kata-ialib: Artificial intelligence library

This library contains the implementation of artificial intelligence modules of different types and classes. Besides, the project includes visualization, preprocessing utilities, and script examples that tests the functionality of the artificial intelligence components.

Examples of Usage

Perceptron Example

from nnetwork.neurons.perceptron import PerceptronGD
from visualizer import ErrorPlotter, NeuronPlotter, NeuronAnimation
from sklearn import datasets

iris = datasets.load_iris()
x = iris.data[:, :2]
y = iris.target
x, y = x[y != 2], y[y != 2]
    
model = PerceptronGD(lr=0.01, n_epochs=50)
history = model.fit(x, y)

ErrorPlotter().init(errors=history['errors']).show()
NeuronPlotter().init(x=x, y=y, model=model).show()
NeuronAnimation().init(x=x, y=y, model=model).show()

Adaline Example

from nnetwork.neurons.adaline import AdalineGD
from visualizer import ErrorPlotter, NeuronPlotter, NeuronAnimation
from sklearn import datasets
import preprocessing
import numpy as np

iris = datasets.load_iris()
x, y = iris['data'], iris['target']

x = preprocessing.normalize_data(x[50:150, :2])
y = np.where(y[50:150] == 2, -1, y[50:150])
    
model = AdalineGD(lr=0.01, n_epochs=50)
history = model.fit(x, y)

ErrorPlotter().init(errors=history['errors']).show()
NeuronPlotter().init(x=x, y=y, model=model).show()
NeuronAnimation().init(x=x, y=y, model=model).show()

Growing Neural Gas Example

from nnetwork.growing_neural_gas.growing_neural_gas import GrowingNeuralGas
from visualizer import ErrorPlotter, GrowingNeuralGasPlotter, GrowingNeuralGasAnimation
from sklearn import datasets
import tensorflow as tf

iris = datasets.load_iris()
iris = iris.data[:, :]
data = tf.Variable(iris, dtype=tf.float32)

model = GrowingNeuralGas(a_max=4, max_number_units=100, max_clusters=4)
history = model.fit(data)

ErrorPlotter().init(errors=history['errors']).show()
GrowingNeuralGasPlotter().init(history=history).show()
GrowingNeuralGasAnimation().init(history=history).show()