ztlearn.ml.regression package

Submodules

ztlearn.ml.regression.base module

class ztlearn.ml.regression.base.Regression(epochs, loss='mean_squared_error', init_method='he_uniform', optimizer={}, penalty='ridge', penalty_weight=0.5, l1_ratio=0.5)[source]

Bases: object

fit[source]
predict(inputs)[source]

ztlearn.ml.regression.elasticnet module

class ztlearn.ml.regression.elasticnet.ElasticNetRegression(degree=2, epochs=100, loss='mean_squared_error', init_method='random_normal', optimizer={}, penalty='elastic', penalty_weight=0.5, l1_ratio=0.5)[source]

Bases: ztlearn.ml.regression.base.Regression

fit(inputs, targets, verbose=False, normalized=True)[source]
predict(inputs, normalized=True)[source]

ztlearn.ml.regression.linear module

class ztlearn.ml.regression.linear.LinearRegression(epochs=100, loss='mean_squared_error', init_method='random_normal', optimizer={}, penalty='ridge', penalty_weight=0.5, l1_ratio=0.5)[source]

Bases: ztlearn.ml.regression.base.Regression

fit(inputs, targets, verbose=False)[source]
fit_OLS[source]

ztlearn.ml.regression.logistic module

class ztlearn.ml.regression.logistic.LogisticRegression(epochs, loss='binary_crossentropy', init_method='he_normal', optimizer={}, penalty='lasso', penalty_weight=0, l1_ratio=0.5)[source]

Bases: object

fit[source]
fit_NR[source]

Newton-Raphson Method

predict(inputs)[source]

ztlearn.ml.regression.polynomial module

class ztlearn.ml.regression.polynomial.PolynomialRegression(degree=2, epochs=100, loss='mean_squared_error', init_method='random_normal', optimizer={}, penalty='ridge', penalty_weight=0.5, l1_ratio=0.5)[source]

Bases: ztlearn.ml.regression.base.Regression

fit(inputs, targets, verbose=False, normalized=True)[source]
predict(inputs, normalized=True)[source]

Module contents