIC:54                if f_e < best_f:55                    self.S[worst_i] = x_e56                    action = 'expanded_c'57                else:58                    self.S[worst_i] = x_r59                    action = 'reflected'60        elif f_r > worst2_f:61            # contract62            if f_r < worst_f:63                # contract outside - from reflected to centroid64                x_c = c + self.Î² * (x_r - c)65                action = 'contracted inside'66            else:67                # contract inside - from worst to centroid 68                x_c = c + self.Î² * (self.S[worst_i] - c)69                action = 'contracted outside'70            f_c = self.f(*x_c)71            if f_c <= f_r:72                self.S[worst_i] = x_c73            else:74                # shrink75                action = 'shrink'76                for i in range(self.N):77                    x_i = self.fS[i+1][0]78                    self.S[x_i] = self.S[best_i] + self.Î´ * (self.S[x_i] - self.S[best_i])79        else:80            # best_f <= self.f(xr) < worst2_f81            # enhanced a bit. save new vertex82            self.S[worst_i] = x_r83            action = 'reflected'84        85        self.fS = self._eval_vertices()86        self.c = c87        self.last_action = action88        89    def _term_condition(self):90        if self.steps == 0: 91            return False92        93        # a) vertex are close94        if ((self.S - self.c) < self.eps_x).all():95            self.term_condition = 'x close'96            return True97        98        # b) vertex values are close99        fc = self.f(*self.c)100        if (np.abs([self.f(*x) - fc for x in self.S]) < self.eps_f).all():101            self.term_condition = 'f close'102            return True103        104        # c) timeout105        if self.steps > self.timeout:106            self.term_condition = 'timeout'107            return True108        109        return False110    111    def optimize(self, f, N, G, S0=None, init_only=False):112        self.lims = G.min(axis=0), G.max(axis=0)113        self.N = N114        self.f = f115        self.G = G116        self.steps = 0117        self.last_action = None118        self.term_condition = None119        120        if S0 is None:121            self.S = self.init_simplex(self.N)122        else:123            self.S = S0124        self.fS = self._eval_vertices()125        126        if init_only:127            return128        129        while not self._term_condition():130            self._step()131        132        c = self.S.mean(axis=0)133        return {134            'x': c,135            'f': f(*c),136            'steps': self.steps,137            'message': 'stopped because '+self.term_condition138        }...

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