from neural_net.architecture import Sequential
from neural_net.layers import Fullyconnected,Activation
from neural_net.init_funcs import zeros,XavierHe
from neural_net.activation import σ,Softmax,LeakyReLU,Tanh,ELU,ReLU
from neural_net.cost import BinaryCrossEntropy,CrossEntropy
from neural_net.metrics import accuracy
from neural_net.pipeline import onehot,scaler,shuffle,Batch
from neural_net.utils import IrisDatasetDownloader

NNN = Sequential(
        [
        Fullyconnected(2,50,XavierHe("Uniform","ReLU").init_func),
        Activation(LeakyReLU),     
        Fullyconnected(50,1,XavierHe("Uniform","Sigmoid").init_func),
        Activation(σ)
        ],
    BinaryCrossEntropy
    )

import numpy

n,k = 5000,2
X = numpy.random.uniform(-100,100,size=(n,k))
y =( (X[:, 0]**2 + X[:, 1]**2)/numpy.pi < 1000).reshape(-1,1)+0

NNN.train(scaler(X),y,metrics=accuracy)

NNN.predict(X)