def predict(self, X):
"""
Predict the Probabilistic Value of
Input, in accordance with
Logistic Regression Model.
PARAMETERS
==========
X: ndarray(dtype=float,ndim=1)
1-D Array of Dataset's Input.
prediction: ndarray(dtype=float,ndim=1)
1-D Array of Predicted Values
corresponding to each Input of
Dataset.
RETURNS
=======
ndarray(dtype=float,ndim=1)
1-D Array of Probabilistic Values
of whether the particular Input
belongs to class 0 or class 1.
"""
prediction = np.dot(X, self.weights).T
return sigmoid(prediction)
def classify(self, X):
prediction = np.dot(X, self.weights).T
prediction = sigmoid(prediction)
actual_predictions = np.zeros((1, X.shape[0]))
for i in range(prediction.shape[1]):
if prediction[0][i] > 0.5:
actual_predictions[0][i] = 1
return actual_predictions
def classify(self, X):
"""
Classify the Input, according to
Logistic Regression Model,i.e in this
case, either class 0 or class 1.
PARAMETERS
==========
X: ndarray(dtype=float,ndim=1)
1-D Array of Dataset's Input.
prediction: ndarray(dtype=float,ndim=1)
1-D Array of Predicted Values
corresponding to their Inputs.
actual_predictions: ndarray(dtype=int,ndim=1)
1-D Array of Output, associated
to each Input of Dataset,
Predicted by Trained Logistic
Regression Model.
RETURNS
=======
ndarray
1-D Array of Predicted classes
(either 0 or 1) corresponding
to their inputs.
"""
prediction = np.dot(X, self.weights).T
prediction = sigmoid(prediction)
actual_predictions = np.zeros((1, X.shape[0]))
for i in range(prediction.shape[1]):
if prediction[0][i] > 0.5:
actual_predictions[0][i] = 1
return actual_predictions
def derivative(X, Y, W):
"""
Calculate derivative for logarithmic error method.
PARAMETERS
==========
X:ndarray(dtype=float,ndim=1)
input vector
Y:ndarray(dtype=float)
output vector
W:ndarray(dtype=float)
Weights
RETURNS
=======
array of derivates
"""
M = X.shape[0]
H = sigmoid(np.dot(X, W).T)
return (1 / M) * (np.dot(X.T, (H - Y).T))
def loss(X, Y, W):
"""
Calculate loss by logarithmic error method.
PARAMETERS
==========
X:ndarray(dtype=float,ndim=1)
input vector
Y:ndarray(dtype=float)
output vector
W:ndarray(dtype=float)
Weights
RETURNS
=======
array of logarithmic losses
"""
M = X.shape[0]
H = sigmoid(np.dot(X, W).T)
return (1 / M) * (np.sum((-Y) * np.log(H) - (1 - Y) * np.log(1 - H)))
def derivative(X, Y, W):
M = X.shape[0]
H = sigmoid(np.dot(X, W).T)
return (1 / M) * (np.dot(X.T, (H - Y).T))
def loss(X, Y, W):
M = X.shape[0]
H = sigmoid(np.dot(X, W).T)
return (1 / M) * (np.sum((-Y) * np.log(H) - (1 - Y) * np.log(1 - H)))
def predict(self, X):
prediction = np.dot(X, self.weights).T
return sigmoid(prediction)