Here are the examples of the python api numpy.atleast_2d taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
145 Examples
3
View Complete Implementation : neuralnetwork.py
Copyright GNU General Public License v3.0
Author : Abhs9
Copyright GNU General Public License v3.0
Author : Abhs9
def predict(self, X, add_bias=True):
# Initialise the output prediction as the input features. This value will be (forward) propagated through the
# network to obtain the final prediction
p = np.atleast_2d(X)
# Check to see if the bias column should be added
if add_bias:
# Insert a column of 1's as the last entry in the feature matrix
p = np.c_[p, np.ones((p.shape[0]))]
# Loop over the layers in the network
for layer in np.arange(0, len(self.W)):
# Compute the output prediction
p = self.sigmoid(np.dot(p, self.W[layer]))
# Return the predicted value
return p
3
View Complete Implementation : perceptron.py
Copyright GNU General Public License v3.0
Author : Abhs9
Copyright GNU General Public License v3.0
Author : Abhs9
def predict(self, X, add_bias=True):
# Ensure our input is a matrix
X = np.atleast_2d(X)
# Check to see if the bias column should be added
if add_bias:
# Insert a column of 1's as the last entry in the feature matrix
X = np.c_[X, np.ones((X.shape[0]))]
# Past the dot product of the input features and weight matrix through the step function
return self.step(np.dot(X, self.W))
3
View Complete Implementation : perceptron.py
Copyright GNU General Public License v3.0
Author : Abhs9
Copyright GNU General Public License v3.0
Author : Abhs9
def predict(self, X, add_bias=True):
# Ensure our input is a matrix
X = np.atleast_2d(X)
# Check to see if the bias column should be added
if add_bias:
# Insert a column of 1's as the last entry in the feature matrix
X = np.c_[X, np.ones((X.shape[0]))]
# Past the dot product of the input features and weight matrix through the step function
return self.step(np.dot(X, self.W))
3
View Complete Implementation : neuralnetwork.py
Copyright GNU General Public License v3.0
Author : Abhs9
Copyright GNU General Public License v3.0
Author : Abhs9
def predict(self, X, add_bias=True):
# Initialise the output prediction as the input features. This value will be (forward) propagated through the
# network to obtain the final prediction
p = np.atleast_2d(X)
# Check to see if the bias column should be added
if add_bias:
# Insert a column of 1's as the last entry in the feature matrix
p = np.c_[p, np.ones((p.shape[0]))]
# Loop over the layers in the network
for layer in np.arange(0, len(self.W)):
# Compute the output prediction
p = self.sigmoid(np.dot(p, self.W[layer]))
# Return the predicted value
return p
3
View Complete Implementation : perceptron.py
Copyright GNU General Public License v3.0
Author : Abhs9
Copyright GNU General Public License v3.0
Author : Abhs9
def predict(self, X, add_bias=True):
# Ensure our input is a matrix
X = np.atleast_2d(X)
# Check to see if the bias column should be added
if add_bias:
# Insert a column of 1's as the last entry in the feature matrix
X = np.c_[X, np.ones((X.shape[0]))]
# Past the dot product of the input features and weight matrix through the step function
return self.step(np.dot(X, self.W))
3
View Complete Implementation : perceptron.py
Copyright GNU General Public License v3.0
Author : Abhs9
Copyright GNU General Public License v3.0
Author : Abhs9
def predict(self, X, add_bias=True):
# Ensure our input is a matrix
X = np.atleast_2d(X)
# Check to see if the bias column should be added
if add_bias:
# Insert a column of 1's as the last entry in the feature matrix
X = np.c_[X, np.ones((X.shape[0]))]
# Past the dot product of the input features and weight matrix through the step function
return self.step(np.dot(X, self.W))
3
View Complete Implementation : perceptron.py
Copyright GNU General Public License v3.0
Author : Abhs9
Copyright GNU General Public License v3.0
Author : Abhs9
def predict(self, X, add_bias=True):
# Ensure our input is a matrix
X = np.atleast_2d(X)
# Check to see if the bias column should be added
if add_bias:
# Insert a column of 1's as the last entry in the feature matrix
X = np.c_[X, np.ones((X.shape[0]))]
# Past the dot product of the input features and weight matrix through the step function
return self.step(np.dot(X, self.W))
3
View Complete Implementation : perceptron.py
Copyright GNU General Public License v3.0
Author : Abhs9
Copyright GNU General Public License v3.0
Author : Abhs9
def predict(self, X, add_bias=True):
# Ensure our input is a matrix
X = np.atleast_2d(X)
# Check to see if the bias column should be added
if add_bias:
# Insert a column of 1's as the last entry in the feature matrix
X = np.c_[X, np.ones((X.shape[0]))]
# Past the dot product of the input features and weight matrix through the step function
return self.step(np.dot(X, self.W))
3
View Complete Implementation : neuralnetwork.py
Copyright GNU General Public License v3.0
Author : Abhs9
Copyright GNU General Public License v3.0
Author : Abhs9
def predict(self, X, add_bias=True):
# Initialise the output prediction as the input features. This value will be (forward) propagated through the
# network to obtain the final prediction
p = np.atleast_2d(X)
# Check to see if the bias column should be added
if add_bias:
# Insert a column of 1's as the last entry in the feature matrix
p = np.c_[p, np.ones((p.shape[0]))]
# Loop over the layers in the network
for layer in np.arange(0, len(self.W)):
# Compute the output prediction
p = self.sigmoid(np.dot(p, self.W[layer]))
# Return the predicted value
return p
3
View Complete Implementation : neuralnetwork.py
Copyright GNU General Public License v3.0
Author : Abhs9
Copyright GNU General Public License v3.0
Author : Abhs9
def predict(self, X, add_bias=True):
# Initialise the output prediction as the input features. This value will be (forward) propagated through the
# network to obtain the final prediction
p = np.atleast_2d(X)
# Check to see if the bias column should be added
if add_bias:
# Insert a column of 1's as the last entry in the feature matrix
p = np.c_[p, np.ones((p.shape[0]))]
# Loop over the layers in the network
for layer in np.arange(0, len(self.W)):
# Compute the output prediction
p = self.sigmoid(np.dot(p, self.W[layer]))
# Return the predicted value
return p