cheind

import cv2
import json
import pandas as pd
import numpy as np

def convert_to_pandas(content):
    events = []
    for obj in content:
        for f in obj['frames']:
            events.append({

cheind

__author__ = 'Christoph Heindl'
__copyright__ = 'Copyright 2017'
__license__ = 'BSD'

"""Trains a HMM based on gradient descent optimization. 

The parameters (theta) of the model are transition and 
emission probabilities, as well as the initial state probabilities. 

Given a start solution, the negative log likelihood of data given the 

cheind

__author__ = 'Christoph Heindl'
__copyright__ = 'Copyright 2017'
__license__ = 'BSD'

"""Trains a HMM based on gradient descent optimization. 

The parameters (theta) of the model are transition and 
emission probabilities, as well as the initial state probabilities. 

Given a start solution, the negative log likelihood of data given the 

cheind

import numpy as np
import matplotlib.pyplot as plt

# Relation of SVD to PCA and eigen-problems
# A = USV'
# A'A = VSU'USV' = VS^2V'
# A'AV = VS^2V'V
# A'AV = VS^2 
# which is an eigenvector problem. Means V are the eigenvectors of A'A.
# A similar argument leads to U being the eigenvectors AA'.

cheind

import numpy as np
import matplotlib.pyplot as plt


o3d = np.array([-100, -100, 50])
d3d = np.array([1, 1, 5.])
d3d /= np.linalg.norm(d3d)

t3d = np.arange(0, 100, 1.)
p3d = o3d + d3d * t3d[:, None]

cheind

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from matplotlib.patches import Ellipse
import math

# Kalman

def lkf_predict(x, P, A, B, u, Q):

cheind

/** 
  * C++ property implementation
  * Christoph Heindl 2011
  * christoph.heindl@gmail.com
  */

#pragma once

#include <cheind/properties/property.h>
#include <cheind/properties/policy_optional_value.h>