Python/neural_network/activation_functions/binary_step.py at master · manmita/Python · GitHub

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"""
This script demonstrates the implementation of the Binary Step function.

It's an activation function in which the neuron is activated if the input is positive
or 0, else it is deactivated

It's a simple activation function which is mentioned in this wikipedia article:
https://en.wikipedia.org/wiki/Activation_function
"""


importnumpyasnp


defbinary_step(vector: np.ndarray) ->np.ndarray:
"""
 Implements the binary step function

 Parameters:
 vector (ndarray): A vector that consists of numeric values

 Returns:
 vector (ndarray): Input vector after applying binary step function

 >>> vector = np.array([-1.2, 0, 2, 1.45, -3.7, 0.3])
 >>> binary_step(vector)
 array([0, 1, 1, 1, 0, 1])
 """

returnnp.where(vector>=0, 1, 0)


if__name__=="__main__":
importdoctest

doctest.testmod()
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"""
This script demonstrates the implementation of the Binary Step function.

It's an activation function in which the neuron is activated if the input is positive
or 0, else it is deactivated

It's a simple activation function which is mentioned in this wikipedia article:
https://en.wikipedia.org/wiki/Activation_function
"""


importnumpyasnp


defbinary_step(vector: np.ndarray) ->np.ndarray:
"""
 Implements the binary step function

 Parameters:
 vector (ndarray): A vector that consists of numeric values

 Returns:
 vector (ndarray): Input vector after applying binary step function

 >>> vector = np.array([-1.2, 0, 2, 1.45, -3.7, 0.3])
 >>> binary_step(vector)
 array([0, 1, 1, 1, 0, 1])
 """

returnnp.where(vector>=0, 1, 0)


if__name__=="__main__":
importdoctest

doctest.testmod()
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"""
This script demonstrates the implementation of the Binary Step function.

It's an activation function in which the neuron is activated if the input is positive
or 0, else it is deactivated

It's a simple activation function which is mentioned in this wikipedia article:
https://en.wikipedia.org/wiki/Activation_function
"""


importnumpyasnp


defbinary_step(vector: np.ndarray) ->np.ndarray:
"""
 Implements the binary step function

 Parameters:
 vector (ndarray): A vector that consists of numeric values

 Returns:
 vector (ndarray): Input vector after applying binary step function

 >>> vector = np.array([-1.2, 0, 2, 1.45, -3.7, 0.3])
 >>> binary_step(vector)
 array([0, 1, 1, 1, 0, 1])
 """

returnnp.where(vector>=0, 1, 0)


if__name__=="__main__":
importdoctest

doctest.testmod()
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"""
This script demonstrates the implementation of the Binary Step function.

It's an activation function in which the neuron is activated if the input is positive
or 0, else it is deactivated

It's a simple activation function which is mentioned in this wikipedia article:
https://en.wikipedia.org/wiki/Activation_function
"""


importnumpyasnp


defbinary_step(vector: np.ndarray) ->np.ndarray:
"""
 Implements the binary step function

 Parameters:
 vector (ndarray): A vector that consists of numeric values

 Returns:
 vector (ndarray): Input vector after applying binary step function

 >>> vector = np.array([-1.2, 0, 2, 1.45, -3.7, 0.3])
 >>> binary_step(vector)
 array([0, 1, 1, 1, 0, 1])
 """

returnnp.where(vector>=0, 1, 0)


if__name__=="__main__":
importdoctest

doctest.testmod()
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"""
This script demonstrates the implementation of the Binary Step function.

It's an activation function in which the neuron is activated if the input is positive
or 0, else it is deactivated

It's a simple activation function which is mentioned in this wikipedia article:
https://en.wikipedia.org/wiki/Activation_function
"""


importnumpyasnp


defbinary_step(vector: np.ndarray) ->np.ndarray:
"""
 Implements the binary step function

 Parameters:
 vector (ndarray): A vector that consists of numeric values

 Returns:
 vector (ndarray): Input vector after applying binary step function

 >>> vector = np.array([-1.2, 0, 2, 1.45, -3.7, 0.3])
 >>> binary_step(vector)
 array([0, 1, 1, 1, 0, 1])
 """

returnnp.where(vector>=0, 1, 0)


if__name__=="__main__":
importdoctest

doctest.testmod()
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"""
This script demonstrates the implementation of the Binary Step function.

It's an activation function in which the neuron is activated if the input is positive
or 0, else it is deactivated

It's a simple activation function which is mentioned in this wikipedia article:
https://en.wikipedia.org/wiki/Activation_function
"""


importnumpyasnp


defbinary_step(vector: np.ndarray) ->np.ndarray:
"""
 Implements the binary step function

 Parameters:
 vector (ndarray): A vector that consists of numeric values

 Returns:
 vector (ndarray): Input vector after applying binary step function

 >>> vector = np.array([-1.2, 0, 2, 1.45, -3.7, 0.3])
 >>> binary_step(vector)
 array([0, 1, 1, 1, 0, 1])
 """

returnnp.where(vector>=0, 1, 0)


if__name__=="__main__":
importdoctest

doctest.testmod()
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"""
This script demonstrates the implementation of the Binary Step function.

It's an activation function in which the neuron is activated if the input is positive
or 0, else it is deactivated

It's a simple activation function which is mentioned in this wikipedia article:
https://en.wikipedia.org/wiki/Activation_function
"""


importnumpyasnp


defbinary_step(vector: np.ndarray) ->np.ndarray:
"""
 Implements the binary step function

 Parameters:
 vector (ndarray): A vector that consists of numeric values

 Returns:
 vector (ndarray): Input vector after applying binary step function

 >>> vector = np.array([-1.2, 0, 2, 1.45, -3.7, 0.3])
 >>> binary_step(vector)
 array([0, 1, 1, 1, 0, 1])
 """

returnnp.where(vector>=0, 1, 0)


if__name__=="__main__":
importdoctest

doctest.testmod()
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"""
This script demonstrates the implementation of the Binary Step function.

It's an activation function in which the neuron is activated if the input is positive
or 0, else it is deactivated

It's a simple activation function which is mentioned in this wikipedia article:
https://en.wikipedia.org/wiki/Activation_function
"""


importnumpyasnp


defbinary_step(vector: np.ndarray) ->np.ndarray:
"""
 Implements the binary step function

 Parameters:
 vector (ndarray): A vector that consists of numeric values

 Returns:
 vector (ndarray): Input vector after applying binary step function

 >>> vector = np.array([-1.2, 0, 2, 1.45, -3.7, 0.3])
 >>> binary_step(vector)
 array([0, 1, 1, 1, 0, 1])
 """

returnnp.where(vector>=0, 1, 0)


if__name__=="__main__":
importdoctest

doctest.testmod()