In [1]:

    

%matplotlib inline


    

In [2]:

    

import os, sys
sys.path.append(os.path.abspath('../../main/python'))

import numpy as np
import matplotlib.pyplot as plt

import thalesians.tsa.distrs as distrs


    

In [3]:

    

normal_distr = distrs.NormalDistr([3., 7.], [[4., -3.], [-3., 9.]])


    

In [4]:

    

normal_distr


    

    
Out[4]:





NormalDistr(mean=[[3.]
 [7.]], cov=[[ 4. -3.]
 [-3.  9.]])

In [5]:

    

normal_distr.mean


    

    
Out[5]:





array([[3.],
       [7.]])

In [6]:

    

normal_distr.cov


    

    
Out[6]:





array([[ 4., -3.],
       [-3.,  9.]])

In [7]:

    

data = normal_distr.sample(1000)


    

In [8]:

    

np.shape(data)


    

    
Out[8]:





(1000, 2)

In [9]:

    

plt.plot(data, 'x');


    

In [10]:

    

empirical_distr = distrs.EmpiricalDistr(data)


    

In [11]:

    

empirical_distr.particle_count


    

    
Out[11]:





1000

In [12]:

    

empirical_distr.dim


    

    
Out[12]:





2

In [13]:

    

empirical_distr.weight_sum


    

    
Out[13]:





1.0000000000000004

In [14]:

    

empirical_distr.mean


    

    
Out[14]:





array([[3.03771919],
       [7.13587533]])

In [15]:

    

empirical_distr.var


    

    
Out[15]:





array([[3.90916885],
       [8.37304867]])

In [16]:

    

empirical_distr.cov


    

    
Out[16]:





array([[ 3.90916885, -2.65787735],
       [-2.65787735,  8.37304867]])

In [17]:

    

empirical_distr.vol


    

In [18]:

    

empirical_distr.var_n


    

    
Out[18]:





array([[3.90916885],
       [8.37304867]])

In [19]:

    

empirical_distr.cov_n


    

    
Out[19]:





array([[ 3.90916885, -2.65787735],
       [-2.65787735,  8.37304867]])

In [20]:

    

empirical_distr.vol_n


    

    
Out[20]:





array([[ 1.97716182,  0.        ],
       [-1.34428924,  2.56240807]])

In [21]:

    

empirical_distr.var_n_minus_1


    

    
Out[21]:





array([[8.80266568e+15],
       [1.88544294e+16]])

In [22]:

    

empirical_distr.cov_n_minus_1


    

    
Out[22]:





array([[ 8.80266568e+15, -5.98500773e+15],
       [-5.98500773e+15,  1.88544294e+16]])

In [23]:

    

empirical_distr.vol_n_minus_1


    

    
Out[23]:





array([[ 9.38225222e+07,  0.00000000e+00],
       [-6.37907358e+07,  1.21594290e+08]])

In [24]:

    

empirical_distr


    

    
Out[24]:





EmpiricalDistr(mean=[[3.03771919]
 [7.13587533]], cov=[[ 3.90916885 -2.65787735]
 [-2.65787735  8.37304867]], particle_count=1000, dim=2)