Piyush3dB

Bubble charts encode data in the area of circles. Although less perceptually-accurate than bar charts, they can pack hundreds of values into a small space. This implementation is an improvement on Static bubble chart adding mouse tooltip and cluster attributes. The data shows the Flare class hierarchy.

Piyush3dB

#!/usr/bin/env python
import numpy as np

# Specify length of signal
N = 512;

# Generate time domain signal
x = np.random.rand(N).astype('complex')

# Evaluate signal power in the time domain

Piyush3dB

m = np.matrix('5 2; -6 4; 1 1')
s = np.matrix('7; 9; 4')
np.linalg.inv(m.H * m) * m.H*s

Piyush3dB

import numpy as np

N = 20
Nfft = 64

#hn = np.random.rand(N)
hn = np.ones(N)

hnr = hn*np.arange(N)
Tg = np.real(np.fft.fft(hnr, Nfft) / np.fft.fft(hn, Nfft))

Piyush3dB

#!/usr/bin/env python

import numpy as np
import matplotlib.pyplot as plt
import pdb

# zoneplte

W = 96
H = 96

Piyush3dB

#!/usr/bin/env python

import numpy as np
import matplotlib.pyplot as plt
import pdb

def lms(u, d, M, step, leak=0, initCoeffs=None, N=None, returnCoeffs=False):

    if N is None:
        N = len(u)-M+1

Piyush3dB

In line: define $$\mathbf{Y}$$ as the network edges, ...

in block:

$$ P(\mathbf{Y} = \mathbf{y}|\mathbf{X}) = exp[{\theta } ^{T} g(\mathbf{y},\mathbf{X})]/k(\theta ) $$

Block equations:

Piyush3dB

#!/usr/bin/env python

import numpy as np
import matplotlib.pyplot as plt

C = 1
for N in range(20,300,70):

	tit = 'N = ' + str(N)
	

Piyush3dB

function [C r] = BlahutArimoto(p)

disp('BlahutArimoto')

% Capacity of discrete memoryless channel
% Blahut-Arimoto algorithm

% Input
% p: m x n matrix
% p is the transition matrix for a channel with m inputs and n outputs

Piyush3dB

#include <iostream>
using namespace std;

int main()
{
    int arr[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
    for(auto i : arr){
        cout << arr[i] <<endl;
    }
    return 0;