Hướng dẫn dùng hyperboli python
To compute the Hyperbolic cosine, use the numpy.cosh() method in Python Numpy. The method is equivalent to 1/2 * (np.exp(x) + np.exp(-x)) and np.cos(1j*x). Returns the corresponding hyperbolic cosine values. This is a scalar if x is a scalar. The 1st parameter, x is input array. The 2nd and 3rd parameters are optional. Show The 2nd parameter is an ndarray, A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. The 3rd parameter is the condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. StepsAt first, import the required library − import numpy as np Get the Trigonometric Hyperbolic cosine. Find cosh − print("\nResult...",np.cosh(np.pi*1j)) Finding cosh 90 degrees − print("\nResult...",np.cosh(np.pi/2.)) Finding cosh 60 degrees − print("\nResult...",np.cosh(np.pi/3.)) Finding cosh 45 degrees − print("\nResult...",np.cosh(np.pi/4.)) Finding cosh 30 degrees − print("\nResult...",np.cosh(np.pi/6.)) Finding cosh 0 degrees − print("\nResult...",np.cosh(0)) Exampleimport numpy as np # To compute the Hyperbolic cosine, use the numpy.cosh() method in Python Numpy # The method is equivalent to 1/2 * (np.exp(x) + np.exp(-x)) and np.cos(1j*x). print("Get the Trigonometric Hyperbolic cosine...") # find cosh print("\nResult...",np.cosh(np.pi*1j)) # finding cosh 90 degrees print("\nResult...",np.cosh(np.pi/2.)) # finding cosh 60 degrees print("\nResult...",np.cosh(np.pi/3.)) # finding cosh 45 degrees print("\nResult...",np.cosh(np.pi/4.)) # finding cosh 30 degrees print("\nResult...",np.cosh(np.pi/6.)) # finding cosh 0 degrees print("\nResult...",np.cosh(0)) OutputGet the Trigonometric Hyperbolic cosine... Result... (-1+0j) Result... 2.5091784786580567 Result... 1.600286857702386 Result... 1.3246090892520057 Result... 1.1402383210764286 Result... 1.0 Updated on 25-Feb-2022 06:29:25
To compute the Hyperbolic cosine, use the numpy.cosh() method in Python Numpy. The method is equivalent to 1/2 * (np.exp(x) + np.exp(-x)) and np.cos(1j*x). Returns the corresponding hyperbolic cosine values. This is a scalar if x is a scalar. The 1st parameter, x is input array. The 2nd and 3rd parameters are optional. The 2nd parameter is an ndarray, A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. The 3rd parameter is the condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. StepsAt first, import the required library − import numpy as np Get the Trigonometric Hyperbolic cosine. Find cosh − print("\nResult...",np.cosh(np.pi*1j)) Finding cosh 90 degrees − print("\nResult...",np.cosh(np.pi/2.)) Finding cosh 60 degrees − print("\nResult...",np.cosh(np.pi/3.)) Finding cosh 45 degrees − print("\nResult...",np.cosh(np.pi/4.)) Finding cosh 30 degrees − print("\nResult...",np.cosh(np.pi/6.)) Finding cosh 0 degrees − print("\nResult...",np.cosh(0)) Exampleimport numpy as np # To compute the Hyperbolic cosine, use the numpy.cosh() method in Python Numpy # The method is equivalent to 1/2 * (np.exp(x) + np.exp(-x)) and np.cos(1j*x). print("Get the Trigonometric Hyperbolic cosine...") # find cosh print("\nResult...",np.cosh(np.pi*1j)) # finding cosh 90 degrees print("\nResult...",np.cosh(np.pi/2.)) # finding cosh 60 degrees print("\nResult...",np.cosh(np.pi/3.)) # finding cosh 45 degrees print("\nResult...",np.cosh(np.pi/4.)) # finding cosh 30 degrees print("\nResult...",np.cosh(np.pi/6.)) # finding cosh 0 degrees print("\nResult...",np.cosh(0)) OutputGet the Trigonometric Hyperbolic cosine... Result... (-1+0j) Result... 2.5091784786580567 Result... 1.600286857702386 Result... 1.3246090892520057 Result... 1.1402383210764286 Result... 1.0 Updated on 25-Feb-2022 06:29:25
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