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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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class SearchSpace: |
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def __init__(self, _config_): |
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self.search_config = _config_.search_config |
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self.warm_start = _config_.warm_start |
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self.scatter_init = _config_.scatter_init |
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self.memory = {} |
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def pos_space_limit(self): |
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dim = [] |
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for pos_key in self.para_space: |
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dim.append(len(self.para_space[pos_key]) - 1) |
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self.dim = np.array(dim) |
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def create_kerasSearchSpace(self): |
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""" |
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para_space = {} |
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for para_key in search_config_temp.keys(): |
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for param_str in search_config_temp[para_key].keys(): |
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new_param_str = para_key + "." + param_str |
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para_space[new_param_str] = search_config_temp[para_key][param_str] |
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""" |
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self.para_space = self.search_config[list(self.search_config)[0]] |
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self.pos_space_limit() |
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def get_random_pos(self): |
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pos_new = np.random.uniform(np.zeros(self.dim.shape), self.dim, self.dim.shape) |
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pos = np.rint(pos_new) |
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# n_zeros = [0] * len(self.dim) |
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# pos = np.clip(pos_new_int, n_zeros, self.dim) |
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return pos |
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def get_random_pos_scalar(self, hyperpara_name): |
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n_para_values = len(self.para_space[hyperpara_name]) |
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pos = random.randint(0, n_para_values - 1) |
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return pos |
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def pos2para(self, pos): |
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if len(self.para_space.keys()) == pos.size: |
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values_dict = {} |
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for i, key in enumerate(self.para_space.keys()): |
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pos_ = int(pos[i]) |
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values_dict[key] = list(self.para_space[key])[pos_] |
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return values_dict |
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SimonBlanke /
Hyperactive
