Uploaded Test files

venv/Lib/site-packages/sklearn/linear_model/_glm/link.py

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+"""
+Link functions used in GLM
+"""
+
+# Author: Christian Lorentzen <lorentzen.ch@googlemail.com>
+# License: BSD 3 clause
+
+from abc import ABCMeta, abstractmethod
+
+import numpy as np
+from scipy.special import expit, logit
+
+
+class BaseLink(metaclass=ABCMeta):
+    """Abstract base class for Link functions."""
+
+    @abstractmethod
+    def __call__(self, y_pred):
+        """Compute the link function g(y_pred).
+
+        The link function links the mean y_pred=E[Y] to the so called linear
+        predictor (X*w), i.e. g(y_pred) = linear predictor.
+
+        Parameters
+        ----------
+        y_pred : array of shape (n_samples,)
+            Usually the (predicted) mean.
+        """
+
+    @abstractmethod
+    def derivative(self, y_pred):
+        """Compute the derivative of the link g'(y_pred).
+
+        Parameters
+        ----------
+        y_pred : array of shape (n_samples,)
+            Usually the (predicted) mean.
+        """
+
+    @abstractmethod
+    def inverse(self, lin_pred):
+        """Compute the inverse link function h(lin_pred).
+
+        Gives the inverse relationship between linear predictor and the mean
+        y_pred=E[Y], i.e. h(linear predictor) = y_pred.
+
+        Parameters
+        ----------
+        lin_pred : array of shape (n_samples,)
+            Usually the (fitted) linear predictor.
+        """
+
+    @abstractmethod
+    def inverse_derivative(self, lin_pred):
+        """Compute the derivative of the inverse link function h'(lin_pred).
+
+        Parameters
+        ----------
+        lin_pred : array of shape (n_samples,)
+            Usually the (fitted) linear predictor.
+        """
+
+
+class IdentityLink(BaseLink):
+    """The identity link function g(x)=x."""
+
+    def __call__(self, y_pred):
+        return y_pred
+
+    def derivative(self, y_pred):
+        return np.ones_like(y_pred)
+
+    def inverse(self, lin_pred):
+        return lin_pred
+
+    def inverse_derivative(self, lin_pred):
+        return np.ones_like(lin_pred)
+
+
+class LogLink(BaseLink):
+    """The log link function g(x)=log(x)."""
+
+    def __call__(self, y_pred):
+        return np.log(y_pred)
+
+    def derivative(self, y_pred):
+        return 1 / y_pred
+
+    def inverse(self, lin_pred):
+        return np.exp(lin_pred)
+
+    def inverse_derivative(self, lin_pred):
+        return np.exp(lin_pred)
+
+
+class LogitLink(BaseLink):
+    """The logit link function g(x)=logit(x)."""
+
+    def __call__(self, y_pred):
+        return logit(y_pred)
+
+    def derivative(self, y_pred):
+        return 1 / (y_pred * (1 - y_pred))
+
+    def inverse(self, lin_pred):
+        return expit(lin_pred)
+
+    def inverse_derivative(self, lin_pred):
+        ep = expit(lin_pred)
+        return ep * (1 - ep)