Evolutionary_Computing/utils.py at main · leguiart/Evolutionary_Computing · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
import numpy as np
import math
import sys
import matplotlib.pyplot as plt

def average_list_of_lists(li):
    li.sort(key = len)

    li_avg = np.zeros(len(li[-1]))
    li_avg_mat = np.zeros((len(li), len(li[-1])))
    length_i_minus_1 = 0
    for i in range(len(li)):
        length_i = len(li[i])
        li_avg_mat[i, 0 : length_i] = np.array(li[i])

    for i in range(len(li) - 1):
        length_i = len(li[i])
        if length_i > length_i_minus_1:
            li_avg[length_i_minus_1: length_i] = np.average(np.array(li_avg_mat[i:, length_i_minus_1: length_i]), axis=0)
            length_i_minus_1 = length_i

    if length_i_minus_1 < len(li[-1]):
        li_avg[length_i_minus_1: len(li[-1])] = np.array(li_avg_mat[len(li) - 1, length_i_minus_1: len(li[-1])])
    return li_avg


def protectedDiv(left, right):
    try:
        return 1 if right == 0 else left / right
    except ZeroDivisionError:
        return 1


def protectedExp(arg):
    try:
        return math.exp(min(round(arg, 4), 100))
    except:
        return 1.

def protectedSqrt(arg):
    return math.sqrt(abs(arg))

def cos(arg):
    try:
        return math.cos(math.radians(arg))
    except:
        return 0

def sin(arg):
    try:
        return math.sin(math.radians(arg))
    except:
        return 0

def tan(arg):
    try:
        return math.min(math.tan(math.radians(arg)), 100)
    except:
        return 0


def plot_superposed(x_axis, y_s, x_label = 'x', y_label = 'y', plot_labels = None, sub_title = None, sup_title = None, show = True, store = False, store_path = "symbolic_regression_estimate"):
    fig, axs = plt.subplots(1, 1, constrained_layout=True)
    for i, y_i in enumerate(y_s):
        if plot_labels and i <= len(plot_labels) - 1:
            if type(y_i) is tuple:
                axs.plot(x_axis, y_i[0], label = plot_labels[i], color = y_i[1])
            else:
                axs.plot(x_axis, y_i, label = plot_labels[i])
        else:
            if type(y_i) is tuple:
                axs.plot(x_axis, y_i[0], color = y_i[1])
            else:
                axs.plot(x_axis, y_i)
    if sub_title:
        axs.set_title(sub_title)
    axs.set_xlabel(x_label)
    axs.set_ylabel(y_label)
    fig.suptitle(sup_title, fontsize=16)
    plt.legend()
    if store:
        plt.savefig(store_path, bbox_inches='tight')
        if show:
            plt.show()
    else:
        if show:
            plt.show()

def plot_superposed_multiple_xs(x_axis_s, y_s, x_label = 'x', y_label = 'y', plot_labels = None, sub_title = None, sup_title = None, show = True, store = False, store_path = "symbolic_regression_estimate"):
    fig, axs = plt.subplots(1, 1, constrained_layout=True)
    for i, y_i in enumerate(y_s):
        if plot_labels and i <= len(plot_labels) - 1:
            if type(y_i) is tuple:
                axs.plot(x_axis_s[i], y_i[0], label = plot_labels[i], color = y_i[1])
            else:
                axs.plot(x_axis_s[i], y_i, label = plot_labels[i])
        else:
            if type(y_i) is tuple:
                axs.plot(x_axis_s[i], y_i[0], color = y_i[1])
            else:
                axs.plot(x_axis_s[i], y_i)
    if sub_title:
        axs.set_title(sub_title)
    axs.set_xlabel(x_label)
    axs.set_ylabel(y_label)
    fig.suptitle(sup_title, fontsize=16)
    plt.legend()
    if store:
        plt.savefig(store_path, bbox_inches='tight')
        if show:
            plt.show()
    else:
        if show:
            plt.show()

def plot_grouped_bar_graph(y_label, title, labels, groups, data, savepath, width, padding, bar_label = False):
    x = np.arange(len(labels))  # the label locations
    #width = 0.05  # the width of the bars
    fig, ax = plt.subplots()
    rects = []
    if len(groups)%2 == 0:
        r = len(groups)//2
        for i, j in enumerate(range(-r, r + 1)):
            if j < 0:
                rects += [ax.bar(x + j*width + width/2, data[i], width, label=groups[i])]
            elif j > 0:
                rects += [ax.bar(x + j*width - width/2, data[i - 1], width, label=groups[i - 1])]
    else:
        r = math.floor(len(groups)/2)
        for i, j in enumerate(range(-r, r + 1)):
            rects += [ax.bar(x + j*width, data[i], width, label=groups[i])]
    # Add some text for labels, title and custom x-axis tick labels, etc.
    ax.set_ylabel(y_label)
    ax.set_title(title)
    ax.set_xticks(x)
    ax.set_xticklabels(labels)
    ax.legend()
    if bar_label:
        for rect in rects:
            ax.bar_label(rect, padding=padding)
    fig.tight_layout()
    plt.savefig(savepath, bbox_inches='tight')