from matplotlib import cm
import numpy as np
from MachineLearn.Classes import Experiment, DataSet, Data
from T3.Perceptron import Layered_perceptron
import matplotlib.pyplot as plt
COLOR = cm.rainbow(np.linspace(0, 1, 5))
learning_rate = 0.01
epochs = 5000
oExp = Experiment()
oDataSet = DataSet()
base = np.loadtxt("Datasets/column_3C.dat", usecols=range(6), delimiter=" ")
classes = np.loadtxt("Datasets/column_3C.dat", dtype=object, usecols=-1, delimiter=" ")
for x, y in enumerate(base):
oDataSet.add_sample_of_attribute(np.array(list(np.float32(y)) + [classes[x]]))
oDataSet.attributes = oDataSet.attributes.astype(float)
# oDataSet.normalize_data_set()
for j in range(20):
print(j)
oData = Data(len(oDataSet.labelsNames), 31, samples=50)
oData.random_training_test_by_percent([60, 150, 100], 0.8)
perc = Layered_perceptron(learning_rate, len(oDataSet.labelsNames))
perc.train(oDataSet.attributes[oData.Training_indexes], oDataSet.labels[oData.Training_indexes], epochs)
oData.model = perc
oData.confusion_matrix = np.zeros((len(oDataSet.labelsNames), len(oDataSet.labelsNames)))
for i in oData.Testing_indexes:
data = np.matrix(np.hstack(([-1], oDataSet.attributes[i]))).T
from sklearn.preprocessing import LabelBinarizer
import matplotlib.pyplot as plt
from T6_2.kmeans_initializer import InitCentersKMeans
COLOR = cm.rainbow(np.linspace(0, 1, 5))
LEARNING_RATE = 0.1
epochs = 300
K_FOLD = 3
GRID_NEURON = [20, 15, 10, 5]
GRID_B = [.25, .5, .75, 1]
_OPTIMIZER = RMSprop(learning_rate=LEARNING_RATE)
oExp = Experiment()
oDataSet = DataSet()
base = np.loadtxt("Datasets/artifitial1.data", usecols=range(1), delimiter=",")
classes = np.loadtxt("Datasets/artifitial1.data", usecols=-1, delimiter=",")
for x, y in enumerate(base):
oDataSet.add_sample_of_attribute(
np.array(list([np.float32(y)]) + [classes[x]]))
oDataSet.attributes = oDataSet.attributes.astype(float)
oDataSet.normalize_data_set()
oDataSet.labels = np.array([classes]).T
for j in range(10):
slices = KFold(n_splits=K_FOLD, shuffle=True)
oData = Data(1, 31, samples=50)
indices = np.arange(oDataSet.attributes.shape[0])
np.random.shuffle(indices)
