__author__ = "kyubyong. kbpark.linguist@gmail.com"

import numpy as np

np.__version__

"""
Explanation: Statistics
End of explanation
"""


x = np.arange(4).reshape((2, 2))
print("x=\n", x)


"""
Explanation: Order statistics
Q1. Return the minimum value of x along the second axis.
End of explanation
"""


x = np.arange(4).reshape((2, 2))
print("x=\n", x)


"""
Explanation: Q2. Return the maximum value of x along the second axis. Reduce the second axis to the dimension with size one.
End of explanation
"""


x = np.arange(10).reshape((2, 5))
print("x=\n", x)



"""
Explanation: Q3. Calcuate the difference between the maximum and the minimum of x along the second axis.
End of explanation
"""


x = np.arange(1, 11).reshape((2, 5))
print("x=\n", x)


"""
Explanation: Q4. Compute the 75th percentile of x along the second axis.
End of explanation
"""


x = np.arange(1, 10).reshape((3, 3))
print("x=\n", x)


"""
Explanation: Averages and variances
Q5. Compute the median of flattened x.
End of explanation
"""


x = np.arange(5)
weights = np.arange(1, 6)


"""
Explanation: Q6. Compute the weighted average of x.
End of explanation
"""


x = np.arange(5)
print("x=\n",x)



"""
Explanation: Q7. Compute the mean, standard deviation, and variance of x along the second axis.
End of explanation
"""


x = np.array([0, 1, 2])
y = np.array([2, 1, 0])


"""
Explanation: Correlating
Q8. Compute the covariance matrix of x and y.
End of explanation
"""


x = np.array([0, 1, 3])
y = np.array([2, 4, 5])


"""
Explanation: Q9. In the above covariance matrix, what does the -1 mean?
Q10. Compute Pearson product-moment correlation coefficients of x and y.
End of explanation
"""


x = np.array([0, 1, 3])
y = np.array([2, 4, 5])



"""
Explanation: Q11. Compute cross-correlation of x and y.
End of explanation
"""


x = np.array([0.5, 0.7, 1.0, 1.2, 1.3, 2.1])
bins = np.array([0, 1, 2, 3])
print("ans=\n", ...)

import matplotlib.pyplot as plt
%matplotlib inline
plt.hist(x, bins=bins)
plt.show()

"""
Explanation: Histograms
Q12. Compute the histogram of x against the bins.
End of explanation
"""


xedges = [0, 1, 2, 3]
yedges = [0, 1, 2, 3, 4]
x = np.array([0, 0.1, 0.2, 1., 1.1, 2., 2.1])
y = np.array([0, 0.1, 0.2, 1., 1.1, 2., 3.3])
...

plt.scatter(x, y)
plt.grid()

"""
Explanation: Q13. Compute the 2d histogram of x and y.
End of explanation
"""


x = np.array([0, 1, 1, 3, 2, 1, 7])


"""
Explanation: Q14. Count number of occurrences of 0 through 7 in x.
End of explanation
"""


x = np.array([0.2, 6.4, 3.0, 1.6])
bins = np.array([0.0, 1.0, 2.5, 4.0, 10.0])



"""
Explanation: Q15. Return the indices of the bins to which each value in x belongs.
End of explanation
"""


