Matěj Šmíd smidm

## statsmodels_influence_plot_tooltip.ipynb

      
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                smidm
                / statsmodels_influence_plot_tooltip.ipynb
            
            
              Created
              October 26, 2021 06:23
            
              
                Tooltips for Outliers in Regression Influence Plots
              
          
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## flann_distance_test.py
from pyflann import *
import numpy as np

dataset = np.array(
    [[1., 1, 1, 2, 3],
     [10, 10, 10, 3, 2],
     [100, 100, 2, 30, 1]
     ])
testset = np.array(
    [[1., 1, 1, 1, 1],

## stable_matching_between_2_sets_of_points.py
import numpy as np
import scipy.optimize

a = np.array([[0, 0], [1, 1], [2, 2]])
b = np.array([[0, 1], [2, 2.2], [1, 1.2], [0, 0]])

indices = np.indices((len(a), len(b)))
# array([[[0, 0, 0, 0],
#         [1, 1, 1, 1],
#         [2, 2, 2, 2]],

## mot.py
import pandas as pd
import errno
import numpy as np
import sys
import warnings


def save_mot(filename, df):
    df.to_csv(filename, header=False, index=False)

## ellipse_bounding_box.ipynb

      
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                smidm
                / ellipse_bounding_box.ipynb
            
            
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              March 26, 2023 04:27
            
              
                Compute bounding box for an ellipse.
              
          
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## matplotlib_wait_or_exit.py
import matplotlib.pylab as plt
import numpy as np

fig = plt.figure()
closed = False

def handle_close(evt):
    global closed
    closed = True

## statnice-phd.txt
Množiny. Teorie matic. Soustavy lineárních rovnic.
	Gaussova eliminace
	Diskriminant
	Invertibilni matice, singularni matice
	Linearni zobrazeni, eigenvalues

Základy matematické analýzy. Metody aproximace funkcí. Metoda nejmenších čtverců. Aplikace pro hledání modelů z naměřených reálných dat.
	Lagrangeho multiplikatory
	Newtonova metoda
	Linearni regrese

## read-camera.py
#!/usr/bin/env python

from PySide.QtCore import *
from PySide.QtGui import *
import cv2
import sys

class MainApp(QWidget):

    def __init__(self):

## pkgconfig.py
import subprocess

def pkgconfig(*packages, **kw):
    """
    Query pkg-config for library compile and linking options. Return configuration in distutils
    Extension format.

    Usage:

    pkgconfig('opencv')

## concatenate, vstack, hstack accepting scalars.py
# modified numpy concatenate, vstack, hstack accepting scalars in the input tuple

def concatenate(tup, axis=0):
    tup_arrays = [x for x in tup if isinstance(x, np.ndarray)]
    shape = list(tup_arrays[0].shape)
    shape[axis] = 1

    tup_ = []
    for x in tup:
        if isinstance(x, (int, long, float)):
	from pyflann import *
	import numpy as np

	dataset = np.array(
	[[1., 1, 1, 2, 3],
	[10, 10, 10, 3, 2],
	[100, 100, 2, 30, 1]
	])
	testset = np.array(
	[[1., 1, 1, 1, 1],
	import numpy as np
	import scipy.optimize

	a = np.array([[0, 0], [1, 1], [2, 2]])
	b = np.array([[0, 1], [2, 2.2], [1, 1.2], [0, 0]])

	indices = np.indices((len(a), len(b)))
	# array([[[0, 0, 0, 0],
	# [1, 1, 1, 1],
	# [2, 2, 2, 2]],
	import pandas as pd
	import errno
	import numpy as np
	import sys
	import warnings


	def save_mot(filename, df):
	df.to_csv(filename, header=False, index=False)
	import matplotlib.pylab as plt
	import numpy as np

	fig = plt.figure()
	closed = False

	def handle_close(evt):
	global closed
	closed = True
	Množiny. Teorie matic. Soustavy lineárních rovnic.
	Gaussova eliminace
	Diskriminant
	Invertibilni matice, singularni matice
	Linearni zobrazeni, eigenvalues

	Základy matematické analýzy. Metody aproximace funkcí. Metoda nejmenších čtverců. Aplikace pro hledání modelů z naměřených reálných dat.
	Lagrangeho multiplikatory
	Newtonova metoda
	Linearni regrese
	#!/usr/bin/env python

	from PySide.QtCore import *
	from PySide.QtGui import *
	import cv2
	import sys

	class MainApp(QWidget):

	def __init__(self):
	import subprocess

	def pkgconfig(packages, *kw):
	"""
	Query pkg-config for library compile and linking options. Return configuration in distutils
	Extension format.

	Usage:

	pkgconfig('opencv')
	# modified numpy concatenate, vstack, hstack accepting scalars in the input tuple

	def concatenate(tup, axis=0):
	tup_arrays = [x for x in tup if isinstance(x, np.ndarray)]
	shape = list(tup_arrays[0].shape)
	shape[axis] = 1

	tup_ = []
	for x in tup:
	if isinstance(x, (int, long, float)):