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# Author: Simon Blanke |
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# Email: [email protected] |
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# License: MIT License |
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import random |
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import numpy as np |
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from .utils import move_random |
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class Initializer: |
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def __init__(self, conv): |
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self.conv = conv |
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def set_pos(self, initialize): |
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init_positions_list = [] |
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if "random" in initialize: |
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positions = self._init_random_search(initialize["random"]) |
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init_positions_list.append(positions) |
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if "grid" in initialize: |
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positions = self._init_grid_search(initialize["grid"]) |
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init_positions_list.append(positions) |
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if "vertices" in initialize: |
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positions = self._init_vertices(initialize["vertices"]) |
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init_positions_list.append(positions) |
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if "warm_start" in initialize: |
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positions = self._init_warm_start(initialize["warm_start"]) |
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init_positions_list.append(positions) |
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return [item for sublist in init_positions_list for item in sublist] |
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def _init_warm_start(self, value_list): |
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positions = [] |
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for value_ in value_list: |
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pos = self.conv.value2position(list(value_.values())) |
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positions.append(pos) |
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return positions |
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def _init_random_search(self, n_pos): |
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positions = [] |
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if n_pos == 0: |
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return positions |
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for nth_pos in range(n_pos): |
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pos = move_random(self.conv.search_space_positions) |
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positions.append(pos) |
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return positions |
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def _fill_rest_random(self, n_pos, positions): |
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diff_pos = n_pos - len(positions) |
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if diff_pos > 0: |
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pos_rnd = self._init_random_search(n_pos=diff_pos) |
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return positions + pos_rnd |
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else: |
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return positions |
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def _init_grid_search(self, n_pos): |
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positions = [] |
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if n_pos == 0: |
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return positions |
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n_dim = len(self.conv.max_positions) |
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p_per_dim = int(np.power(n_pos, 1 / n_dim)) |
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for dim in self.conv.max_positions: |
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dim_dist = int(dim / (p_per_dim + 1)) |
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n_points = [n * dim_dist for n in range(1, p_per_dim + 1)] |
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positions.append(n_points) |
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pos_mesh = np.array(np.meshgrid(*positions)) |
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positions = list(pos_mesh.T.reshape(-1, n_dim)) |
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positions = self._fill_rest_random(n_pos, positions) |
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return positions |
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def _init_vertices(self, n_pos): |
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positions = [] |
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if n_pos == 0: |
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return positions |
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# zero_array = np.zeros(self.conv.max_positions.shape) |
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sub_arrays = [] |
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for dim in self.conv.max_positions: |
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sub_array = np.array([0, dim]) |
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sub_arrays.append(sub_array) |
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n_dims = len(self.conv.max_positions) |
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pos_comb_np = list(np.array(np.meshgrid(*sub_arrays)).T.reshape(-1, n_dims)) |
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k = min(len(pos_comb_np), n_pos) |
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positions = random.sample(pos_comb_np, k) |
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positions = self._fill_rest_random(n_pos, positions) |
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return positions |
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SimonBlanke /
Gradient-Free-Optimizers
