gradient_free_optimizers.conv.positions2values() - Code Metrics - Inspection of "add tests for initializers" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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by Simon

created 2020-09-01 14:06 UTC

gradient_free_optimizers.conv.positions2values() A

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Complexity

Conditions

Size

Total Lines	10
Code Lines	8

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	2
eloc	8
nop	2
dl	0
loc	10
rs	10
c	0
b	0
f	0

# Author: Simon Blanke
# Email: [email protected]
# License: MIT License

import numpy as np


def position2value(search_space, position):
    value = []

    for n, space_dim in enumerate(search_space):
        value.append(space_dim[position[n]])

    return value


def values2positions(search_space, values):
    init_pos_conv_list = []
    values_np = np.array(values)

    for n, space_dim in enumerate(search_space):
        pos_1d = values_np[:, n]
        init_pos_conv = np.where(space_dim == pos_1d)[0]
        init_pos_conv_list.append(init_pos_conv)

    return init_pos_conv_list


"""
def positions2values(search_space, positions):
    pos_converted = []
    positions_np = np.array(positions)

    for n, space_dim in enumerate(search_space):
        pos_1d = positions_np[:, n]
        pos_conv = np.take(space_dim, pos_1d, axis=0)
        pos_converted.append(pos_conv)

    return list(np.array(pos_converted).T)

"""



1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5			import numpy as np
6
7
8			def position2value(search_space, position):
9			value = []
10
11			for n, space_dim in enumerate(search_space):
12			value.append(space_dim[position[n]])
13
14			return value
15
16
17			def values2positions(search_space, values):
18			init_pos_conv_list = []
19			values_np = np.array(values)
20
21			for n, space_dim in enumerate(search_space):
22			pos_1d = values_np[:, n]
23			init_pos_conv = np.where(space_dim == pos_1d)[0]
24			init_pos_conv_list.append(init_pos_conv)
25
26			return init_pos_conv_list
27
28
29			"""
30			def positions2values(search_space, positions):
31			pos_converted = []
32			positions_np = np.array(positions)
33
34			for n, space_dim in enumerate(search_space):
35			pos_1d = positions_np[:, n]
36			pos_conv = np.take(space_dim, pos_1d, axis=0)
37			pos_converted.append(pos_conv)
38
39			return list(np.array(pos_converted).T)
40
41			"""
42
43

SimonBlanke / Gradient-Free-Optimizers

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