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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, α=1, β=0.5, γ=2, δ=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.α, self.β, self.γ, self.δ = α, β, γ, δ20 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.α * (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.γ * (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.β * (x_r - c)65 action = 'contracted inside'66 else:67 # contract inside - from worst to centroid 68 x_c = c + self.β * (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.δ * (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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