machinelearning/rgbAI/lib/func.py

import numpy as np
from copy import deepcopy as copy


class AIlib:
    def sigmoid(x):
        return 1/(1 + np.exp(-x))

    def correctFunc(inp: np.array):  # generates the correct answer for the AI
        # basically invert the rgb values
        return np.asarray([1.0 - inp[0], 1.0 - inp[1], 1.0 - inp[2]])

    # cost function, lower -> good, higher -> bad, bad bot, bad
    def calcCost(predicted: np.array, correct: np.array):
        costSum = 0
        maxLen = len(correct)

        for i in range(maxLen):
            costSum += abs((predicted[i] - correct[i]))

        return costSum / maxLen

    def getThinkCost(inp: np.array, predicted: np.array):
        corr = AIlib.correctFunc(inp)
        return AIlib.calcCost(predicted, corr)

    # generate a matrix with x, y dimensions with random values from min-max in it
    def genRandomMatrix(x: int, y: int, min: float = 0.0, max: float = 1.0):
        # apply ranger with * and -
        mat = np.random.rand(x, y) - 0.25
        return mat

    def think(inp: np.array, obj, layerIndex: int = 0):  # recursive thinking, hehe
        maxLayer = len(obj.weights) - 1
        # dot multiply the input and the weights
        weightedLayer = np.dot(inp, obj.weights[layerIndex])
        layer = AIlib.sigmoid(
            np.add(weightedLayer, obj.bias[layerIndex]))  # add the biases

        if(layerIndex < maxLayer):
            return AIlib.think(layer, obj, layerIndex + 1)
        else:
            out = np.squeeze(np.asarray(layer))
            return out

    def propDer(dCost, dProp):
        # Calculate the partial derivative for that prop
        return dCost / dProp

    def compareAIobjects(inp, obj1, obj2):
        # Compare the two instances
        res1 = AIlib.think(inp, obj1)
        cost1 = AIlib.getThinkCost(inp, res1)  # get the cost

        res2 = AIlib.think(inp, obj2)
        cost2 = AIlib.getThinkCost(inp, res2)  # get the second cost

        # Actually calculate stuff
        dCost = cost2 - cost1
        return dCost, cost1

    def compareInstanceWeight(obj, inp, theta: float, layerIndex: int, neuronIndex_X: int, neuronIndex_Y: int):
        # Create new a instance of the object
        obj2 = copy(obj)  # annoying way to create a new instance of the object

        # mutate the second objects neuron
        obj2.weights[layerIndex][neuronIndex_X][neuronIndex_Y] += theta
        # compare the two and get the dCost with respect to the weights
        dCost, curCost = AIlib.compareAIobjects(inp, obj, obj2)

        return dCost, curCost

    def compareInstanceBias(obj, inp, theta: float, layerIndex: int, biasIndex: int):
        obj2 = copy(obj)

        # do the same thing for the bias
        obj2.bias[layerIndex][0][biasIndex] += theta
        dCost, curCost = AIlib.compareAIobjects(inp, obj, obj2)

        return dCost, curCost

    def getChangeInCost(obj, inp, theta, layerIndex):
        mirrorObj = copy(obj)

        # Fill the buffer with None so that the dCost can replace it later
        # fill it with a placeholder
        dCost_W = np.zeros(shape=mirrorObj.weights[layerIndex].shape)
        dCost_B = np.zeros(shape=mirrorObj.bias[layerIndex].shape)

        # Get the cost change for the weights
        weightLenX = len(dCost_W)
        weightLenY = len(dCost_W[0])

        for x in range(weightLenX):  # get the dCost for each x,y
            for y in range(weightLenY):
                dCost_W[x][y], curCostWeight = AIlib.compareInstanceWeight(
                    obj, inp, theta, layerIndex, x, y)

        # Get the cost change for the biases
        biasLenY = len(dCost_B[0])
        for index in range(biasLenY):
            dCost_B[0][index], curCostBias = AIlib.compareInstanceBias(
                obj, inp, theta, layerIndex, index)

        return dCost_W, dCost_B, (curCostBias + curCostWeight)/2

    # Calculate the gradient for that prop
    def gradient(inp: np.array, obj, theta: float, maxLayer: int, layerIndex: int = 0, grads=None, obj1=None, obj2=None):
        # Check if grads exists, if not create the buffer
        if(not grads):
            grads = [None] * (maxLayer+1)

        dCost_W, dCost_B, meanCurCost = AIlib.getChangeInCost(
            obj, inp, theta, layerIndex)

        # Calculate the gradient for the layer
        weightDer = AIlib.propDer(dCost_W, theta)
        biasDer = AIlib.propDer(dCost_B, theta)

        # Append the gradients to the list
        grads[layerIndex] = {
            "weight": weightDer,
            "bias": biasDer
        }

        newLayer = layerIndex + 1
        if(newLayer <= maxLayer):
            return AIlib.gradient(inp, obj, theta, maxLayer, newLayer, grads, obj1, obj2)
        else:
            return grads, meanCurCost

    def mutateProps(inpObj, maxLen: int, gradient: list):
        obj = copy(inpObj)
        for i in range(maxLen):
            obj.weights[i] -= obj.learningrate * \
                gradient[i]["weight"]  # mutate the weights
            obj.bias[i] -= obj.learningrate * gradient[i]["bias"]

        return obj

    def learn(inputNum: int, targetCost: float, obj, theta: float, curCost: float = None):
        # Calculate the derivative for:
        # Cost in respect to weights
        # Cost in respect to biases

        # i.e. : W' = W - lr * gradient (respect to W in layer i) = W - lr*[ dC / dW[i] ... ]
        # So if we change all the weights with i.e. 0.01 = theta, then we can derive the gradient with math and stuff

        inp = np.asarray(np.random.rand(1, inputNum))[
            0]  # create a random learning sample

        # targetCost is the target for the cost function
        while(not curCost or curCost > targetCost):
            maxLen = len(obj.bias)
            grads, curCost = AIlib.gradient(inp, obj, theta, maxLen - 1)

            # mutate the props for next round
            obj = AIlib.mutateProps(obj, maxLen, grads)
            print(f"Cost: {curCost}")

        print("DONE\n")
        print(obj.weights)
        print(obj.bias)