In [2]:
import numpy as np
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np.__version__
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In [8]:
__author__ = "kyubyong. kbpark.linguist@gmail.com. https://github.com/kyubyong"
Q1. Calculate sine, cosine, and tangent of x, element-wise.
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x = np.array([0., 1., 30, 90])
Q2. Calculate inverse sine, inverse cosine, and inverse tangent of x, element-wise.
In [31]:
x = np.array([-1., 0, 1.])
Q3. Convert angles from radians to degrees.
In [45]:
x = np.array([-np.pi, -np.pi/2, np.pi/2, np.pi])
Q4. Convert angles from degrees to radians.
In [48]:
x = np.array([-180., -90., 90., 180.])
Q5. Calculate hyperbolic sine, hyperbolic cosine, and hyperbolic tangent of x, element-wise.
In [65]:
x = np.array([-1., 0, 1.])
Q6. Predict the results of these, paying attention to the difference among the family functions.
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x = np.array([2.1, 1.5, 2.5, 2.9, -2.1, -2.5, -2.9])
out1 = np.around(x)
out2 = np.floor(x)
out3 = np.ceil(x)
out4 = np.trunc(x)
out5 = [round(elem) for elem in x]
#print out1
#print out2
#print out3
#print out4
#print out5
Q7. Implement out5 in the above question using numpy.
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Q8. Predict the results of these.
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x = np.array(
[[1, 2, 3, 4],
[5, 6, 7, 8]])
outs = [np.sum(x),
np.sum(x, axis=0),
np.sum(x, axis=1, keepdims=True),
"",
np.prod(x),
np.prod(x, axis=0),
np.prod(x, axis=1, keepdims=True),
"",
np.cumsum(x),
np.cumsum(x, axis=0),
np.cumsum(x, axis=1),
"",
np.cumprod(x),
np.cumprod(x, axis=0),
np.cumprod(x, axis=1),
"",
np.min(x),
np.min(x, axis=0),
np.min(x, axis=1, keepdims=True),
"",
np.max(x),
np.max(x, axis=0),
np.max(x, axis=1, keepdims=True),
"",
np.mean(x),
np.mean(x, axis=0),
np.mean(x, axis=1, keepdims=True)]
for out in outs:
if out == "":
pass
#print
else:
pass
#print("->", out)
Q9. Calculate the difference between neighboring elements, element-wise.
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x = np.array([1, 2, 4, 7, 0])
Q10. Calculate the difference between neighboring elements, element-wise, and prepend [0, 0] and append[100] to it.
In [108]:
x = np.array([1, 2, 4, 7, 0])
Q11. Return the cross product of x and y.
In [110]:
x = np.array([1, 2, 3])
y = np.array([4, 5, 6])
Q12. Compute $e^x$, element-wise.
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x = np.array([1., 2., 3.], np.float32)
Q13. Calculate exp(x) - 1 for all elements in x.
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x = np.array([1., 2., 3.], np.float32)
Q14. Calculate $2^p$ for all p in x.
In [124]:
x = np.array([1., 2., 3.], np.float32)
Q15. Compute natural, base 10, and base 2 logarithms of x element-wise.
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x = np.array([1, np.e, np.e**2])
Q16. Compute the natural logarithm of one plus each element in x in floating-point accuracy.
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x = np.array([1e-99, 1e-100])
Q17. Return element-wise True where signbit is set.
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x = np.array([-3, -2, -1, 0, 1, 2, 3])
Q18. Change the sign of x to that of y, element-wise.
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x = np.array([-1, 0, 1])
y = -1.1
Q19. Add x and y element-wise.
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x = np.array([1, 2, 3])
y = np.array([-1, -2, -3])
Q20. Subtract y from x element-wise.
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x = np.array([3, 4, 5])
y = np.array(3)
Q21. Multiply x by y element-wise.
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x = np.array([3, 4, 5])
y = np.array([1, 0, -1])
Q22. Divide x by y element-wise in two different ways.
In [161]:
x = np.array([3., 4., 5.])
y = np.array([1., 2., 3.])
Q23. Compute numerical negative value of x, element-wise.
In [146]:
x = np.array([1, -1])
Q24. Compute the reciprocal of x, element-wise.
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x = np.array([1., 2., .2])
Q25. Compute $x^y$, element-wise.
In [163]:
x = np.array([[1, 2], [3, 4]])
y = np.array([[1, 2], [1, 2]])
Q26. Compute the remainder of x / y element-wise in two different ways.
In [168]:
x = np.array([-3, -2, -1, 1, 2, 3])
y = 2
Q27. If an element of x is smaller than 3, replace it with 3. And if an element of x is bigger than 7, replace it with 7.
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x = np.arange(10)
Q28. Compute the square of x, element-wise.
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x = np.array([1, 2, -1])
Q29. Compute square root of x element-wise.
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x = np.array([1., 4., 9.])
Q30. Compute the absolute value of x.
In [178]:
x = np.array([[1, -1], [3, -3]])
Q31. Compute an element-wise indication of the sign of x, element-wise.
In [181]:
x = np.array([1, 3, 0, -1, -3])
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