# %% Initialize and Train Adaline
adaline = Adaline([0] * (adalineDimension), 0.1, lambda x: 1 if x >= 0 else -1)
xTrain = inputData[trainIndexes]
yTrain = outputData[trainIndexes]
adaline.train(xTrain, yTrain, tol, maxIterations)
# %% Test
xTest = inputData[testIndexes]
yTest = outputData[testIndexes]
testResult = adaline.test(xTest, yTest)
print(f"Mean Squared Error: {testResult}")
# %% Plot
adalineApproxYArr = adaline.evaluate(inputData)
weights = adaline.getWeights()
def hyperPlan(x):
return -(weights[0] * x + weights[2]) / weights[1]
xPlan = np.linspace(0.5, 5.5, 100)
yPlan = np.vectorize(hyperPlan)(xPlan)
fig = make_subplots(x_title="x", y_title="y")
# Add traces
fig.add_trace(
wi = [round(x, 4) for x in wi]
wf = [round(x, 4) for x in wf]
w = np.concatenate(([wi], [wf], [[ep]]), axis=1)
Treino = pd.DataFrame(data=w,
columns=[
'Wi0', 'Wi1', 'Wi2', 'Wi3', 'Wi4', 'Wf0', 'Wf1',
'Wf2', 'Wf3', 'Wf4', 'N_Epocas'
],
index=[f'T{i+1}'])
Treinamentos = pd.concat([Treinamentos, Treino])
resultado_parcial = pd.DataFrame(columns=[f'T{i+1}'])
respostas = []
for j in teste.values:
respostas.append(adaline.evaluate(j))
resultado_parcial = pd.DataFrame(data=respostas, columns=[f'T{i+1}'])
Resultado = pd.concat([Resultado, resultado_parcial], axis=1)
print(Treinamentos)
print(Resultado)
import matplotlib.pyplot as plt
plt.figure(figsize=(4, 8))
for i in range(5):
plt.subplot(5, 1, i + 1)
plt.plot(hist_EQM[i])
plt.title(f"T{i+1}")
plt.subplots_adjust(hspace=0.7)
# %% Initialize and Train Adaline
adaline = Adaline([0] * (xDimension + 1), 0.1)
xTrain = adalineXSamples[trainIndexes]
yTrain = ySamples[trainIndexes]
adaline.train(xTrain, yTrain, tol, maxIterations)
# %% Test
xTest = adalineXSamples[testIndexes]
yTest = ySamples[testIndexes]
testResult = adaline.test(xTest, yTest)
print(f"Mean Squared Error: {testResult}")
# %% Plot
adalineApproxYArr = adaline.evaluate(adalineXSamples)
fig = make_subplots(x_title="t", y_title="y")
# for xSample in xSamples.T:
# fig.add_trace(go.Scatter(x=timeSamples, y=xSample, name="Entrada"))
fig.add_trace(
go.Scatter(x=timeSamples, y=ySamples, name="Saida", mode='lines+markers'))
fig.add_trace(
go.Scatter(x=timeSamples,
y=adalineApproxYArr,
name="Adaline",
mode='lines+markers'))
import pandas as pd
import matplotlib.pyplot as plt
from activation_functions import SignFunction
from adaline import Adaline
# Database dataset-treinamento:
dataset = pd.read_csv('database/dataset-treinamento_ADALINE.csv')
X = dataset.iloc[:, 0:4].values
d = dataset.iloc[:, 4:].values
adaline = Adaline(
X, d, 0.0025, 10**(-6), SignFunction
) # entrada, saída, taxa de ativação, precisão e função de ativação
values_eqm = adaline.train()
# Database dataset-teste:
dataset = pd.read_csv('database/dataset-teste_ADALINE.csv')
X_teste = dataset.iloc[:, 0:4].values
for x in X_teste:
y = adaline.evaluate(x)
print(f'Input: {x},Output: {y}')
# Plotando figura do problema
plt.plot(values_eqm)
plt.show()