manuelschmidt

/**
 * @file kde.c
 * @author Carl Boettiger, <[email protected]>
 * @section DESCRIPTION
 * Estimates the kernel density p(x) at a given value x from
 * an array of sample points.  Uses the default algorithm from
 * the R langauge's 'density' function.  Requires the GSL statistics
 * library.  
 *   
 * @section LICENCE

manuelschmidt

#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential

manuelschmidt

"""
Specifically, the Kullback–Leibler divergence from Q to P, denoted DKL(P‖Q), is
a measure of the information gained when one revises one's beliefs from the
prior probability distribution Q to the posterior probability distribution P. In
other words, it is the amount of information lost when Q is used to approximate
P.
"""
import numpy as np
from scipy.stats import entropy

manuelschmidt

var intrinsic:Vector.<Number> = new Vector.<Number>(9, true);
var intrinsicInverse:Vector.<Number> = new Vector.<Number>(9, true);
		
var R:Vector.<Number> = new Vector.<Number>( 9, true );
var t:Vector.<Number> = new Vector.<Number>( 3, true );

// SVD routine
var svd:SVD = new SVD();

// input homography[9] - 3x3 Matrix