Python read_data Exemples

Exemple #1

Fichier : linear_example.py Projet : vighneshanand/ML-DL-implementation

from MLlib.models import LinearRegression
from MLlib.optimizers import Adam
from MLlib.loss_func import MeanSquaredError
from MLlib.utils.misc_utils import read_data, printmat

X, Y = read_data('datasets/linear_reg_00.txt')

linear_model = LinearRegression()

optimizer = Adam(0.01, MeanSquaredError)

linear_model.fit(X, Y, optimizer=optimizer, epochs=200, zeros=False)

printmat('predictions', linear_model.predict(X))

linear_model.save('test')

Exemple #2

Fichier : Polynomial_example.py Projet : venkateshjnv123/ML-DL-implementation

from MLlib.models import PolynomialRegression
from MLlib.optimizers import Adam
from MLlib.loss_func import MeanSquaredError
from MLlib.utils.misc_utils import read_data, printmat

X, Y = read_data('datasets/Polynomial_reg.txt')

polynomial_model = PolynomialRegression(3)  # degree as user's choice

optimizer = Adam(0.01, MeanSquaredError)

polynomial_model.fit(X, Y, optimizer=optimizer, epochs=200, zeros=True)

printmat('predictions', polynomial_model.predict(X))

Z = polynomial_model.predict(X)

polynomial_model.save('test')

polynomial_model.plot(X, Y, Z, optimizer=optimizer, epochs=200, zeros=True)

Exemple #3

Fichier : numerical_outliers_example.py Projet : venkateshjnv123/ML-DL-implementation

from MLlib.utils.misc_utils import read_data
from MLlib.models import Numerical_outliers

x, y = read_data("datasets/numerical_outliers.txt")

Numerical_outliers.get_outliers(y[0])

Exemple #4

Fichier : logistic_example.py Projet : venkateshjnv123/ML-DL-implementation

from MLlib.models import LogisticRegression
from MLlib.optimizers import Adam
from MLlib.loss_func import LogarithmicError
from MLlib.utils.misc_utils import read_data, printmat

X, Y = read_data('datasets/logistic_reg_00.txt')

linear_model = LogisticRegression()

optimizer = Adam(0.03, LogarithmicError)

linear_model.fit(X, Y, optimizer=optimizer, epochs=200, zeros=False)

printmat('predictions', linear_model.predict(X))

linear_model.Plot(X,
                  Y,
                  linear_model.classify(X),
                  optimizer=optimizer,
                  epochs=200,
                  zeros=False)

linear_model.save('test')

Exemple #5

from MLlib.models import LogisticRegression
from MLlib.optimizers import Adam
from MLlib.loss_func import LogarithmicError
from MLlib.utils.misc_utils import read_data, printmat

X, Y = read_data('MLlib/datasets/logistic_reg_00.txt')

linear_model = LogisticRegression()

optimizer = Adam(0.03, LogarithmicError)

linear_model.fit(X, Y, optimizer=optimizer, epochs=200, zeros=False)

printmat('predictions', linear_model.predict(X))

linear_model.save('test')

Exemple #6

from MLlib.models import LinearRegression
from MLlib.optimizers import Adam
from MLlib.loss_func import MeanSquaredError
from MLlib.utils.misc_utils import read_data, printmat


X, Y = read_data('MLlib/datasets/linear_reg_00.txt')

linear_model = LinearRegression()

optimizer = Adam(0.001, MeanSquaredError)

linear_model.fit(X, Y, optimizer=optimizer, epochs=200, zeros=False)

printmat('predictions', linear_model.predict(X))

linear_model.save('test')

Exemple #7

import numpy as np
from MLlib.metrics import matrix_evolution
from MLlib.utils.misc_utils import read_data
p, y = read_data("datasets/metrics_dataset.txt")
z = np.transpose(p)
x = z[0]
matrix = matrix_evolution.confusion_matrix(x, y[0])
print(matrix)
matrix_evolution.score_metrics(x, y[0])

Exemple #8

from MLlib.utils.misc_utils import read_data
from MLlib.models import z_score

x, y = read_data("datasets/z_score_dataset.txt")

z_score.get_outlier(y[0], threshold_value=3)
# threshold_value as per user's choice