Collection of my machine-learning stuff.
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machinelearning/rgbAI/main.py

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1.6 KiB

#!/usr/bin/env python
import numpy as np
from lib.func import AIlib as ai
class rgb(object):
def __init__(self, loadedWeights: np.matrix=None, loadedBias: np.matrix=None):
if( not loadedWeights or not loadedBias ): # if one is null (None) then just generate new ones
print("Generating weights and biases...")
self.weights = [ ai.genRandomMatrix(3, 4), ai.genRandomMatrix(4, 4), ai.genRandomMatrix(4, 3) ] # array of matrices of weights
# 3 input neurons -> 4 hidden neurons -> 4 hidden neurons -> 3 output neurons
# Generate the biases
self.bias = [ ai.genRandomMatrix(1, 4), ai.genRandomMatrix(1, 4), ai.genRandomMatrix(1, 3) ]
# This doesn't look very good, but it works so...
self.generation = 0
else: # if we want to load our progress from before then this would do it
self.weights = loadedWeights
self.bias = loadedBias
def calcError( self, inp:np.array, out:np.array ):
cost = ai.calcCost( inp, out )
print(cost)
# Cost needs to get to 0, we can figure out this with backpropagation
def learn():
print("learn")
def think(self, inp:np.array):
print("-----Gen " + str(self.generation) + "------")
print("\n-Input-")
print(inp)
print("\n")
res = ai.think( inp, self.weights, self.bias )
print("\n-Output-")
print(res)
print("\n----------------\n\n")
return res
def init(): # init
bot = rgb()
inpArr = np.array( [0.2, 0.4, 0.8] )
res = bot.think( inpArr )
cost = bot.calcError(inpArr, res)
init()