Illustration bivariate
¶
In [1]:
import bivariate
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pyvinecopulib as cop
import scipy.stats as st
In [3]:
X_1 = st.norm(loc=3, scale=1)
X_2 = st.norm(loc=5, scale=1)
n = 10000
X_1_samples = X_1.rvs(size=n)
X_2_samples = X_2.rvs(size=n)
X_combined_samples = np.array([X_1_samples, X_2_samples]).T
X_class_A = bivariate.class_copula.Region_of_interest(
random_samples=X_combined_samples)
X_class_A.plot_emperical_contours(bandwidth=4)
def underwater(X1,X2):
Z_now = 10.0
function = (Z_now - X1 - X2 <= 0)
return function
X_class_A.function = underwater
X_class_A.inside_function()
X_class_A.plot_inside_function();
In [7]:
# define multivariate normal distribution
X = st.multivariate_normal(mean=[3, 5],
cov=[[1, 0.5],
[0.5, 1]])
n = 10000
X_samples = X.rvs(size=n)
X_class_A = bivariate.class_copula.Region_of_interest(
random_samples=X_samples)
X_class_A.plot_emperical_contours(bandwidth=4)
def underwater(X1,X2):
Z_now = 10.0
function = (Z_now - X1 - X2 <= 0)
return function
X_class_A.function = underwater
X_class_A.inside_function()
X_class_A.plot_inside_function();
End of notebook.
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