72 lines
2.1 KiB
Python
72 lines
2.1 KiB
Python
# Sebastian Raschka 2014-2020
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# mlxtend Machine Learning Library Extensions
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#
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# Base Clusteer (Clutering Parent Class)
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# Author: Sebastian Raschka <sebastianraschka.com>
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#
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# License: BSD 3 clause
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import numpy as np
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from time import time
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class _Regressor(object):
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def __init__(self):
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pass
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def _check_target_array(self, y, allowed=None):
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if not isinstance(y[0], (int, np.float)):
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raise AttributeError('y must be a float array.\nFound %s'
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% y.dtype)
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def fit(self, X, y, init_params=True):
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"""Learn model from training data.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape = [n_samples, n_features]
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Training vectors, where n_samples is the number of samples and
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n_features is the number of features.
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y : array-like, shape = [n_samples]
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Target values.
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init_params : bool (default: True)
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Re-initializes model parameters prior to fitting.
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Set False to continue training with weights from
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a previous model fitting.
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Returns
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-------
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self : object
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"""
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self._is_fitted = False
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self._check_arrays(X=X, y=y)
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self._check_target_array(y)
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if hasattr(self, 'self.random_seed') and self.random_seed:
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self._rgen = np.random.RandomState(self.random_seed)
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self._init_time = time()
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self._fit(X=X, y=y, init_params=init_params)
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self._is_fitted = True
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return self
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def predict(self, X):
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"""Predict targets from X.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape = [n_samples, n_features]
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Training vectors, where n_samples is the number of samples and
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n_features is the number of features.
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Returns
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----------
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target_values : array-like, shape = [n_samples]
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Predicted target values.
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"""
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self._check_arrays(X=X)
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if not self._is_fitted:
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raise AttributeError('Model is not fitted, yet.')
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return self._predict(X)
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