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/** | |
* C++ property implementation | |
* Christoph Heindl 2011 | |
* christoph.heindl@gmail.com | |
*/ | |
#pragma once | |
#include <cheind/properties/property.h> | |
#include <cheind/properties/policy_optional_value.h> |
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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): |
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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] |
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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'. |
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__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 |
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__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 |
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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({ |
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import numpy as np | |
from itertools import count | |
def perm_matrix(perm_indices): | |
'''Returns the permutation matrix corresponding to given permutation indices | |
Here `perm_indices` defines the permutation order in the following sense: | |
value `j` at index `i` will move row/column `j` of the original matrix to | |
row/column `i`in the permuated matrix P*M/M*P^T. | |
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from scipy.optimize import linprog | |
import numpy as np | |
import pandas as pd | |
def print_metrics(df): | |
print('Total staff costs', df.to_numpy().sum()) | |
print('Management cost ratio') | |
print(df.MgtStaffCosts / df.to_numpy().sum()) | |
print('Partner cost ratio') |
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