numpy.expm1(array, out = None, where = True, casting = ‘same_kind’, order = ‘K’, dtype = None) :
This mathematical function helps user to calculate exponential of all the elements subtracting 1 from all the input array elements.
Parameters :
array : [array_like]Input array or object whose elements, we need to test.
out : [ndarray, optional]Output array with same dimensions as Input array,
placed with result.
**kwargs : allows you to pass keyword variable length of argument to a function.
It is used when we want to handle named argument in a function.
where : [array_like, optional]True value means to calculate the universal
functions(ufunc) at that position, False value means to leave the
value in the output alone.
Return :
An array with exponential(all elements of input array) - 1.
Code 1 : Working
import numpy as np
in_array = [ 1 , 3 , 5 ]
print ( "Input array : \n" , in_array)
exp_values = np.exp(in_array)
print ( "\nExponential value of array element : "
"\n" , exp_values)
expm1_values = np.expm1(in_array)
print ( "\n(Exponential value of array element) - (1) "
": \n" , expm1_values)
|
Output :
Input array :
[1, 3, 5]
Exponential value of array element :
[ 2.71828183 20.08553692 148.4131591 ]
(Exponential value of array element) - (1) :
[ 1.71828183 19.08553692 147.4131591 ]
Code 2 : Graphical representation
import numpy as np
import matplotlib.pyplot as plt
in_array = [ 1 , 1.2 , 1.4 , 1.6 , 1.8 , 2 ]
out_array = np.expm1(in_array)
print ( "out_array : " , out_array)
y = [ 1 , 1.2 , 1.4 , 1.6 , 1.8 , 2 ]
plt.plot(in_array, y, color = 'blue' , marker = "*" )
plt.plot(out_array, y, color = 'red' , marker = "o" )
plt.title( "numpy.expm1()" )
plt.xlabel( "X" )
plt.ylabel( "Y" )
plt.show()
|
Output :
out_array : [ 1.71828183 2.32011692 3.05519997 3.95303242 5.04964746 6.3890561 ]
References :
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.expm1.html#numpy.expm1
.
Last Updated :
29 Nov, 2018
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