from matplotlib import cm
import numpy as np
from MachineLearn.Classes import Experiment, DataSet, Data
from T4.Perceptron import Layered_perceptron_Logistic
COLOR = cm.rainbow(np.linspace(0, 1, 5))
learning_rate = 0.01
epochs = 5000
oExp = Experiment()
oDataSet = DataSet()
base = np.loadtxt("Datasets/dermatology.data",
usecols=range(34),
dtype=int,
delimiter=",")
classes = np.loadtxt("Datasets/dermatology.data",
dtype=int,
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)
import numpy as np
from matplotlib.lines import Line2D
from MachineLearn.Classes import Experiment
import matplotlib.pyplot as plt
oExp11 = Experiment.load("Objects/EXP01_1_LP_20.gzip".format())
oExp12 = Experiment.load("Objects/EXP01_2_LP_20.gzip".format())
oExp13 = Experiment.load("Objects/EXP01_3_LP_20.gzip".format())
oExp14 = Experiment.load("Objects/EXP01_4_LP_20.gzip".format())
oExp15 = Experiment.load("Objects/EXP01_5_LP_20.gzip".format())
COLORS = ['GREEN', 'RED', 'BLUE']
MARKER = ['o', '^', "*"]
base1 = np.loadtxt("Datasets/XOR.txt", delimiter=",")
print(oExp11)
print()
print(oExp12)
print()
print(oExp13)
print()
print(oExp14)
print()
print(oExp15)
print()
def getBestTrain(exp, name):
"""Etapa 1: Matriz confusao e grafico para melhor treinamento."""
oDataSet = exp.experimentResults[0]
import numpy as np
from matplotlib.lines import Line2D
from MachineLearn.Classes import Experiment
import matplotlib.pyplot as plt
oExp11 = Experiment.load("Objects/EXP02_1_LP_20.gzip".format())
oExp12 = Experiment.load("Objects/EXP02_2_LP_20.gzip".format())
oExp13 = Experiment.load("Objects/EXP02_3_LP_20.gzip".format())
COLORS = ['GREEN', 'RED', 'BLUE']
MARKER = ['o', '^', "*"]
base1 = np.loadtxt("Datasets/artifitial1.data", delimiter=",")
def imprimir_resultado(oexp, name, oData):
oDataSet = oexp.experimentResults[0]
print(
"EXPERIMENTO " + name + " MELHOR RESULTADO MSE: ",
oData.params['MSE'],
)
RMSE = []
MSE = []
for i in oDataSet.dataSet:
RMSE.append(i.params['RMSE'])
MSE.append(i.params['MSE'])
MSE = np.array(MSE)
RMSE = np.array(RMSE)
print("\tRMSE MEDIO ", np.mean(RMSE), "DESVIO", np.std(RMSE))
print("\tMSE MEDIO ", np.mean(MSE), "DESVIO", np.std(MSE))
from sklearn.metrics import accuracy_score, confusion_matrix, mean_squared_error
from sklearn.model_selection import KFold
from T5.Perceptron import multi_Layered_perceptron_Logistic
from T5.Perceptron_r import multi_Layered_perceptron_linear
import matplotlib.pyplot as plt
COLOR = cm.rainbow(np.linspace(0, 1, 5))
LEARNING_RATE = 0.1
epochs = 200
K_FOLD = 5
GRID = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
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(20):
slices = KFold(n_splits=K_FOLD)
oData = Data(1, 31, samples=50)
from matplotlib import cm
import numpy as np
from MachineLearn.Classes import Experiment, DataSet, Data
from T1.Perceptron import Perceptron
COLOR = cm.rainbow(np.linspace(0, 1, 5))
learning_rate = 0.01
epochs = 5000
oExp = Experiment()
oDataSet = DataSet()
base = np.loadtxt("Datasets/iris_3.data", usecols=range(4), delimiter=",")
classes = np.loadtxt("Datasets/iris_3.data",
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(2, 31, samples=50)
oData.random_training_test_by_percent([100, 50], 0.8)
perc = Perceptron(learning_rate)
perc.train(oDataSet.attributes[oData.Training_indexes],
oDataSet.labels[oData.Training_indexes], epochs)
def binarizer(mat):
result = np.zeros((mat.shape[0], 2))
for i in range(mat.shape[0]):
result[i, int(mat[i, 0])] = 1
return result
COLOR = cm.rainbow(np.linspace(0, 1, 5))
LEARNING_RATE = 0.1
epochs = 300
K_FOLD = 3
GRID_NEURON = [5, 10, 15, 20]
GRID_B = [.25, .5, .75, 1]
_OPTIMIZER = SGD(lr=LEARNING_RATE, momentum=0.0, decay=0.0, nesterov=False)
oExp = Experiment()
oDataSet = DataSet()
base = np.loadtxt("Datasets/breast-cancer-wisconsin.data",
usecols=range(1, 10),
dtype=int,
delimiter=",")
classes = np.loadtxt("Datasets/breast-cancer-wisconsin.data",
dtype=int,
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)
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
from T4.Perceptron import Layered_perceptron_Logistic
COLOR = cm.rainbow(np.linspace(0, 1, 5))
learning_rate = 0.10
epochs = 30000
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()
oExp.add_data_set(oDataSet,
description=" Experimento COLUNA 3C LP 20 realizaçoes. com 30000 epocas".format())
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_Logistic(learning_rate, len(oDataSet.labelsNames))
perc.train(oDataSet.attributes[oData.Training_indexes], oDataSet.labels[oData.Training_indexes], epochs)
from matplotlib import cm
import numpy as np
import matplotlib.pyplot as plt
from MachineLearn.Classes import Experiment, DataSet, Data
from T2.Perceptron import Perceptron_Adaline
COLOR = cm.rainbow(np.linspace(0, 1, 5))
learning_rate = 0.1
epochs = 5000
oExp = Experiment()
oDataSet = DataSet()
base = np.loadtxt("Datasets/dt_1.txt", usecols=range(1), delimiter=" ")
classes = np.loadtxt("Datasets/dt_1.txt", 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()
classes = np.array([classes]).T
for j in range(20):
print(j)
oData = Data(2, 31, samples=50)
indices = np.arange(oDataSet.attributes.shape[0])
np.random.shuffle(indices)
oData.Testing_indexes = indices[int(oDataSet.attributes.shape[0] * 0.85):]
oData.Training_indexes = indices[:int(oDataSet.attributes.shape[0] * 0.85)]
perc = Perceptron_Adaline(learning_rate)
import numpy as np
from matplotlib.lines import Line2D
from MachineLearn.Classes import Experiment
import matplotlib.pyplot as plt
oExp11 = Experiment.load("Objects/EXP01_DT1_20.gzip".format())
COLORS = ['GREEN', 'RED', 'BLUE']
base1 = np.loadtxt("Datasets/dt_1.txt", delimiter=" ")
# Etapa 1
def getBestTrain(exp):
"""Etapa 1: Matriz confusao e grafico para melhor treinamento."""
oDataSet = exp.experimentResults[0]
best = 1000000
oBestData = None
for oData in oDataSet.dataSet:
txAcc = oData.params['MSE']
if txAcc < best:
best = txAcc
oBestData = oData
return oBestData
oData11 = getBestTrain(oExp11)
oDataSet11 = oExp11.experimentResults[0]
print("EXPERIMENTO 1 MELHOR RESULTADO", oData11.params)
RMSE = []
MSE = []
from matplotlib import cm
import numpy as np
from MachineLearn.Classes import Experiment, DataSet, Data
from T4.Perceptron import Layered_perceptron_Logistic
COLOR = cm.rainbow(np.linspace(0, 1, 5))
learning_rate = 0.01
epochs = 5000
oExp = Experiment()
oDataSet = DataSet()
base = np.loadtxt("Datasets/iris.data", usecols=[2, 3], delimiter=",")
classes = np.loadtxt("Datasets/iris.data",
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([50, 50, 50], 0.8)
perc = Layered_perceptron_Logistic(learning_rate,
len(oDataSet.labelsNames))
from MachineLearn.Classes import Experiment, DataSet, Data
from sklearn.metrics import accuracy_score, confusion_matrix, mean_squared_error
from sklearn.model_selection import KFold
from T5.Perceptron_r import multi_Layered_perceptron_linear
import matplotlib.pyplot as plt
COLOR = cm.rainbow(np.linspace(0, 1, 5))
LEARNING_RATE = 0.01
epochs = 200
K_FOLD = 5
GRID = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
oExp = Experiment()
oDataSet = DataSet()
base = np.loadtxt("Datasets/pmsm_temperature_data.csv",
usecols=[0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12],
skiprows=1,
delimiter=",")
classes = np.loadtxt("Datasets/pmsm_temperature_data.csv",
usecols=[8],
skiprows=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()
from keras.utils import to_categorical
from rbflayer import RBFLayer, InitCentersRandom
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/measurements.csv", usecols=range(7), delimiter=",")
classes = np.loadtxt("Datasets/measurements.csv", 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)
import numpy as np
from matplotlib.lines import Line2D
from MachineLearn.Classes import Experiment
import matplotlib.pyplot as plt
oExp11 = Experiment.load("Objects/EXP01_1_PS_20.gzip".format())
oExp12 = Experiment.load("Objects/EXP01_2_PS_20.gzip".format())
oExp13 = Experiment.load("Objects/EXP01_3_PS_20.gzip".format())
oExp2 = Experiment.load("Objects/EXP02_PS_20.gzip".format())
COLORS = ['RED', 'BLUE']
print(oExp11)
print()
print(oExp12)
print()
print(oExp13)
print()
print(oExp2)
print()
# Etapa 1
def getBestTrain(exp, name):
"""Etapa 1: Matriz confusao e grafico para melhor treinamento."""
oDataSet = exp.experimentResults[0]
best = 0
oBestData = None
for oData in oDataSet.dataSet:
txAcc = oData.get_metrics()[1, -1]
if txAcc > best:
