60 lines
1.7 KiB
Python
60 lines
1.7 KiB
Python
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Mon Aug 21 14:59:22 2023
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@author: astral
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"""
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import json
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import math as m
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import numpy as np
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from datetime import datetime
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from plot import qtplot
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eps_space = list(np.linspace(0.005,0.5,100))
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def resultant(sample):
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phase_x = [m.cos(ind) for ind in sample]
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phase_y = [m.sin(ind) for ind in sample]
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return (np.average(phase_x), np.average(phase_y))
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def H2(x):
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return -x*m.log2(x)-(1-x)*m.log2(1-x)
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for dim in [7]:
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diff_res = [0]
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av_ei = [0]
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for eps in eps_space:
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path=f'/cloud/Public/_data/neuropercolation/4lay/steps=100000/dim={dim:02}/'
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with open(path+f"eps={round(eps,3):.3f}_activation.txt", 'r', encoding='utf-8') as f:
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activation = json.load(f)[100:]
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osc = list(zip(*activation))
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phase = np.array([[np.arctan2(*act[::-1]) for act in osc[i]] for i in range(2)])
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phase_diff = (phase[1]-phase[0]+m.pi)%(2*m.pi)-m.pi
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res = np.linalg.norm(resultant(phase_diff))
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diff_res.append(res)
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with open(path+f"eps={round(eps,3):.3f}_channels.txt", 'r', encoding='utf-8') as f:
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channels = json.load(f)[100:]
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ei = [np.sum(cha)*(1-H2(eps)) for cha in channels]
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av_ei.append(np.average(ei))
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print(f'Done eps={eps:.3f} with dim={dim} at {datetime.now()}')
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qtplot(f'Resultant and EI evolution for dim={dim} with 4 layers',
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[[0]+eps_space]*2,
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[diff_res, av_ei],
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['Resultant', 'avEI'],
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export=True,
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path=path,
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filename=f'Resultant and EI for dim={dim}.png',
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close=True)
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