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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
```python
import numpy as np
import matplotlib.pyplot as plt
import random
import math
from sympy import exp, sqrt, pi, Integral, Symbol, S
coin = ['1','0'] # 1 앞면 0 뒷면
coin10 = [0,0,0,0,0,0,0,0,0]
#tcnt = 0 # 앞면이 두번 나온 횟수
for i in range(100000):
cnt = 0 # 10개중에 앞면 갯수
for j in range(8):
cnt += int(rand... | 168afbce0a9bae750221cefca0170c890644f194 | 51,238 | ipynb | Jupyter Notebook | 0522.ipynb | hwan17/it_python | f987291793dcbb5426f7a8b87bd51eda9c56f225 | [
"MIT"
] | null | null | null | 0522.ipynb | hwan17/it_python | f987291793dcbb5426f7a8b87bd51eda9c56f225 | [
"MIT"
] | null | null | null | 0522.ipynb | hwan17/it_python | f987291793dcbb5426f7a8b87bd51eda9c56f225 | [
"MIT"
] | null | null | null | 28.931677 | 1,182 | 0.516257 | true | 13,343 | Qwen/Qwen-72B | 1. YES
2. YES | 0.752013 | 0.73412 | 0.552067 | __label__yue_Hant | 0.174439 | 0.120967 |
# Convolutional Neural Networks
## Introduction
A neural network's hidden layers are used to learn feature detectors from input data. For simple input data types we can use fully connected hidden layers to allow us to learn features across any combination of input feature values. Thus one feature might be learned whic... | 941c25261d3611735ac2122fb6d7496fdb4467d0 | 47,871 | ipynb | Jupyter Notebook | Complete Modules/Deep Learning/week 7/CNNs.ipynb | Maks-Drzezdzon/Working-With-Data-L-O | 86a4b1953d4687cba6cb9b0c2754bc3c801b719b | [
"Apache-2.0"
] | 1 | 2021-11-01T12:18:13.000Z | 2021-11-01T12:18:13.000Z | Complete Modules/Deep Learning/week 7/CNNs.ipynb | Maks-Drzezdzon/Working-With-Data-L-O | 86a4b1953d4687cba6cb9b0c2754bc3c801b719b | [
"Apache-2.0"
] | 22 | 2020-10-01T17:52:52.000Z | 2022-03-05T22:40:39.000Z | Deep Learning/week 7/CNNs.ipynb | Maks-Drzezdzon/Masters-Classes-L-O | 489f6812d80ca57d86adbaca5d25497939ce33f0 | [
"Apache-2.0"
] | null | null | null | 47,871 | 47,871 | 0.777381 | true | 6,561 | Qwen/Qwen-72B | 1. YES
2. YES | 0.930458 | 0.865224 | 0.805055 | __label__eng_Latn | 0.998836 | 0.708745 |
# Function
### Projectile
\begin{equation}
R=\frac{u^2\sin 2\theta}{g}
\end{equation}
\begin{equation}
TF=\frac{2u\sin \theta}{g}
\end{equation}
\begin{equation}
H=\frac{u^2\sin^2\theta}{2g}
\end{equation}
```python
import numpy as np # from numpy import* not required to write np
import panda... | 8da22f580e66d5dc077914384d92d0bcdf6f34c6 | 6,312 | ipynb | Jupyter Notebook | func_pms1.ipynb | AmbaPant/NPS | 0500f39f6708388d5c3f2b8d3e5ee5e56a1f646f | [
"MIT"
] | 1 | 2020-09-16T03:21:55.000Z | 2020-09-16T03:21:55.000Z | func_pms1.ipynb | AmbaPant/NPS | 0500f39f6708388d5c3f2b8d3e5ee5e56a1f646f | [
"MIT"
] | null | null | null | func_pms1.ipynb | AmbaPant/NPS | 0500f39f6708388d5c3f2b8d3e5ee5e56a1f646f | [
"MIT"
] | 2 | 2020-08-10T12:17:21.000Z | 2020-09-13T14:31:02.000Z | 30.200957 | 1,600 | 0.544043 | true | 569 | Qwen/Qwen-72B | 1. YES
2. YES | 0.927363 | 0.863392 | 0.800678 | __label__eng_Latn | 0.417071 | 0.698575 |
```python
import ipywidgets as ipw
import json
import random
import time
import pandas as pd
import os
import webbrowser
import math
from IPython.display import display, Markdown
# set kinetic parameters
with open("rate_parameters.json") as infile:
jsdata = json.load(infile)
params = jsdata["kin1"]
```
Copyright... | e16082ee8fa2bcbd581d28b2cfce56457a1e7261 | 10,258 | ipynb | Jupyter Notebook | CEK_problems/w4_01.ipynb | blake-armstrong/TeachingNotebook | 30cdca5bffd552eaecc0368c3e92744c4d6d368c | [
"MIT"
] | null | null | null | CEK_problems/w4_01.ipynb | blake-armstrong/TeachingNotebook | 30cdca5bffd552eaecc0368c3e92744c4d6d368c | [
"MIT"
] | null | null | null | CEK_problems/w4_01.ipynb | blake-armstrong/TeachingNotebook | 30cdca5bffd552eaecc0368c3e92744c4d6d368c | [
"MIT"
] | 1 | 2022-02-23T11:36:12.000Z | 2022-02-23T11:36:12.000Z | 41.196787 | 628 | 0.591441 | true | 1,974 | Qwen/Qwen-72B | 1. YES
2. YES | 0.705785 | 0.73412 | 0.518131 | __label__eng_Latn | 0.993483 | 0.04212 |
# Examen Módulo 2 - Ecuaciones diferenciales. <font color=blue>Tipo de examen 1</font>.
Lea cuidadosamente las siguientes **indicaciones** antes de comenzar el examen:
- Para resolver el examen edite este mismo archivo y renómbrelo de la siguiente manera: *Examen1_ApellidoNombre*, donde *ApellidoNombre* corresponde a s... | be39cd35aa8db09cf65a4f7f9ff93b7d1877834c | 234,750 | ipynb | Jupyter Notebook | Modulo2/Clase15_RepasoModulo2.ipynb | lilianaavila/SimMat2018-2 | 85ef5d977c536276902c917ac5cd3f1820627fa7 | [
"MIT"
] | 1 | 2022-01-29T04:16:12.000Z | 2022-01-29T04:16:12.000Z | Modulo2/Clase15_RepasoModulo2.ipynb | lilianaavila/SimMat2018-2 | 85ef5d977c536276902c917ac5cd3f1820627fa7 | [
"MIT"
] | 1 | 2020-08-14T17:44:49.000Z | 2020-08-14T17:48:39.000Z | Modulo2/Clase15_RepasoModulo2.ipynb | lilianaavila/SimMat2018-2 | 85ef5d977c536276902c917ac5cd3f1820627fa7 | [
"MIT"
] | 3 | 2019-01-28T20:48:49.000Z | 2022-02-05T20:53:55.000Z | 442.924528 | 77,208 | 0.939757 | true | 3,086 | Qwen/Qwen-72B | 1. YES
2. YES | 0.855851 | 0.718594 | 0.61501 | __label__spa_Latn | 0.894716 | 0.267204 |
<b>Traçar um esboço do gráfico e obter uma equação da parábola que satisfaça as condições dadas.</b>
<b>17. Vértice: $V(0,0)$; Eixo $y=0$; Passa pelo ponto $(4,5)$</b><br><br>
<b>Como a parábola é paralela ao eixo $x$ a equação que a representa é dada por </b>$y^2 = 2px$<br><br>
<b>Substituindo os pontos dados na equ... | 27b084e9cda2042171bf2781ce26fbecd4ce3243 | 16,719 | ipynb | Jupyter Notebook | Problemas Propostos. Pag. 172 - 175/17.ipynb | mateuschaves/GEOMETRIA-ANALITICA | bc47ece7ebab154e2894226c6d939b7e7f332878 | [
"MIT"
] | 1 | 2020-02-03T16:40:45.000Z | 2020-02-03T16:40:45.000Z | Problemas Propostos. Pag. 172 - 175/17.ipynb | mateuschaves/GEOMETRIA-ANALITICA | bc47ece7ebab154e2894226c6d939b7e7f332878 | [
"MIT"
] | null | null | null | Problemas Propostos. Pag. 172 - 175/17.ipynb | mateuschaves/GEOMETRIA-ANALITICA | bc47ece7ebab154e2894226c6d939b7e7f332878 | [
"MIT"
] | null | null | null | 185.766667 | 14,364 | 0.898678 | true | 535 | Qwen/Qwen-72B | 1. YES
2. YES | 0.928409 | 0.893309 | 0.829356 | __label__por_Latn | 0.89051 | 0.765206 |
# Elliptic curve cryptography
What is an Elliptic curve (EC)? An elliptic curve is a plane algebraic curve over a [finite field](https://en.wikipedia.org/wiki/Finite_field) which is defined by an equation of the form:
\begin{equation}
y^2 = x^3+ax+b \quad \textrm{where} \quad 4a^3+27b^2 ≠ 0
\label{eq:ecurve}
\tag{1}
... | 485bb24a4ba64466b8d8c791285643f9a561bf74 | 281,948 | ipynb | Jupyter Notebook | e_curve.ipynb | grenaad/elliptic_curve | 4dd3f0338dca1eeb23df531be4fcfa0c4268151d | [
"MIT"
] | null | null | null | e_curve.ipynb | grenaad/elliptic_curve | 4dd3f0338dca1eeb23df531be4fcfa0c4268151d | [
"MIT"
] | null | null | null | e_curve.ipynb | grenaad/elliptic_curve | 4dd3f0338dca1eeb23df531be4fcfa0c4268151d | [
"MIT"
] | null | null | null | 237.930802 | 60,256 | 0.900318 | true | 9,149 | Qwen/Qwen-72B | 1. YES
2. YES | 0.944995 | 0.874077 | 0.825998 | __label__eng_Latn | 0.929149 | 0.757404 |
# Four level system
We study population dynamics and effects of optical pumping on spectral line singal for near-resonant cyclic $D_{2}$ line transitions in Cs and Na atoms with the help of simplified four-level system. A classical laser field of frequency $ω_{L}$ is in resonance between highest energy ground state h... | 17da0c1251a3001c1e1cfa9320e232bfe34b2bb9 | 656,245 | ipynb | Jupyter Notebook | notebooks/4-level-system/Hyperfine transition dynamics of D2 line using 4-level system Hamiltonian and QuTiP.ipynb | bruvelis/thesis | 515cf710e4e8a898a0e15b33b37e1e054284bcd5 | [
"CC0-1.0"
] | null | null | null | notebooks/4-level-system/Hyperfine transition dynamics of D2 line using 4-level system Hamiltonian and QuTiP.ipynb | bruvelis/thesis | 515cf710e4e8a898a0e15b33b37e1e054284bcd5 | [
"CC0-1.0"
] | null | null | null | notebooks/4-level-system/Hyperfine transition dynamics of D2 line using 4-level system Hamiltonian and QuTiP.ipynb | bruvelis/thesis | 515cf710e4e8a898a0e15b33b37e1e054284bcd5 | [
"CC0-1.0"
] | null | null | null | 52.558465 | 16,348 | 0.50074 | true | 15,513 | Qwen/Qwen-72B | 1. YES
2. YES | 0.849971 | 0.787931 | 0.669719 | __label__eng_Latn | 0.590457 | 0.394312 |
# How to Draw Ellipse of Covariance Matrix
Given a 2x2 covariance matrix, how to draw the ellipse representing it. The following function explains the method to visualize multivariate normal distributions and correlation matrices. Formulae for radii & rotation are provided for covariance matrix shown below
\begin{align... | 67ca447a2021197c2e43b576d78e932ba3c6ce26 | 21,086 | ipynb | Jupyter Notebook | .ipynb_checkpoints/Draw_Covariance_Ellipse-checkpoint.ipynb | venkatramanrenganathan/Demonstrations | 6d25f6b6b208b6c74aecb6c1482ad54d44ad8038 | [
"MIT"
] | null | null | null | .ipynb_checkpoints/Draw_Covariance_Ellipse-checkpoint.ipynb | venkatramanrenganathan/Demonstrations | 6d25f6b6b208b6c74aecb6c1482ad54d44ad8038 | [
"MIT"
] | null | null | null | .ipynb_checkpoints/Draw_Covariance_Ellipse-checkpoint.ipynb | venkatramanrenganathan/Demonstrations | 6d25f6b6b208b6c74aecb6c1482ad54d44ad8038 | [
"MIT"
] | null | null | null | 92.078603 | 9,232 | 0.817035 | true | 1,235 | Qwen/Qwen-72B | 1. YES
2. YES | 0.935347 | 0.913677 | 0.854604 | __label__eng_Latn | 0.803121 | 0.823865 |
```python
surf_choice = 'torus'
```
```python
from sympy import init_printing; init_printing();
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
```
```python
from silkpy.symbolic.surface.surface import ParametricSurface
from sympy import symbols, sin, cos, ... | c76e18bae4d5b9c52142f939e4c5bb37c578fc2d | 24,348 | ipynb | Jupyter Notebook | nb/construct_surface.ipynb | WenyinWei/silkpy | 2e3773e15fbebc4914f5627d8361a24d35fb5935 | [
"MIT"
] | 1 | 2021-05-13T09:23:03.000Z | 2021-05-13T09:23:03.000Z | nb/construct_surface.ipynb | WenyinWei/silkpy | 2e3773e15fbebc4914f5627d8361a24d35fb5935 | [
"MIT"
] | null | null | null | nb/construct_surface.ipynb | WenyinWei/silkpy | 2e3773e15fbebc4914f5627d8361a24d35fb5935 | [
"MIT"
] | 1 | 2021-05-27T14:13:24.000Z | 2021-05-27T14:13:24.000Z | 45.256506 | 3,000 | 0.551996 | true | 2,662 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91848 | 0.812867 | 0.746603 | __label__eng_Latn | 0.299931 | 0.57294 |
# Statistical parameters using probability density function
### Given probability density function, $p(x)$
$ p = 2x/b^2$, $0 < x < b$
### The mean value of $x$ is estimated analytically:
$\overline{x} = \int\limits_0^b x\, p(x)\, dx = \int\limits_0^b 2x^2/b^2 = \left. 2x^3/3b^2\right|_0^b =2b^3/3b^2 = 2b/3$
### the... | 6ac40ff747f3969f9a9d3d2c9e5ec8432b4149b8 | 12,517 | ipynb | Jupyter Notebook | notebooks/unsorted/estimate_mean_variance_median_using_pdf.ipynb | alexlib/engineering_experiments_measurements_course | 0b80d90519a2a72547ffd9ef4da2158530016196 | [
"CC0-1.0"
] | 2 | 2018-05-03T09:41:03.000Z | 2022-03-26T12:39:27.000Z | notebooks/unsorted/estimate_mean_variance_median_using_pdf.ipynb | alexlib/engineering_experiments_measurements_course | 0b80d90519a2a72547ffd9ef4da2158530016196 | [
"CC0-1.0"
] | 1 | 2018-04-22T09:04:13.000Z | 2018-04-22T09:04:13.000Z | notebooks/unsorted/estimate_mean_variance_median_using_pdf.ipynb | alexlib/engineering_experiments_measurements_course | 0b80d90519a2a72547ffd9ef4da2158530016196 | [
"CC0-1.0"
] | 4 | 2015-07-02T11:39:57.000Z | 2021-05-03T15:49:42.000Z | 67.295699 | 7,805 | 0.772949 | true | 652 | Qwen/Qwen-72B | 1. YES
2. YES | 0.941654 | 0.817574 | 0.769872 | __label__eng_Latn | 0.252825 | 0.627004 |
```python
%matplotlib notebook
```
```python
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
from matplotlib.ticker import ScalarFormatter
import math
```
This notebook assumes you have completed the notebook [Introduction of sine waves](TDS_Introduction-sine_waves.ipynb).... | 0e356bf0ad95a9dcddff4e8b8bc8570b3ba9a09b | 639,659 | ipynb | Jupyter Notebook | course/tds-200/week_01/notebooks/TDS_Part_1-chirp_basics.ipynb | potto216/tds-tutorials | 2acf2002ac5514dc60781c3e2e6797a4595104e6 | [
"MIT"
] | 6 | 2020-07-12T19:17:59.000Z | 2020-09-24T22:19:02.000Z | course/tds-200/week_01/notebooks/TDS_Part_1-chirp_basics.ipynb | potto216/tds-tutorials | 2acf2002ac5514dc60781c3e2e6797a4595104e6 | [
"MIT"
] | 7 | 2020-09-16T12:18:01.000Z | 2020-12-17T23:04:37.000Z | course/tds-200/week_01/notebooks/TDS_Part_1-chirp_basics.ipynb | potto216/tds-tutorials | 2acf2002ac5514dc60781c3e2e6797a4595104e6 | [
"MIT"
] | null | null | null | 236.998518 | 296,297 | 0.879209 | true | 3,295 | Qwen/Qwen-72B | 1. YES
2. YES | 0.92523 | 0.868827 | 0.803865 | __label__eng_Latn | 0.887876 | 0.705979 |
#### MIT License (c) 2018 by Andrew Lyasoff
#### Jupyter notebook written in Python 3. It illustrates the use of SymPy to compute the distribution function of the Gaussian law and its inverse, which is then used to transform a uniform Monte Carlo sample into a Gaussian Monte Carlo sample.
First, compute the distribut... | 5ed4c3761cef0d203f4bc7c34606e88fcb2e5ace | 43,785 | ipynb | Jupyter Notebook | Inverse_of_a_Distribution_Function_Example_Python.ipynb | AndrewLyasoff/SMAP | 6eeea8953a26a05b1e23387067109d23b2011824 | [
"MIT"
] | 21 | 2018-09-04T19:12:32.000Z | 2022-03-20T02:05:44.000Z | Inverse_of_a_Distribution_Function_Example_Python.ipynb | lhyzh/SMAP | f6687291769d4c16a0d51a06a941384f646bb432 | [
"MIT"
] | 1 | 2019-02-13T14:24:38.000Z | 2019-02-13T14:24:38.000Z | Inverse_of_a_Distribution_Function_Example_Python.ipynb | lhyzh/SMAP | f6687291769d4c16a0d51a06a941384f646bb432 | [
"MIT"
] | 6 | 2019-02-10T03:43:40.000Z | 2021-03-28T03:53:22.000Z | 87.047714 | 11,248 | 0.854494 | true | 565 | Qwen/Qwen-72B | 1. YES
2. YES | 0.927363 | 0.879147 | 0.815288 | __label__eng_Latn | 0.846532 | 0.732521 |
# In this demo
This demo presents the design and functionality of `Signal`s, which are the core objects for representing coefficients in models. `Signal`s are structured to represent the mathematical formula:
\begin{equation}
s(t) = Re[f(t)e^{i(2 \pi \nu t + \phi)}],
\end{equation}
where
- $f(t)$ is a complex-valu... | 76e2132adf2c3c15e9e65f283d35e91fa5313fb6 | 480,911 | ipynb | Jupyter Notebook | example_notebooks/Signals.ipynb | divshacker/qiskit-ode | 3b5d7afb1a80faea9b489f1d79b09c1e52580107 | [
"Apache-2.0"
] | null | null | null | example_notebooks/Signals.ipynb | divshacker/qiskit-ode | 3b5d7afb1a80faea9b489f1d79b09c1e52580107 | [
"Apache-2.0"
] | null | null | null | example_notebooks/Signals.ipynb | divshacker/qiskit-ode | 3b5d7afb1a80faea9b489f1d79b09c1e52580107 | [
"Apache-2.0"
] | null | null | null | 344.245526 | 40,324 | 0.937047 | true | 4,020 | Qwen/Qwen-72B | 1. YES
2. YES | 0.857768 | 0.782662 | 0.671343 | __label__eng_Latn | 0.970735 | 0.398086 |
<center>
<h1><b>Lab 3</b></h1>
<h1>PHYS 580 - Computational Physics</h1>
<h2>Professor Molnar</h2>
</br>
<h3><b>Ethan Knox</b></h3>
<h4>https://www.github.com/ethank5149</h4>
<h4>ethank5149@gmail.com</h4>
</br>
</br>
<h3><b>September 17, 2020</b></h3>
</center>
### Imports
```python
import numpy as np
import sympy a... | 815a0d3091b871a87090696ac2f4a7189e2be03b | 747,657 | ipynb | Jupyter Notebook | Labs/Lab03/Lab3out.ipynb | ethank5149/PurduePHYS580 | 54d5d75737aa0d31ed723dd0e79c98dc01e71ca7 | [
"MIT"
] | null | null | null | Labs/Lab03/Lab3out.ipynb | ethank5149/PurduePHYS580 | 54d5d75737aa0d31ed723dd0e79c98dc01e71ca7 | [
"MIT"
] | null | null | null | Labs/Lab03/Lab3out.ipynb | ethank5149/PurduePHYS580 | 54d5d75737aa0d31ed723dd0e79c98dc01e71ca7 | [
"MIT"
] | null | null | null | 651.269164 | 222,368 | 0.947459 | true | 6,874 | Qwen/Qwen-72B | 1. YES
2. YES | 0.859664 | 0.863392 | 0.742226 | __label__eng_Latn | 0.106986 | 0.562773 |
# Exercise 1.1: Compute 1+1
```python
a = 1 + 1
print(a)
```
2
# Exercise 1.2: Write a Hello World program
```python
print("Hello, World!")
```
Hello, World!
# Exercise 1.3: Derive and compute a formula
```python
from sympy import Symbol
t = Symbol('t') # Symbol: time [s]
t = t / 60 / 60 / 24 /... | 874b98b064f30bb76965d1ff309ee7638a97eac6 | 19,551 | ipynb | Jupyter Notebook | Exercises 1.ipynb | onnoeberhard/scipro-primer-notebooks | e9cf1069d62f8c50549d9912403c640f08a99392 | [
"MIT"
] | 16 | 2019-04-18T13:35:42.000Z | 2021-09-05T21:01:06.000Z | Exercises 1.ipynb | onnoeberhard/scipro-primer-notebooks | e9cf1069d62f8c50549d9912403c640f08a99392 | [
"MIT"
] | 1 | 2021-02-15T16:26:00.000Z | 2021-02-15T16:26:00.000Z | Exercises 1.ipynb | onnoeberhard/scipro-primer-notebooks | e9cf1069d62f8c50549d9912403c640f08a99392 | [
"MIT"
] | 17 | 2018-09-30T07:35:00.000Z | 2021-08-28T19:15:09.000Z | 23.871795 | 138 | 0.44944 | true | 4,090 | Qwen/Qwen-72B | 1. YES
2. YES | 0.90599 | 0.76908 | 0.696779 | __label__eng_Latn | 0.881366 | 0.457182 |
# Listen
In der Praxis sind Berechnungen häufig nicht nur für einen einzelnen Wert durchzuführen, sondern für mehrere gleichartige Werte. Als Beispiel kann eine Wohnung dienen, bei der der Abluftvolumenstrom für jeden einzelnen Abluftraum berechnet werden muss und verschiedene weitere Berechnungen davon ebenfalls betr... | 445aa0cbacd3503aa924236868f914cd50f1d1de | 14,827 | ipynb | Jupyter Notebook | src/04-Listen_lsg.ipynb | w-meiners/anb-first-steps | 6cb3583f77ae853922acd86fa9e48e9cf5188596 | [
"MIT"
] | null | null | null | src/04-Listen_lsg.ipynb | w-meiners/anb-first-steps | 6cb3583f77ae853922acd86fa9e48e9cf5188596 | [
"MIT"
] | null | null | null | src/04-Listen_lsg.ipynb | w-meiners/anb-first-steps | 6cb3583f77ae853922acd86fa9e48e9cf5188596 | [
"MIT"
] | null | null | null | 26.242478 | 327 | 0.527079 | true | 3,256 | Qwen/Qwen-72B | 1. YES
2. YES | 0.865224 | 0.731059 | 0.632529 | __label__deu_Latn | 0.925284 | 0.307909 |
<a href="https://colab.research.google.com/github/alanexplorer/Robotic-Algorithm-Tutorial/blob/master/kalmanFIlter.ipynb" target="_parent"></a>
# Kalman Filter
## Introduction
Kalman filtering is an algorithm that provides estimates of some unknown variables given the measurements observed over time. Kalman filters ... | 5367fbda8928fe27c4f071a87e0ceb094b14c7fe | 398,851 | ipynb | Jupyter Notebook | kalmanFIlter.ipynb | alanexplorer/contatosalan-outlook.com | e315c59054ae7d68200263a581f38c02b754491e | [
"MIT"
] | 1 | 2020-04-13T16:58:41.000Z | 2020-04-13T16:58:41.000Z | kalmanFIlter.ipynb | alanexplorer/Robotic-Algorithm-Tutorial | e315c59054ae7d68200263a581f38c02b754491e | [
"MIT"
] | null | null | null | kalmanFIlter.ipynb | alanexplorer/Robotic-Algorithm-Tutorial | e315c59054ae7d68200263a581f38c02b754491e | [
"MIT"
] | null | null | null | 257.822237 | 52,500 | 0.916886 | true | 6,906 | Qwen/Qwen-72B | 1. YES
2. YES | 0.944177 | 0.853913 | 0.806245 | __label__eng_Latn | 0.896733 | 0.711509 |
<a href="https://colab.research.google.com/github/probml/probml-notebooks/blob/main/notebooks/advi_beta_binom_jax.ipynb" target="_parent"></a>
# ADVI from scratch in JAX
Authors: karm-patel@, murphyk@
In this notebook we apply ADVI (automatic differentiation variational inference) to the beta-binomial model, using ... | 28c14e34e8a0dced43dd7f361c5114516298f50e | 233,086 | ipynb | Jupyter Notebook | notebooks/advi_beta_binom_jax.ipynb | patel-zeel/probml-notebooks | 1ff09bfddb2bd6b3932d81845546770e7e2fce3a | [
"MIT"
] | null | null | null | notebooks/advi_beta_binom_jax.ipynb | patel-zeel/probml-notebooks | 1ff09bfddb2bd6b3932d81845546770e7e2fce3a | [
"MIT"
] | 1 | 2022-03-30T20:00:48.000Z | 2022-03-30T20:30:42.000Z | notebooks/advi_beta_binom_jax.ipynb | patel-zeel/probml-notebooks | 1ff09bfddb2bd6b3932d81845546770e7e2fce3a | [
"MIT"
] | null | null | null | 189.655004 | 27,348 | 0.899054 | true | 5,513 | Qwen/Qwen-72B | 1. YES
2. YES | 0.819893 | 0.839734 | 0.688492 | __label__eng_Latn | 0.383548 | 0.437929 |
```python
import import_ipynb
from Data_Analysis_1 import *
```
importing Jupyter notebook from Data_Analysis_1.ipynb
# $e^x$
```python
from sympy import *
import numpy as np
x = Symbol('x')
y = exp(x)
# yprime = y.diff(x)
def factorial(i):
if i == 1 or i == 0:
return 1
else:
retur... | 2fc194824a29821b7f5249c5e5286477b5814eb5 | 487,815 | ipynb | Jupyter Notebook | Taylor Polynomial.ipynb | RiccardoTancredi/Polynomials | 6ddeb927284092cbb52308065d1119a2f7f7e277 | [
"MIT"
] | null | null | null | Taylor Polynomial.ipynb | RiccardoTancredi/Polynomials | 6ddeb927284092cbb52308065d1119a2f7f7e277 | [
"MIT"
] | null | null | null | Taylor Polynomial.ipynb | RiccardoTancredi/Polynomials | 6ddeb927284092cbb52308065d1119a2f7f7e277 | [
"MIT"
] | null | null | null | 37.025806 | 80,165 | 0.549501 | true | 1,717 | Qwen/Qwen-72B | 1. YES
2. YES | 0.805632 | 0.740174 | 0.596308 | __label__eng_Latn | 0.10713 | 0.223754 |
# Deep Learning
**CS5483 Data Warehousing and Data Mining**
___
```python
%reset -f
%load_ext tensorboard
%matplotlib inline
import jupyter_manim
from manimlib.imports import *
import pprint as pp
import tensorflow_datasets as tfds
import tensorflow.compat.v2 as tf
import tensorflow_addons as tfa
import os, datetime... | 7b012154beaec72210540634fa5f5e8840c346ee | 92,504 | ipynb | Jupyter Notebook | Tutorial7/Deep Learning.ipynb | ccha23/cs5483 | e8fa9d9b8a0545696958ca87c2c9a8a133109191 | [
"MIT"
] | null | null | null | Tutorial7/Deep Learning.ipynb | ccha23/cs5483 | e8fa9d9b8a0545696958ca87c2c9a8a133109191 | [
"MIT"
] | 1 | 2021-04-19T09:21:04.000Z | 2021-04-19T09:21:06.000Z | Tutorial7/Deep Learning.ipynb | ccha23/cs5483 | e8fa9d9b8a0545696958ca87c2c9a8a133109191 | [
"MIT"
] | 1 | 2022-03-20T05:25:45.000Z | 2022-03-20T05:25:45.000Z | 32.063778 | 539 | 0.559965 | true | 12,722 | Qwen/Qwen-72B | 1. YES
2. YES | 0.782662 | 0.793106 | 0.620734 | __label__eng_Latn | 0.932696 | 0.280504 |
```python
import pyprob
from pyprob import Model
from pyprob import InferenceEngine
from pyprob.distributions import Normal
from pyprob.dis import ModelDIS
import torch
import numpy as np
import math
import matplotlib.pyplot as plt
%matplotlib inline
fig = plt.figure();
```
<Figure size 432x288 with 0 Axes>
# ... | a01ec8da5cda1a41c2d1da384038e1550eb82218 | 84,989 | ipynb | Jupyter Notebook | examples/Gaussian_DIS.ipynb | SRagy/pyprob | c8fc67d5f4ad78d565a86c2f86abe10b73549f18 | [
"BSD-2-Clause"
] | null | null | null | examples/Gaussian_DIS.ipynb | SRagy/pyprob | c8fc67d5f4ad78d565a86c2f86abe10b73549f18 | [
"BSD-2-Clause"
] | null | null | null | examples/Gaussian_DIS.ipynb | SRagy/pyprob | c8fc67d5f4ad78d565a86c2f86abe10b73549f18 | [
"BSD-2-Clause"
] | null | null | null | 118.53417 | 20,294 | 0.82626 | true | 6,705 | Qwen/Qwen-72B | 1. YES
2. YES | 0.863392 | 0.812867 | 0.701823 | __label__eng_Latn | 0.421924 | 0.468901 |
```python
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys
sys.path.append('../../pyutils')
import metrics
import utils
```
# When $p$ is much bigger than $N$
High variance and overfitting are a major concern in this setting.
Simple, highly regularized models are often used.
Let... | 58d342ac59acb80652e991040435f3f03b357f97 | 14,364 | ipynb | Jupyter Notebook | refs/elements-of-statistical-learning/eosl18_high_dim_problems.ipynb | obs145628/ml-notebooks | 08a64962e106ec569039ab204a7ae4c900783b6b | [
"MIT"
] | 1 | 2020-10-29T11:26:00.000Z | 2020-10-29T11:26:00.000Z | refs/elements-of-statistical-learning/eosl18_high_dim_problems.ipynb | obs145628/ml-notebooks | 08a64962e106ec569039ab204a7ae4c900783b6b | [
"MIT"
] | 5 | 2021-03-18T21:33:45.000Z | 2022-03-11T23:34:50.000Z | refs/elements-of-statistical-learning/eosl18_high_dim_problems.ipynb | obs145628/ml-notebooks | 08a64962e106ec569039ab204a7ae4c900783b6b | [
"MIT"
] | 1 | 2019-12-23T21:50:02.000Z | 2019-12-23T21:50:02.000Z | 46.335484 | 202 | 0.595377 | true | 3,299 | Qwen/Qwen-72B | 1. YES
2. YES | 0.894789 | 0.851953 | 0.762318 | __label__eng_Latn | 0.989191 | 0.609453 |
# Exercise 6.2 in JT: More on pooling in credit markets.
Import packages, classes and settings:
```python
import numpy as np
import math
import itertools
from scipy import optimize
import scipy.stats as stats
import PS1 as func
# For plots:
import matplotlib.pyplot as plt
import matplotlib as mpl
%matplotlib inline
... | 9145162b9180e64b567beabaf2c1f280876aa293 | 115,347 | ipynb | Jupyter Notebook | PS1/PS1_3.ipynb | ChampionApe/FinancialFriction | eb5be29c7951871972b55fd863c89b83bb50d295 | [
"MIT"
] | null | null | null | PS1/PS1_3.ipynb | ChampionApe/FinancialFriction | eb5be29c7951871972b55fd863c89b83bb50d295 | [
"MIT"
] | null | null | null | PS1/PS1_3.ipynb | ChampionApe/FinancialFriction | eb5be29c7951871972b55fd863c89b83bb50d295 | [
"MIT"
] | null | null | null | 353.825153 | 29,300 | 0.938438 | true | 1,103 | Qwen/Qwen-72B | 1. YES
2. YES | 0.894789 | 0.782662 | 0.700318 | __label__eng_Latn | 0.989322 | 0.465405 |
# Programación Entera: Heurísticas para el Problema de Localización de Servicios
## Descripción
En este trabajo se estudia el problema de localización de servicios de manera detallada, en concreto localización con costos fijos, analizando el problema de programación lineal además de la heurística *ADD*. Todo ello se ... | b8e494ab982839ad2a369bdaced5a78475fe73ff | 115,042 | ipynb | Jupyter Notebook | integer-programming-service-location-heuristics.ipynb | garciparedes/linear-programming-heuristics | 2b4aa26f4c68f800f93cc7530daf3272d7f67b14 | [
"Apache-2.0"
] | 1 | 2019-06-24T02:14:25.000Z | 2019-06-24T02:14:25.000Z | integer-programming-service-location-heuristics.ipynb | garciparedes/linear-programming-heuristics | 2b4aa26f4c68f800f93cc7530daf3272d7f67b14 | [
"Apache-2.0"
] | null | null | null | integer-programming-service-location-heuristics.ipynb | garciparedes/linear-programming-heuristics | 2b4aa26f4c68f800f93cc7530daf3272d7f67b14 | [
"Apache-2.0"
] | null | null | null | 85.724292 | 1,838 | 0.3939 | true | 21,644 | Qwen/Qwen-72B | 1. YES
2. YES | 0.752013 | 0.760651 | 0.572019 | __label__kor_Hang | 0.222616 | 0.167321 |
```python
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy.special import factorial
from collections import Counter
```
### G-M tube high coltage
```python
#the amplitude, counting rate and background noise
A = np.array([0.440,0.586,0.624,0.728,0.808,0.952,1.002])
R = np.array([0.02,1... | bcd282bb160c16d88460954379c610bfa056a29b | 102,658 | ipynb | Jupyter Notebook | Appendix.LAB_1.ipynb | LorenzoZhu/Phys133 | df818055e279ccbdd92851ad1cb4d6851735912d | [
"Apache-2.0"
] | 1 | 2021-07-24T04:01:08.000Z | 2021-07-24T04:01:08.000Z | Appendix.LAB_1.ipynb | PengpeiZhu/Phys133 | df818055e279ccbdd92851ad1cb4d6851735912d | [
"Apache-2.0"
] | null | null | null | Appendix.LAB_1.ipynb | PengpeiZhu/Phys133 | df818055e279ccbdd92851ad1cb4d6851735912d | [
"Apache-2.0"
] | null | null | null | 235.995402 | 22,200 | 0.921263 | true | 1,879 | Qwen/Qwen-72B | 1. YES
2. YES | 0.763484 | 0.833325 | 0.63623 | __label__eng_Latn | 0.370578 | 0.316506 |
**NOTE:** *The slope-deflection sign convention may seem strange to those used to matirx stiffness analysis, but it makes sense. None of the slope deflection equations explicitly state a
member 'direction' and it doesn't matter. For example, whether you consider the column AB as
going from A to B or as going from B ... | b526c70b426f9255bd45731097ea3d6f4afba8f4 | 93,147 | ipynb | Jupyter Notebook | slope-deflection/KG-Example-8.2.ipynb | nholtz/structural-analysis | 246d6358355bd9768e30075d1f6af282ceb995be | [
"CC0-1.0"
] | 3 | 2016-05-26T07:01:51.000Z | 2019-05-31T23:48:11.000Z | slope-deflection/KG-Example-8.2.ipynb | nholtz/structural-analysis | 246d6358355bd9768e30075d1f6af282ceb995be | [
"CC0-1.0"
] | null | null | null | slope-deflection/KG-Example-8.2.ipynb | nholtz/structural-analysis | 246d6358355bd9768e30075d1f6af282ceb995be | [
"CC0-1.0"
] | 1 | 2016-08-30T06:08:03.000Z | 2016-08-30T06:08:03.000Z | 60.485065 | 9,360 | 0.657047 | true | 5,621 | Qwen/Qwen-72B | 1. YES
2. YES | 0.921922 | 0.831143 | 0.766249 | __label__eng_Latn | 0.520126 | 0.618585 |
# A model for stock return calculation
The purpose of this model is to help us estimate, what size a stock position need to have, to be considered lucrative given the boundary conditions (investment costs & market conditions).
### Annotations:
$R$: Return<br>
$I$: Income<br>
$E$: Expenditure<br>
$_b$ or $_s$: buy or ... | 0850175a6411706197eafa0095c18ae23a163df3 | 749,489 | ipynb | Jupyter Notebook | Stock return model.ipynb | jkotula89/StockAnalysis | b8e56feb34693a042d599a16aa9d5bcfc9cc4f4a | [
"MIT"
] | null | null | null | Stock return model.ipynb | jkotula89/StockAnalysis | b8e56feb34693a042d599a16aa9d5bcfc9cc4f4a | [
"MIT"
] | null | null | null | Stock return model.ipynb | jkotula89/StockAnalysis | b8e56feb34693a042d599a16aa9d5bcfc9cc4f4a | [
"MIT"
] | null | null | null | 1,411.467043 | 209,440 | 0.957164 | true | 2,837 | Qwen/Qwen-72B | 1. YES
2. YES | 0.901921 | 0.795658 | 0.71762 | __label__eng_Latn | 0.380702 | 0.505604 |
# Finding Roots of Equations
## Calculus review
```python
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy as scipy
from scipy.interpolate import interp1d
```
Let's review the theory of optimization for multivariate functions. Recall that in the single-variable case, extreme values... | be4751a3214fdd7a26e84ac9cffe4c7442421ae2 | 340,669 | ipynb | Jupyter Notebook | notebooks/S09A_Root_Finding.ipynb | ZhechangYang/STA663 | 0dcf48e3e7a2d1f698b15e84946e44344b8153f5 | [
"BSD-3-Clause"
] | null | null | null | notebooks/S09A_Root_Finding.ipynb | ZhechangYang/STA663 | 0dcf48e3e7a2d1f698b15e84946e44344b8153f5 | [
"BSD-3-Clause"
] | null | null | null | notebooks/S09A_Root_Finding.ipynb | ZhechangYang/STA663 | 0dcf48e3e7a2d1f698b15e84946e44344b8153f5 | [
"BSD-3-Clause"
] | null | null | null | 249.574359 | 79,964 | 0.905483 | true | 6,897 | Qwen/Qwen-72B | 1. YES
2. YES | 0.919643 | 0.932453 | 0.857524 | __label__eng_Latn | 0.949041 | 0.830648 |
# Energy storage convex loss model
Objective: explore the *possibilities* to have convex loss models (i.e. $P_{losses}(...)$ below):
$$E_b(k+1) = E_b(k) + (P_b(k) - P_{losses}(P_b, E_b)) \Delta_t$$
Reminder: to preserve convexity, we need to replace the equality constraint with an **inequality**:
$$ E_b(k+1) ≤ ...$... | 9a1f3a8b2e4706aa9da3738a85d42b0a4ae5d63e | 912,709 | ipynb | Jupyter Notebook | ESS Convex loss model.ipynb | pierre-haessig/convex-storage-loss | 821929c887e9a12b70e7ae9becb2830f1764e5d6 | [
"CC-BY-4.0"
] | null | null | null | ESS Convex loss model.ipynb | pierre-haessig/convex-storage-loss | 821929c887e9a12b70e7ae9becb2830f1764e5d6 | [
"CC-BY-4.0"
] | null | null | null | ESS Convex loss model.ipynb | pierre-haessig/convex-storage-loss | 821929c887e9a12b70e7ae9becb2830f1764e5d6 | [
"CC-BY-4.0"
] | null | null | null | 875.920345 | 120,612 | 0.951919 | true | 2,115 | Qwen/Qwen-72B | 1. YES
2. YES | 0.824462 | 0.841826 | 0.694053 | __label__eng_Latn | 0.592856 | 0.450849 |
# Modelo de Reverchon
El modelo de Reverchon resulta de realizar los balances de masa sobre el lecho de extracción utilizando la supocición de flujo pisto en el interior del lecho, despreciando la disperción axial y en las que se considera que tanto el flujo del fluido, la presión, temperatura se mantienen constantes... | d72775adbc0807bfab2b274b7d6014b3074bd4b5 | 3,435 | ipynb | Jupyter Notebook | docs/source/reverchon_doc.ipynb | pysg/sepya | b9973026c625df1c913f5ed85637acd6f7e69408 | [
"MIT"
] | 1 | 2021-02-27T01:05:12.000Z | 2021-02-27T01:05:12.000Z | docs/source/reverchon_doc.ipynb | pysg/sepya | b9973026c625df1c913f5ed85637acd6f7e69408 | [
"MIT"
] | null | null | null | docs/source/reverchon_doc.ipynb | pysg/sepya | b9973026c625df1c913f5ed85637acd6f7e69408 | [
"MIT"
] | 1 | 2021-02-27T01:05:13.000Z | 2021-02-27T01:05:13.000Z | 26.835938 | 306 | 0.547307 | true | 629 | Qwen/Qwen-72B | 1. YES
2. YES | 0.779993 | 0.715424 | 0.558026 | __label__spa_Latn | 0.88461 | 0.13481 |
```python
# Models from Introduction to Algorithmic Marketing
# https://algorithmicweb.wordpress.com/
#
# Markov chain-based LTV model predicts customer lifetime value
# using the probabilities of transition between different customer states
```
```python
%matplotlib inline
import sympy as sy
import numpy as np
impor... | 7cee1e79048135c00a252d52c0bc2a06757134f2 | 21,997 | ipynb | Jupyter Notebook | promotions/markov-ltv.ipynb | sayandesarkar/algorithmic-marketing-examples | 6feb857bfcae8a3b792747577ad05914b081a25c | [
"Apache-2.0"
] | 1 | 2019-06-05T09:40:57.000Z | 2019-06-05T09:40:57.000Z | promotions/markov-ltv.ipynb | axlander83/algorithmic-examples | 8a2478b99017502f2b1b82e326bc41fbcfcffe02 | [
"Apache-2.0"
] | null | null | null | promotions/markov-ltv.ipynb | axlander83/algorithmic-examples | 8a2478b99017502f2b1b82e326bc41fbcfcffe02 | [
"Apache-2.0"
] | 1 | 2022-02-07T05:56:32.000Z | 2022-02-07T05:56:32.000Z | 167.916031 | 18,336 | 0.886212 | true | 557 | Qwen/Qwen-72B | 1. YES
2. YES | 0.891811 | 0.800692 | 0.714066 | __label__eng_Latn | 0.992644 | 0.497346 |
```python
%pylab inline
```
Populating the interactive namespace from numpy and matplotlib
```python
N = 11
h = 1/(N-1)
x = linspace(0,1,N)
```
```python
f = ones((N,))
```
```python
A = zeros((N,N))
for i in range(1,N-1):
A[i, i-1] = A[i, i+1] = -1
A[i,i] = 2
A[0,0] = A[-1,-1] = 1
f[0] = f[-1] = 0
... | ede7863c3fc96c2bd58283fa8baca8b69234793d | 55,766 | ipynb | Jupyter Notebook | slides/Lecture 10 - LH - LAB - Introduction to PDEs - Finite Differences in 1D.ipynb | vitturso/numerical-analysis-2021-2022 | d675a6f766a42d0a46e7cd69dbfed8645a0b2590 | [
"CC-BY-4.0"
] | null | null | null | slides/Lecture 10 - LH - LAB - Introduction to PDEs - Finite Differences in 1D.ipynb | vitturso/numerical-analysis-2021-2022 | d675a6f766a42d0a46e7cd69dbfed8645a0b2590 | [
"CC-BY-4.0"
] | null | null | null | slides/Lecture 10 - LH - LAB - Introduction to PDEs - Finite Differences in 1D.ipynb | vitturso/numerical-analysis-2021-2022 | d675a6f766a42d0a46e7cd69dbfed8645a0b2590 | [
"CC-BY-4.0"
] | null | null | null | 125.035874 | 16,264 | 0.861815 | true | 1,697 | Qwen/Qwen-72B | 1. YES
2. YES | 0.927363 | 0.877477 | 0.81374 | __label__yue_Hant | 0.128546 | 0.728923 |
# 5. Gyakorlat - 1 DoF csillapított lengő kar
2021.03.08
## Feladat:
```python
from IPython.display import Image
Image(filename='gyak_5_1.png',width=500)
```
A mellékelt ábrán egy lengőkar látható, ami két különböző tömegű és hosszúságú rúdból és a hozzá csatlakozó $R$ sugarú korongból áll. A két rúd két $k_1$, ill... | 9ce05c987664a0d899a7560457253823ba976f6a | 310,993 | ipynb | Jupyter Notebook | otodik_het/.ipynb_checkpoints/gyak_5-checkpoint.ipynb | barnabaspiri/RezgestanPython | 3fcc4374c90d041436c816d26ded63af95b44103 | [
"MIT"
] | null | null | null | otodik_het/.ipynb_checkpoints/gyak_5-checkpoint.ipynb | barnabaspiri/RezgestanPython | 3fcc4374c90d041436c816d26ded63af95b44103 | [
"MIT"
] | null | null | null | otodik_het/.ipynb_checkpoints/gyak_5-checkpoint.ipynb | barnabaspiri/RezgestanPython | 3fcc4374c90d041436c816d26ded63af95b44103 | [
"MIT"
] | null | null | null | 457.342647 | 127,912 | 0.937092 | true | 3,967 | Qwen/Qwen-72B | 1. YES
2. YES | 0.826712 | 0.749087 | 0.619279 | __label__hun_Latn | 0.999918 | 0.277124 |
```python
%pylab inline
import numpy as np
import pandas as pd
import sympy as sp
```
Populating the interactive namespace from numpy and matplotlib
```python
font = {'size' : 14}
matplotlib.rc('font', **font)
sp.init_printing()
```
# Approximate integration methods
Recall that the definite integral is defi... | f0f781cff1fa356c39a06b304720557e8a48469b | 45,724 | ipynb | Jupyter Notebook | Notebooks/Approximate integration.ipynb | darkeclipz/jupyter-notebooks | 5de784244ad9db12cfacbbec3053b11f10456d7e | [
"Unlicense"
] | 1 | 2018-08-28T12:16:12.000Z | 2018-08-28T12:16:12.000Z | Notebooks/Approximate integration.ipynb | darkeclipz/jupyter-notebooks | 5de784244ad9db12cfacbbec3053b11f10456d7e | [
"Unlicense"
] | null | null | null | Notebooks/Approximate integration.ipynb | darkeclipz/jupyter-notebooks | 5de784244ad9db12cfacbbec3053b11f10456d7e | [
"Unlicense"
] | null | null | null | 80.358524 | 28,402 | 0.779263 | true | 3,382 | Qwen/Qwen-72B | 1. YES
2. YES | 0.908618 | 0.888759 | 0.807542 | __label__eng_Latn | 0.838423 | 0.714524 |
# Harmonic oscillator
```python
import sympy
sympy.init_printing()
from IPython.display import display
import numpy
import matplotlib.pyplot as plt
import sys
sys.path.insert(0, './code')
from gauss_legendre import gauss_legendre
from evaluate_functional import evaluate_functional
```
```python
# state vector (sy... | 2132301ae00f2ba9adf6aefcb0bf909c194b8f68 | 272,054 | ipynb | Jupyter Notebook | harmonic_oscillator.ipynb | MarkusLohmayer/master-thesis-code | b107d1b582064daf9ad4414e1c9f332ef0be8660 | [
"MIT"
] | 1 | 2020-11-14T15:56:07.000Z | 2020-11-14T15:56:07.000Z | harmonic_oscillator.ipynb | MarkusLohmayer/master-thesis-code | b107d1b582064daf9ad4414e1c9f332ef0be8660 | [
"MIT"
] | null | null | null | harmonic_oscillator.ipynb | MarkusLohmayer/master-thesis-code | b107d1b582064daf9ad4414e1c9f332ef0be8660 | [
"MIT"
] | null | null | null | 1,519.854749 | 266,584 | 0.960817 | true | 563 | Qwen/Qwen-72B | 1. YES
2. YES | 0.931463 | 0.831143 | 0.774179 | __label__eng_Latn | 0.668488 | 0.637009 |
# Combination - Passwords & Students
> This document is written in *R*.
>
> ***GitHub***: https://github.com/czs108
## Question A
> If passwords can consist of **6** *letters*, find the probability that a randomly chosen password will *not* have any *repeated* letters.
\begin{equation}
P = \frac{26 \times 25 \times... | 351dc578b236a2d8657c6e3e7373b9c704ee3c85 | 6,639 | ipynb | Jupyter Notebook | exercises/Combination - Passwords & Students.ipynb | czs108/Probability-Theory-Exercises | 60c6546db1e7f075b311d1e59b0afc3a13d93229 | [
"MIT"
] | null | null | null | exercises/Combination - Passwords & Students.ipynb | czs108/Probability-Theory-Exercises | 60c6546db1e7f075b311d1e59b0afc3a13d93229 | [
"MIT"
] | null | null | null | exercises/Combination - Passwords & Students.ipynb | czs108/Probability-Theory-Exercises | 60c6546db1e7f075b311d1e59b0afc3a13d93229 | [
"MIT"
] | 1 | 2022-03-21T05:04:07.000Z | 2022-03-21T05:04:07.000Z | 21.143312 | 220 | 0.463775 | true | 764 | Qwen/Qwen-72B | 1. YES
2. YES | 0.944177 | 0.91118 | 0.860315 | __label__eng_Latn | 0.894813 | 0.837133 |
# Método dos Mínimos Quadrados (MMQ)
## License
All content can be freely used and adapted under the terms of the
[Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).
## Imports
Coloque **todos** os `import` na célula abaixo. Não se esqueça do `%matplotlib inline... | 27808b3f638c1333ad3e1140884d9bd5369f0acb | 18,806 | ipynb | Jupyter Notebook | minimos-quadrados.ipynb | mat-esp-uerj/minimos-quadrados-leovsf | 39f283e91ef192658507b2e825b942a4f27ef990 | [
"CC-BY-4.0"
] | null | null | null | minimos-quadrados.ipynb | mat-esp-uerj/minimos-quadrados-leovsf | 39f283e91ef192658507b2e825b942a4f27ef990 | [
"CC-BY-4.0"
] | 1 | 2016-01-14T01:13:53.000Z | 2016-01-14T13:08:06.000Z | minimos-quadrados.ipynb | mat-esp-uerj/minimos-quadrados-leovsf | 39f283e91ef192658507b2e825b942a4f27ef990 | [
"CC-BY-4.0"
] | 1 | 2021-05-22T02:37:26.000Z | 2021-05-22T02:37:26.000Z | 24.423377 | 294 | 0.557748 | true | 2,705 | Qwen/Qwen-72B | 1. YES
2. YES | 0.699254 | 0.912436 | 0.638025 | __label__por_Latn | 0.999403 | 0.320677 |
```python
#import krotov
#import qutip as qt# NBVAL_IGNORE_OUTPUT
#import qutip
#import numpy as np
#import scipy
#import matplotlib
#import matplotlib.pylab as plt
#import krotov
import numpy as np
import sympy as sp
from sympy import Function,Symbol,symbols,zeros,Matrix,sqrt,simplify,solve,diff,dsolve,lambdify
from s... | b1b64a376141ce2434d9be9f8cecb8c96b8e1ae6 | 82,692 | ipynb | Jupyter Notebook | Squeezed+dissipation/save(squeezed).ipynb | mcditoos/krotov | 6a70cc791fa21186997ad2ca5a72f6d30574e7a0 | [
"BSD-3-Clause"
] | null | null | null | Squeezed+dissipation/save(squeezed).ipynb | mcditoos/krotov | 6a70cc791fa21186997ad2ca5a72f6d30574e7a0 | [
"BSD-3-Clause"
] | null | null | null | Squeezed+dissipation/save(squeezed).ipynb | mcditoos/krotov | 6a70cc791fa21186997ad2ca5a72f6d30574e7a0 | [
"BSD-3-Clause"
] | 1 | 2021-11-26T17:01:29.000Z | 2021-11-26T17:01:29.000Z | 43.3851 | 5,006 | 0.409375 | true | 16,648 | Qwen/Qwen-72B | 1. YES
2. YES | 0.831143 | 0.685949 | 0.570122 | __label__kor_Hang | 0.195831 | 0.162915 |
# Objective: To filter a textured image without loosing its texture
```python
# Import the required libs
from torch_pdegraph.pdes import pdeanisodiff
from torch_pdegraph.utilities import *
import numpy as np
from matplotlib import image as mpimg
import faiss
import matplotlib.pyplot as plt
import torch
```
```pytho... | 73998876af153cbbd8d9ba4ae459a0c157e994d3 | 994,519 | ipynb | Jupyter Notebook | applications/2_texture_denoising.ipynb | aGIToz/Pytorch_pdegraph | fade6817e437b606c43221a5ca13bdaeec563fff | [
"MIT"
] | 17 | 2020-08-24T09:04:48.000Z | 2022-03-19T03:46:07.000Z | applications/2_texture_denoising.ipynb | aGIToz/Pytorch_pdegraph | fade6817e437b606c43221a5ca13bdaeec563fff | [
"MIT"
] | null | null | null | applications/2_texture_denoising.ipynb | aGIToz/Pytorch_pdegraph | fade6817e437b606c43221a5ca13bdaeec563fff | [
"MIT"
] | 3 | 2020-08-27T15:53:02.000Z | 2021-09-13T08:36:27.000Z | 2,762.552778 | 655,668 | 0.964978 | true | 1,781 | Qwen/Qwen-72B | 1. YES
2. YES | 0.939025 | 0.847968 | 0.796263 | __label__eng_Latn | 0.732104 | 0.688318 |
# Exercises
<!-- --- begin exercise --- -->
## Problem 11: Define nodes and elements
<div id="fem:approx:fe:exer:mesh1"></div>
Consider a domain $\Omega =[0,2]$ divided into the three elements
$[0,1]$, $[1,1.2]$, and $[1.2,2]$.
For P1 and P2 elements, set up the list of coordinates and nodes
(`nodes`) and the num... | c9daed30acb8d44daf4dd2d40625f03bb705b349 | 67,188 | ipynb | Jupyter Notebook | 3- approx-fe-exercises.ipynb | mbarzegary/finite-element-intro | 47ef0a3592b823ae71a874ee35850114f16b6d8b | [
"MIT"
] | 8 | 2021-01-26T13:18:02.000Z | 2022-02-14T15:20:11.000Z | 3- approx-fe-exercises.ipynb | mbarzegary/finite-element-intro | 47ef0a3592b823ae71a874ee35850114f16b6d8b | [
"MIT"
] | null | null | null | 3- approx-fe-exercises.ipynb | mbarzegary/finite-element-intro | 47ef0a3592b823ae71a874ee35850114f16b6d8b | [
"MIT"
] | 2 | 2021-08-05T23:14:15.000Z | 2021-10-05T10:22:29.000Z | 32.663102 | 205 | 0.477198 | true | 15,077 | Qwen/Qwen-72B | 1. YES
2. YES | 0.689306 | 0.7773 | 0.535797 | __label__eng_Latn | 0.847334 | 0.083166 |
```python
from sympy import *
init_printing()
```
```python
Uo,x,s=symbols('Uo x s', real=True)
```
```python
U=Uo*((s/x)**12-(s/x)**6)
U
```
```python
Up=U.diff(x)
Up
```
```python
roots = solve(Up,x)
roots
```
```python
x0=roots[1]
print(x0)
x0
```
```python
Um=U.subs(x,x0)
print(Um)
Um
```
```python
Up... | ce4f961f15ab98915c4e86ee036bbe3bb2394053 | 21,061 | ipynb | Jupyter Notebook | P03-TaylorSeriesWarmUpA.ipynb | parduhne/PHYS_280 | 9890804b87fa23c3d53e826fde4a3b82430c875c | [
"MIT"
] | null | null | null | P03-TaylorSeriesWarmUpA.ipynb | parduhne/PHYS_280 | 9890804b87fa23c3d53e826fde4a3b82430c875c | [
"MIT"
] | 2 | 2021-02-02T23:14:34.000Z | 2021-03-20T05:46:23.000Z | P03-TaylorSeriesWarmUpA.ipynb | TejasAvinashShetty/sci-comp-notebooks | 8ac7887a6c5da6fff8173febbc9a9968a81836af | [
"MIT"
] | null | null | null | 61.402332 | 5,188 | 0.761692 | true | 191 | Qwen/Qwen-72B | 1. YES
2. YES | 0.877477 | 0.835484 | 0.733117 | __label__yue_Hant | 0.449598 | 0.541609 |
# Jupyter like a pro
In this third notebook of the tutorial ["The World of Jupyter"](https://github.com/barbagroup/jupyter-tutorial/blob/master/World-of-Jupyter.md), we want to leave you with pro tips for using Jupyter in your future work.
## Importing libraries
First, a word on importing libraries. Previously, we u... | a1a33fa5334d86f6c651762741e3d1dd9226bd6e | 193,184 | ipynb | Jupyter Notebook | week1/jupyter-tutorial/3--Jupyter like a pro.ipynb | leoliu0/FINS5517 | 897fb730c2b7ebac71174cbd08a0bf417a25dbd5 | [
"MIT"
] | 2 | 2021-10-06T13:23:02.000Z | 2021-10-13T23:29:45.000Z | week1/jupyter-tutorial/3--Jupyter like a pro.ipynb | leoliu0/FINS5517 | 897fb730c2b7ebac71174cbd08a0bf417a25dbd5 | [
"MIT"
] | null | null | null | week1/jupyter-tutorial/3--Jupyter like a pro.ipynb | leoliu0/FINS5517 | 897fb730c2b7ebac71174cbd08a0bf417a25dbd5 | [
"MIT"
] | null | null | null | 83.847222 | 44,473 | 0.705115 | true | 4,155 | Qwen/Qwen-72B | 1. YES
2. YES | 0.757794 | 0.91118 | 0.690487 | __label__eng_Latn | 0.991383 | 0.442564 |
# M6803 Assignment3
## Wang Longqi
## Ex.1.
(a). Apply LDLT decomposition on the matrix A. Since the matrix is symmetrical, we only need to calculate L.
$$L_{21}=\frac{20}{8}=2.5 \\ L_{31}=\frac{15}{8}=1.875\\U_{22}=30\\U_{13}=15
\\L_{32}=\frac{A_{32}-L_{31}U_{12}}{U_{22}}=0.4167\\U_{23}=12.5\\U_{33}=A_{33}-L_{31}U_{... | 476dd85e72a66526ae559030d102ae279d2125ed | 14,906 | ipynb | Jupyter Notebook | public/res/M6803.ipynb | wanglongqi/wanglongqi.github.io | 359f16930d4b0af8b377657d9bade7fd64120027 | [
"MIT"
] | 7 | 2015-02-11T02:09:18.000Z | 2021-03-30T03:07:53.000Z | public/res/M6803.ipynb | wanglongqi/wanglongqi.github.io | 359f16930d4b0af8b377657d9bade7fd64120027 | [
"MIT"
] | null | null | null | public/res/M6803.ipynb | wanglongqi/wanglongqi.github.io | 359f16930d4b0af8b377657d9bade7fd64120027 | [
"MIT"
] | 17 | 2015-01-24T14:17:48.000Z | 2022-02-11T16:54:13.000Z | 40.069892 | 333 | 0.381256 | true | 4,290 | Qwen/Qwen-72B | 1. YES
2. YES | 0.857768 | 0.740174 | 0.634898 | __label__yue_Hant | 0.174284 | 0.313411 |
# Decision Tree & Ensemble Learning
Classification And Regression Trees (CART for short) is a term introduced by [Leo Breiman](https://en.wikipedia.org/wiki/Leo_Breiman) to refer to Decision Tree algorithms that can be used for classification or regression predictive modeling problems.
In this lab assignment, you wil... | 09fb6b1d3215dfd91a65ef90b91e92a7b043cc43 | 141,184 | ipynb | Jupyter Notebook | Iris Dataset Ensemble learning.ipynb | jessiececilya/ensemble-irisdataset | a66de222086b14fd0730d6e57152de5ca48a3d8d | [
"MIT"
] | null | null | null | Iris Dataset Ensemble learning.ipynb | jessiececilya/ensemble-irisdataset | a66de222086b14fd0730d6e57152de5ca48a3d8d | [
"MIT"
] | null | null | null | Iris Dataset Ensemble learning.ipynb | jessiececilya/ensemble-irisdataset | a66de222086b14fd0730d6e57152de5ca48a3d8d | [
"MIT"
] | null | null | null | 109.870817 | 77,882 | 0.823634 | true | 6,068 | Qwen/Qwen-72B | 1. YES
2. YES | 0.879147 | 0.803174 | 0.706108 | __label__eng_Latn | 0.900503 | 0.478856 |
###### Content under Creative Commons Attribution license CC-BY 4.0, code under MIT license © 2014 L.A. Barba, C.D. Cooper, G.F. Forsyth. Based on [CFD Python](https://github.com/barbagroup/CFDPython), © 2013 L.A. Barba, also under CC-BY license.
# Relax and hold steady
Welcome to the second notebook of *"Relax and ... | 27ee70949e812e32f3e412dc374494d309d06332 | 396,727 | ipynb | Jupyter Notebook | lessons/05_relax/05_02_2D.Poisson.Equation.ipynb | mcarpe/numerical-mooc | 62b3c14c2c56d85d65c6075f2d7eb44266b49c17 | [
"CC-BY-3.0"
] | 748 | 2015-01-04T22:50:56.000Z | 2022-03-30T20:42:16.000Z | lessons/05_relax/05_02_2D.Poisson.Equation.ipynb | mcarpe/numerical-mooc | 62b3c14c2c56d85d65c6075f2d7eb44266b49c17 | [
"CC-BY-3.0"
] | 62 | 2015-02-02T01:06:07.000Z | 2020-11-09T12:27:41.000Z | lessons/05_relax/05_02_2D.Poisson.Equation.ipynb | mcarpe/numerical-mooc | 62b3c14c2c56d85d65c6075f2d7eb44266b49c17 | [
"CC-BY-3.0"
] | 1,270 | 2015-01-02T19:19:52.000Z | 2022-02-27T01:02:44.000Z | 467.838443 | 175,360 | 0.934189 | true | 5,160 | Qwen/Qwen-72B | 1. YES
2. YES | 0.851953 | 0.727975 | 0.620201 | __label__eng_Latn | 0.972995 | 0.279265 |
<a href="https://colab.research.google.com/github/annissatessffaaye/QAPython/blob/master/02_Python_Numpy.ipynb" target="_parent"></a>
```python
```
The first thing we want to do is import numpy.
```python
import numpy as np
```
Let us first define a Python list containing the ages of 6 people.
```python
ages_l... | b1e595733a7c4e3832e0d7f5e20516ddba4bba09 | 70,359 | ipynb | Jupyter Notebook | 02_Python_Numpy.ipynb | annissatessffaaye/QA-Data-Engineering-Bootcamp-Azure-Python-SQL | 9204dfb4b1e9ac4dd477200a2a79f9aed49c6a11 | [
"MIT"
] | 1 | 2021-11-23T19:57:34.000Z | 2021-11-23T19:57:34.000Z | 02_Python_Numpy.ipynb | annissatessffaaye/QA-Data-Engineering-Bootcamp-Azure-Python-SQL | 9204dfb4b1e9ac4dd477200a2a79f9aed49c6a11 | [
"MIT"
] | null | null | null | 02_Python_Numpy.ipynb | annissatessffaaye/QA-Data-Engineering-Bootcamp-Azure-Python-SQL | 9204dfb4b1e9ac4dd477200a2a79f9aed49c6a11 | [
"MIT"
] | null | null | null | 26.001109 | 240 | 0.327805 | true | 10,262 | Qwen/Qwen-72B | 1. YES
2. YES | 0.835484 | 0.861538 | 0.719801 | __label__krc_Cyrl | 0.712371 | 0.510671 |
<a id='iterative-methods-sparsity'></a>
<div id="qe-notebook-header" style="text-align:right;">
<a href="https://quantecon.org/" title="quantecon.org">
</a>
</div>
# Krylov Methods and Matrix Conditioning
## Contents
- [Krylov Methods and Matrix Conditioning](#Krylov-Methods-and-Matr... | f54df79f8c991f101761771237efd5a1a407dadc | 227,496 | ipynb | Jupyter Notebook | tools_and_techniques/iterative_methods_sparsity.ipynb | shanemcmiken/quantecon-notebooks-julia | c9968403bc866fe1f520762619055bd21e09ad10 | [
"MIT"
] | null | null | null | tools_and_techniques/iterative_methods_sparsity.ipynb | shanemcmiken/quantecon-notebooks-julia | c9968403bc866fe1f520762619055bd21e09ad10 | [
"MIT"
] | null | null | null | tools_and_techniques/iterative_methods_sparsity.ipynb | shanemcmiken/quantecon-notebooks-julia | c9968403bc866fe1f520762619055bd21e09ad10 | [
"MIT"
] | null | null | null | 63.581889 | 26,338 | 0.630719 | true | 18,046 | Qwen/Qwen-72B | 1. YES
2. YES | 0.752013 | 0.810479 | 0.60949 | __label__eng_Latn | 0.990846 | 0.254381 |
# Linear Vs. Non-Linear Functions
** October 2017 **
** Andrew Riberio @ [AndrewRib.com](http://www.andrewrib.com) **
Resources
* https://en.wikipedia.org/wiki/Linear_function
* https://www.montereyinstitute.org/courses/Algebra1/COURSE_TEXT_RESOURCE/U03_L2_T5_text_final.html
* https://en.wikipedia.org/wiki/Linear_com... | 64a4f5c25499c5496728d5c87a37a5e551d60934 | 151,088 | ipynb | Jupyter Notebook | Notebooks/Linear Vs. Non-Linear Functions.ipynb | Andrewnetwork/WorkshopScipy | 739d24b9078fffb84408e7877862618d88d947dc | [
"MIT"
] | 433 | 2017-12-16T20:50:07.000Z | 2021-11-08T13:05:57.000Z | Notebooks/Linear Vs. Non-Linear Functions.ipynb | Andrewnetwork/WorkshopScipy | 739d24b9078fffb84408e7877862618d88d947dc | [
"MIT"
] | 3 | 2017-12-17T06:10:28.000Z | 2018-11-14T15:50:10.000Z | Notebooks/Linear Vs. Non-Linear Functions.ipynb | Andrewnetwork/WorkshopScipy | 739d24b9078fffb84408e7877862618d88d947dc | [
"MIT"
] | 47 | 2017-12-06T20:40:09.000Z | 2019-06-01T11:33:57.000Z | 614.178862 | 72,682 | 0.9375 | true | 893 | Qwen/Qwen-72B | 1. YES
2. YES | 0.972415 | 0.870597 | 0.846582 | __label__eng_Latn | 0.796037 | 0.805226 |
```python
from logicqubit.logic import *
from cmath import *
import numpy as np
import sympy as sp
import scipy
from scipy.optimize import *
import matplotlib.pyplot as plt
```
Cuda is not available!
logicqubit version 1.5.8
https://arxiv.org/abs/1304.3061
https://cpb-us-w2.wpmucdn.com/voices.uchicago.edu/d... | fa162dadd46578dfe84f9470e5f32a0293d7c66e | 16,265 | ipynb | Jupyter Notebook | vqe_2q bell base.ipynb | clnrp/logicqubit-codes | a86b85d704958143a9188dad2d76a591c3ac20dc | [
"Apache-2.0"
] | null | null | null | vqe_2q bell base.ipynb | clnrp/logicqubit-codes | a86b85d704958143a9188dad2d76a591c3ac20dc | [
"Apache-2.0"
] | null | null | null | vqe_2q bell base.ipynb | clnrp/logicqubit-codes | a86b85d704958143a9188dad2d76a591c3ac20dc | [
"Apache-2.0"
] | null | null | null | 29.201077 | 169 | 0.504826 | true | 3,822 | Qwen/Qwen-72B | 1. YES
2. YES | 0.897695 | 0.737158 | 0.661743 | __label__eng_Latn | 0.108575 | 0.375783 |
<!-- dom:TITLE: Project 4, deadline November 18 -->
# Project 4, deadline November 18
<!-- dom:AUTHOR: [Computational Physics I FYS3150/FYS4150](http://www.uio.no/studier/emner/matnat/fys/FYS3150/index-eng.html) at Department of Physics, University of Oslo, Norway -->
<!-- Author: -->
**[Computational Physics I FYS... | 2558e3341603901280ac35b31cdaf8c527a23b3f | 21,176 | ipynb | Jupyter Notebook | doc/Projects/2019/Project4/ipynb/Project4.ipynb | solisius/ComputationalPhysics | 94d32d177881695d443eea34af3410e886b8cb9a | [
"CC0-1.0"
] | 220 | 2016-08-25T09:18:33.000Z | 2022-03-31T14:09:16.000Z | doc/Projects/2019/Project4/ipynb/Project4.ipynb | solisius/ComputationalPhysics | 94d32d177881695d443eea34af3410e886b8cb9a | [
"CC0-1.0"
] | 1 | 2020-12-04T12:55:10.000Z | 2020-12-04T12:55:10.000Z | doc/Projects/2019/Project4/ipynb/Project4.ipynb | solisius/ComputationalPhysics | 94d32d177881695d443eea34af3410e886b8cb9a | [
"CC0-1.0"
] | 136 | 2016-08-25T09:04:56.000Z | 2022-03-12T09:54:21.000Z | 39.142329 | 410 | 0.619805 | true | 3,871 | Qwen/Qwen-72B | 1. YES
2. YES
| 0.737158 | 0.853913 | 0.629469 | __label__eng_Latn | 0.998104 | 0.300797 |
```python
%pylab inline
```
Populating the interactive namespace from numpy and matplotlib
```python
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import proj3d, Axes3D
```
```python
from sympy.parsing.sympy_parser import parse_expr
from sympy import Matrix, symbols, expand
```
```python
from ma... | c100c1f475a62b34fb7476e96eff616efe7d7001 | 268,891 | ipynb | Jupyter Notebook | Applied Math/Y2S2/.ipynb_checkpoints/directional derivatives-checkpoint.ipynb | darkeclipz/jupyter-notebooks | 5de784244ad9db12cfacbbec3053b11f10456d7e | [
"Unlicense"
] | 1 | 2018-08-28T12:16:12.000Z | 2018-08-28T12:16:12.000Z | Applied Math/Y2S2/.ipynb_checkpoints/directional derivatives-checkpoint.ipynb | darkeclipz/jupyter-notebooks | 5de784244ad9db12cfacbbec3053b11f10456d7e | [
"Unlicense"
] | null | null | null | Applied Math/Y2S2/.ipynb_checkpoints/directional derivatives-checkpoint.ipynb | darkeclipz/jupyter-notebooks | 5de784244ad9db12cfacbbec3053b11f10456d7e | [
"Unlicense"
] | null | null | null | 1,407.806283 | 263,616 | 0.957488 | true | 970 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91848 | 0.766294 | 0.703826 | __label__eng_Latn | 0.31925 | 0.473554 |
# Mine-Sweeper and Neural Networks
## Getting Started
The goals of this project were to gain experience in trying to translate a problem into one solvable with neural networks. Beating a game of Mine-Sweeper, through predicting mine spaces, is not something that can be solved with iterative functions, so neural nets m... | 7f8a65937268c51e97f8bb9635b9bb9314d51460 | 103,344 | ipynb | Jupyter Notebook | Project Stuff/DemoNotebook.ipynb | CSCI4850/S20-team4-project | 89ff9a06b4bafcad2f1dedd8bcc6087f3d88a9e5 | [
"MIT"
] | null | null | null | Project Stuff/DemoNotebook.ipynb | CSCI4850/S20-team4-project | 89ff9a06b4bafcad2f1dedd8bcc6087f3d88a9e5 | [
"MIT"
] | null | null | null | Project Stuff/DemoNotebook.ipynb | CSCI4850/S20-team4-project | 89ff9a06b4bafcad2f1dedd8bcc6087f3d88a9e5 | [
"MIT"
] | null | null | null | 106.650155 | 66,812 | 0.765173 | true | 7,716 | Qwen/Qwen-72B | 1. YES
2. YES | 0.766294 | 0.665411 | 0.5099 | __label__eng_Latn | 0.7231 | 0.022997 |
# The Linear Classifier
```{eval-rst}
Last updated |lastupdate|
```
[](https://colab.research.google.com/github/vanvalenlab/bebi205/blob/master/bebi205/notebooks/linear-classifier.ipynb)
[](https://colab.research.google.com/github/vanvalenlab/bebi205/blob/master/bebi205/notebooks/linear-classifier-key.ipynb)
To illus... | 8780a0be254be93ce28b887962fdd5691af6d986 | 21,942 | ipynb | Jupyter Notebook | bebi205/notebooks/linear-classifier.ipynb | vanvalenlab/bebi205 | 14f0d9d266b1fb329f34cc882c4f27778bca626a | [
"Apache-2.0"
] | null | null | null | bebi205/notebooks/linear-classifier.ipynb | vanvalenlab/bebi205 | 14f0d9d266b1fb329f34cc882c4f27778bca626a | [
"Apache-2.0"
] | 1 | 2021-12-13T00:58:26.000Z | 2021-12-13T00:58:26.000Z | bebi205/notebooks/linear-classifier.ipynb | vanvalenlab/bebi205 | 14f0d9d266b1fb329f34cc882c4f27778bca626a | [
"Apache-2.0"
] | null | null | null | 38.293194 | 648 | 0.603227 | true | 3,411 | Qwen/Qwen-72B | 1. YES
2. YES | 0.880797 | 0.795658 | 0.700813 | __label__eng_Latn | 0.993276 | 0.466556 |
```julia
using Catalyst
# NOTE: both models MUST preserve the same ordering of reactions in order to detect
# how the nonlinear reactions are to be transformed using LMA
rn_nonlinear = @reaction_network begin
σ_b, g + p → 0
σ_u*(1-g), 0 ⇒ g + p
ρ_u, g → g + p
ρ_b*(1-g), 0 ⇒ p
1, p → 0
e... | c6faf8a7ad7b74b22a2a77b627a32044828ff684 | 108,505 | ipynb | Jupyter Notebook | examples/LMA_example.ipynb | FHoltorf/MomentClosure.jl | 8c2a1b7870973e96ce9382d47cd63023f1891cf6 | [
"MIT"
] | 27 | 2021-02-21T00:44:05.000Z | 2022-03-25T23:48:52.000Z | examples/LMA_example.ipynb | FHoltorf/MomentClosure.jl | 8c2a1b7870973e96ce9382d47cd63023f1891cf6 | [
"MIT"
] | 10 | 2021-02-26T15:44:04.000Z | 2022-03-16T12:48:27.000Z | examples/LMA_example.ipynb | FHoltorf/MomentClosure.jl | 8c2a1b7870973e96ce9382d47cd63023f1891cf6 | [
"MIT"
] | 3 | 2021-02-21T01:20:10.000Z | 2022-03-24T13:18:07.000Z | 133.462485 | 19,820 | 0.664366 | true | 2,787 | Qwen/Qwen-72B | 1. YES
2. YES | 0.841826 | 0.857768 | 0.722091 | __label__eng_Latn | 0.636359 | 0.515991 |
# Section 2.1 $\quad$ Echelon Form of a Matrix
## Definitions
An $m\times n$ matrix $A$ is said to be in $\underline{\hspace{3in}}$ if <br />
(a) <br /><br /><br /><br />
(b) <br /><br /><br /><br />
(c) <br /><br /><br /><br />
(d) <br /><br /><br /><br />
An $m\times n$ matrix satisfying properties **a**, **b**, ... | 9bb33d4a003307f9b23fe157413c36c05cde52f8 | 8,111 | ipynb | Jupyter Notebook | Jupyter_Notes/Lecture05_Sec2-1_EchelonForm.ipynb | xiuquan0418/MAT341 | 2fb7ec4e5f0771f10719cb5e4a00a7ab07c49b59 | [
"MIT"
] | null | null | null | Jupyter_Notes/Lecture05_Sec2-1_EchelonForm.ipynb | xiuquan0418/MAT341 | 2fb7ec4e5f0771f10719cb5e4a00a7ab07c49b59 | [
"MIT"
] | null | null | null | Jupyter_Notes/Lecture05_Sec2-1_EchelonForm.ipynb | xiuquan0418/MAT341 | 2fb7ec4e5f0771f10719cb5e4a00a7ab07c49b59 | [
"MIT"
] | null | null | null | 21.921622 | 117 | 0.372087 | true | 1,653 | Qwen/Qwen-72B | 1. YES
2. YES | 0.743168 | 0.819893 | 0.609318 | __label__eng_Latn | 0.48535 | 0.253981 |
# Linear Regression
# Simple Linear Regression
Running a SLR in Python is fairly simple once you know how to use the relevant functions. What might be confusing is that there exist several packages which provide functions for linear regression. We will use functions from the `statsmodels` (sub-)package. Other package... | fb1a363f3ebe7431d210622f1ed073a9aa889de8 | 235,587 | ipynb | Jupyter Notebook | 0205_LinearRegression.ipynb | mauriciocpereira/ML_in_Finance_UZH | d99fa0f56b92f4f81f9bbe024de317a7949f0d38 | [
"MIT"
] | null | null | null | 0205_LinearRegression.ipynb | mauriciocpereira/ML_in_Finance_UZH | d99fa0f56b92f4f81f9bbe024de317a7949f0d38 | [
"MIT"
] | null | null | null | 0205_LinearRegression.ipynb | mauriciocpereira/ML_in_Finance_UZH | d99fa0f56b92f4f81f9bbe024de317a7949f0d38 | [
"MIT"
] | null | null | null | 104.473171 | 57,242 | 0.792582 | true | 15,282 | Qwen/Qwen-72B | 1. YES
2. YES | 0.79053 | 0.795658 | 0.628992 | __label__eng_Latn | 0.853723 | 0.299689 |
```python
import sympy
from einsteinpy.symbolic import MetricTensor, ChristoffelSymbols, RiemannCurvatureTensor
import astropy.units as u
from einsteinpy import constant
from einsteinpy.utils import scalar_factor as sf
from einsteinpy.utils import scalar_factor_derivative as sfd
from einsteinpy.utils import time_veloc... | d4356a2ed46c1d8139dc8c08e0f8b04254169f62 | 3,874 | ipynb | Jupyter Notebook | Friedman-Robertson-Walker Spacetime.ipynb | SheepWaitForWolf/General-Relativity | e9eb0f8cc65be9368c6648c8afaa1f8e631516a8 | [
"MIT"
] | 1 | 2021-06-04T11:01:54.000Z | 2021-06-04T11:01:54.000Z | Friedman-Robertson-Walker Spacetime.ipynb | SheepWaitForWolf/General-Relativity | e9eb0f8cc65be9368c6648c8afaa1f8e631516a8 | [
"MIT"
] | null | null | null | Friedman-Robertson-Walker Spacetime.ipynb | SheepWaitForWolf/General-Relativity | e9eb0f8cc65be9368c6648c8afaa1f8e631516a8 | [
"MIT"
] | null | null | null | 41.655914 | 1,584 | 0.603511 | true | 245 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.689306 | 0.611555 | __label__eng_Latn | 0.540223 | 0.259178 |
```
import numpy as np
import matplotlib.pyplot as plt
import scipy.sparse as sps
```
# Dataset, binary data and continuous data
```
def digit_basis(geometry):
num_bit = np.prod(geometry)
M = 2**num_bit
x = np.arange(M)
return x
def binary_basis(geometry):
num_bit = np.prod(geometry)
M = 2**... | 11bde836fbb8a20d4d076bfafb9670731b66eb6b | 395,481 | ipynb | Jupyter Notebook | notebooks/qcbm_advanced.ipynb | GiggleLiu/QuantumCircuitBornMachine | cb0648843210a249392b3600577eb69455b78b3d | [
"MIT"
] | 32 | 2018-04-10T14:34:28.000Z | 2021-08-20T15:03:21.000Z | notebooks/qcbm_advanced.ipynb | GiggleLiu/QuantumCircuitBornMachine | cb0648843210a249392b3600577eb69455b78b3d | [
"MIT"
] | null | null | null | notebooks/qcbm_advanced.ipynb | GiggleLiu/QuantumCircuitBornMachine | cb0648843210a249392b3600577eb69455b78b3d | [
"MIT"
] | 8 | 2018-06-17T01:09:50.000Z | 2021-04-19T20:53:19.000Z | 174.067342 | 63,538 | 0.84182 | true | 12,857 | Qwen/Qwen-72B | 1. YES
2. YES | 0.824462 | 0.766294 | 0.63178 | __label__eng_Latn | 0.388405 | 0.306167 |
# Taylor series
> ### $ f(x) = a_0 + a_1x + a_2x^2 + a_3x^3 + a_4x^4 + ... = \displaystyle \sum_{i=0}^{\infty} a_{i}x^{i} $
> ### $ f(x) = a_0(x-a)^0 + a_1(x-a)^1 + a_2(x-a)^2 + a_3(x-a)^3 + a_4(x-a)^4 + ... = \displaystyle \sum_{i=0}^{\infty} a_{i}(x-a)^{i} $
$$\require{cancel}$$
# differential
> ### $ \therefore f(... | 5ce5371943aaa03e908095d54545ceefd2ee15dd | 27,964 | ipynb | Jupyter Notebook | python/TaylorSeries.ipynb | karng87/nasm_game | a97fdb09459efffc561d2122058c348c93f1dc87 | [
"MIT"
] | null | null | null | python/TaylorSeries.ipynb | karng87/nasm_game | a97fdb09459efffc561d2122058c348c93f1dc87 | [
"MIT"
] | null | null | null | python/TaylorSeries.ipynb | karng87/nasm_game | a97fdb09459efffc561d2122058c348c93f1dc87 | [
"MIT"
] | null | null | null | 36.602094 | 738 | 0.449292 | true | 7,915 | Qwen/Qwen-72B | 1. YES
2. YES | 0.932453 | 0.795658 | 0.741914 | __label__eng_Latn | 0.150096 | 0.562047 |
Trusted Notebook" width="500 px" align="left">
# Elementary arithmetic operations
In this tutorial, we are going to provide a construction of quantum networks effecting basic arithmetic operations, covering from addition to modular exponentiation, providing some executable examples using the simulator and five qubit ... | 11f8cb48432f80d3eba40e4cee40d5a779391931 | 104,271 | ipynb | Jupyter Notebook | awards/teach_me_qiskit_2018/elementary_arithmetic_operations/elementary_arithmetic_operations.ipynb | Aniruddha120/qiskit-community-tutorials | 3212a140f3753c2aa0f53691cee96952c50dc898 | [
"Apache-2.0"
] | null | null | null | awards/teach_me_qiskit_2018/elementary_arithmetic_operations/elementary_arithmetic_operations.ipynb | Aniruddha120/qiskit-community-tutorials | 3212a140f3753c2aa0f53691cee96952c50dc898 | [
"Apache-2.0"
] | null | null | null | awards/teach_me_qiskit_2018/elementary_arithmetic_operations/elementary_arithmetic_operations.ipynb | Aniruddha120/qiskit-community-tutorials | 3212a140f3753c2aa0f53691cee96952c50dc898 | [
"Apache-2.0"
] | null | null | null | 65.869236 | 9,568 | 0.700271 | true | 15,396 | Qwen/Qwen-72B | 1. YES
2. YES | 0.882428 | 0.828939 | 0.731479 | __label__eng_Latn | 0.841978 | 0.537802 |
# Homework 01
Congratulations! You've managed to open this Juypter notebook on either Github or on your local machine.
Help for Jupyter Notebooks can be found in the Jupyter Lab by going to `Help > Notebook Reference`. You can also go to the [Notebook basics](https://jupyter-notebook.readthedocs.io/en/latest/examples... | 069f3acc64e8ebf5d4894d3de3ba1b21b2dae102 | 576,915 | ipynb | Jupyter Notebook | Hwk01.ipynb | jhbuckner/Hwk01 | 8077ad222921b4825c7c99739fe72587c75ee9c3 | [
"MIT"
] | null | null | null | Hwk01.ipynb | jhbuckner/Hwk01 | 8077ad222921b4825c7c99739fe72587c75ee9c3 | [
"MIT"
] | null | null | null | Hwk01.ipynb | jhbuckner/Hwk01 | 8077ad222921b4825c7c99739fe72587c75ee9c3 | [
"MIT"
] | null | null | null | 80.891054 | 339 | 0.779838 | true | 2,181 | Qwen/Qwen-72B | 1. YES
2. YES | 0.851953 | 0.94079 | 0.801508 | __label__eng_Latn | 0.961096 | 0.700505 |
```python
# A geometric algebra for the unit sphere in R^3
# as a submanifold of R^3 with spherical coordintes.
# Make SymPy available to this program:
import sympy
from sympy import *
# Make GAlgebra available to this program:
from galgebra.ga import *
from galgebra.mv import *
from galgebra.printer import Fmt, ... | 27280692b3525be9efce2283b5f909382be79565 | 2,629 | ipynb | Jupyter Notebook | python/GeometryAG/gaprimer/sp2sp3.ipynb | karng87/nasm_game | a97fdb09459efffc561d2122058c348c93f1dc87 | [
"MIT"
] | null | null | null | python/GeometryAG/gaprimer/sp2sp3.ipynb | karng87/nasm_game | a97fdb09459efffc561d2122058c348c93f1dc87 | [
"MIT"
] | null | null | null | python/GeometryAG/gaprimer/sp2sp3.ipynb | karng87/nasm_game | a97fdb09459efffc561d2122058c348c93f1dc87 | [
"MIT"
] | null | null | null | 26.826531 | 97 | 0.550399 | true | 430 | Qwen/Qwen-72B | 1. YES
2. YES | 0.907312 | 0.785309 | 0.71252 | __label__eng_Latn | 0.880221 | 0.493754 |
# Free Body Diagram for Rigid Bodies
Renato Naville Watanabe
```python
import numpy as np
import matplotlib.pyplot as plt
%matplotlib notebook
```
## Equivalent systems
A set of forces and moments is considered equivalent if its resultant force and sum of the moments computed relative to a given point are the sam... | 99b5ba0df64a87d9b02bda047cc0e0295bd7cec0 | 113,766 | ipynb | Jupyter Notebook | notebooks/FreeBodyDiagramForRigidBodies.ipynb | ahmadhassan01/bmc | 3114b7d3ecd1f7c678fac0c04e8e139ac2898992 | [
"MIT"
] | null | null | null | notebooks/FreeBodyDiagramForRigidBodies.ipynb | ahmadhassan01/bmc | 3114b7d3ecd1f7c678fac0c04e8e139ac2898992 | [
"MIT"
] | null | null | null | notebooks/FreeBodyDiagramForRigidBodies.ipynb | ahmadhassan01/bmc | 3114b7d3ecd1f7c678fac0c04e8e139ac2898992 | [
"MIT"
] | null | null | null | 71.10375 | 33,421 | 0.652181 | true | 12,859 | Qwen/Qwen-72B | 1. YES
2. YES | 0.637031 | 0.83762 | 0.53359 | __label__eng_Latn | 0.817628 | 0.078037 |
### Introduction to ARCH and GARCH models
Various problems such as option pricing in finance have motivated the study of the volatility, or variability, of a time series. ARMA models were used to model the conditional mean of a process when the conditional variance was constant. In many problems, however, the assumpti... | 23904cae2ef7e03c1bbeee5bb4f874f6529dc693 | 621,085 | ipynb | Jupyter Notebook | Volatility modeling.ipynb | ddeMoivre/Time-Series-Analysis | 02ad1dae6795e7036252eb738a137ab5af0a81cb | [
"MIT"
] | null | null | null | Volatility modeling.ipynb | ddeMoivre/Time-Series-Analysis | 02ad1dae6795e7036252eb738a137ab5af0a81cb | [
"MIT"
] | null | null | null | Volatility modeling.ipynb | ddeMoivre/Time-Series-Analysis | 02ad1dae6795e7036252eb738a137ab5af0a81cb | [
"MIT"
] | null | null | null | 513.717949 | 138,276 | 0.931177 | true | 7,949 | Qwen/Qwen-72B | 1. YES
2. YES | 0.931462 | 0.849971 | 0.791716 | __label__eng_Latn | 0.581134 | 0.677755 |
```python
import numpy as np
from scipy import ndimage
from scipy import spatial
from scipy import io
from scipy import sparse
from scipy.sparse import csgraph
from scipy import linalg
from matplotlib import pyplot as plt
import seaborn as sns
from skimage import data
from skimage import color
from skimage import img_a... | dcd8908014717b8c56834699449c6194682808a1 | 128,538 | ipynb | Jupyter Notebook | notebooks/tikhonov_regularization.ipynb | mdbartos/graph-signals | e598d8fe7bcef101f740f7b810414043c607f06e | [
"MIT"
] | 22 | 2018-10-23T12:13:38.000Z | 2022-01-26T00:00:44.000Z | notebooks/tikhonov_regularization.ipynb | mdbartos/graph-signals | e598d8fe7bcef101f740f7b810414043c607f06e | [
"MIT"
] | 1 | 2021-03-25T07:49:54.000Z | 2021-03-25T07:49:54.000Z | notebooks/tikhonov_regularization.ipynb | mdbartos/graph-signals | e598d8fe7bcef101f740f7b810414043c607f06e | [
"MIT"
] | 6 | 2018-10-23T13:37:49.000Z | 2021-12-11T03:52:51.000Z | 382.553571 | 75,888 | 0.927088 | true | 1,304 | Qwen/Qwen-72B | 1. YES
2. YES | 0.926304 | 0.831143 | 0.769891 | __label__eng_Latn | 0.410553 | 0.627047 |
# Driving a skyrmion with spin-polarised current
**Author:** Weiwei Wang (2014)
**Edited:** Marijan Beg (2016)
The implemented equation in finmag with STT is [1,2],
\begin{equation}
\frac{\partial \mathbf{m}}{\partial t} = - \gamma \mathbf{m} \times \mathbf{H} + \alpha \mathbf{m} \times \frac{\partial \mathbf{m}}{... | 2025b0601ba319caa7701841ae4f7a4a8d334aa8 | 103,362 | ipynb | Jupyter Notebook | doc/ipython_notebooks_src/tutorial-skyrmion-nucleation-and-manipulation.ipynb | davidcortesortuno/finmag | 9ac0268d2c0e45faf1284cee52a73525aa589e2b | [
"BSL-1.0"
] | 10 | 2018-03-24T07:43:17.000Z | 2022-03-26T10:42:27.000Z | doc/ipython_notebooks_src/tutorial-skyrmion-nucleation-and-manipulation.ipynb | davidcortesortuno/finmag | 9ac0268d2c0e45faf1284cee52a73525aa589e2b | [
"BSL-1.0"
] | 21 | 2018-03-26T15:08:53.000Z | 2021-07-10T16:11:14.000Z | doc/ipython_notebooks_src/tutorial-skyrmion-nucleation-and-manipulation.ipynb | davidcortesortuno/finmag | 9ac0268d2c0e45faf1284cee52a73525aa589e2b | [
"BSL-1.0"
] | 7 | 2018-04-09T11:50:48.000Z | 2021-06-10T09:23:25.000Z | 218.063291 | 16,732 | 0.877508 | true | 2,110 | Qwen/Qwen-72B | 1. YES
2. YES | 0.757794 | 0.709019 | 0.537291 | __label__eng_Latn | 0.712471 | 0.086636 |
# Regression
## Linear Regression
### Step 1 : Setup the Environment
```python
# Import necessary Libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
```
### Step 2 : Clean the data and Visually analyse the data
```python
x = 10 * np.random.random(50)
... | aca73995dcf3599260dfe94bdd06efe06309a886 | 456,937 | ipynb | Jupyter Notebook | python/MachineLearning/Basic Linear regression.ipynb | BharathC15/NielitChennai | c817aaf63b741eb7a8e4c1df16b5038a0b4f0df7 | [
"MIT"
] | null | null | null | python/MachineLearning/Basic Linear regression.ipynb | BharathC15/NielitChennai | c817aaf63b741eb7a8e4c1df16b5038a0b4f0df7 | [
"MIT"
] | null | null | null | python/MachineLearning/Basic Linear regression.ipynb | BharathC15/NielitChennai | c817aaf63b741eb7a8e4c1df16b5038a0b4f0df7 | [
"MIT"
] | 1 | 2020-06-11T08:04:43.000Z | 2020-06-11T08:04:43.000Z | 361.500791 | 112,772 | 0.93902 | true | 4,036 | Qwen/Qwen-72B | 1. YES
2. YES | 0.924142 | 0.868827 | 0.802919 | __label__eng_Latn | 0.250366 | 0.703783 |
# Contraste Bilateral: Cálculo del Error de tipo II
## Parámetro $p$ en variables de $Bernoulli$
#### Autor:
Sergio García Prado - [garciparedes.me](https://garciparedes.me)
#### Fecha:
Abril de 2018
#### Agradecimientos:
Me gustaría agradecer a la profesora [Pilar Rodríguez del Tío](http://www.eio.uva.es/~pilar/)... | e753369dd0ebdf2771f44ba295a525af5cec3745 | 90,656 | ipynb | Jupyter Notebook | notebooks/beta-error-bernoulli-hypothesis-test.ipynb | garciparedes/r-examples | 0e0e18439ad859f97eafb27c5e7f77d33da28bc6 | [
"Apache-2.0"
] | 1 | 2017-09-15T19:56:31.000Z | 2017-09-15T19:56:31.000Z | notebooks/beta-error-bernoulli-hypothesis-test.ipynb | garciparedes/r-examples | 0e0e18439ad859f97eafb27c5e7f77d33da28bc6 | [
"Apache-2.0"
] | 5 | 2018-03-23T09:34:55.000Z | 2019-01-09T14:13:32.000Z | notebooks/beta-error-bernoulli-hypothesis-test.ipynb | garciparedes/r-examples | 0e0e18439ad859f97eafb27c5e7f77d33da28bc6 | [
"Apache-2.0"
] | null | null | null | 370.02449 | 81,660 | 0.907772 | true | 2,238 | Qwen/Qwen-72B | 1. YES
2. YES | 0.835484 | 0.793106 | 0.662627 | __label__spa_Latn | 0.945848 | 0.377835 |
[Table of Contents](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)
# Designing Nonlinear Kalman Filters
```python
#format the book
%matplotlib inline
from __future__ import division, print_function
from book_format import load_style
load_style()
`... | 5100e477137421ce9795850b0a68ac07e4dd5698 | 131,570 | ipynb | Jupyter Notebook | Appendix-G-Designing-Nonlinear-Kalman-Filters.ipynb | asfaltboy/Kalman-and-Bayesian-Filters-in-Python | 4669507d7a8274a40cff93a011d34b6171227ea6 | [
"CC-BY-4.0"
] | 4 | 2017-10-17T06:53:41.000Z | 2021-04-03T14:16:06.000Z | Appendix-G-Designing-Nonlinear-Kalman-Filters.ipynb | asfaltboy/Kalman-and-Bayesian-Filters-in-Python | 4669507d7a8274a40cff93a011d34b6171227ea6 | [
"CC-BY-4.0"
] | null | null | null | Appendix-G-Designing-Nonlinear-Kalman-Filters.ipynb | asfaltboy/Kalman-and-Bayesian-Filters-in-Python | 4669507d7a8274a40cff93a011d34b6171227ea6 | [
"CC-BY-4.0"
] | 4 | 2017-12-08T09:27:49.000Z | 2022-02-21T17:14:06.000Z | 125.185538 | 34,930 | 0.819343 | true | 7,848 | Qwen/Qwen-72B | 1. YES
2. YES | 0.685949 | 0.847968 | 0.581663 | __label__eng_Latn | 0.982423 | 0.189728 |
# Detectors Comparison
O Detectors Comparison é um software coletor de estatísticas de algoritmos de detecção e extração de keypoints em fotografias turisticas.
Os dados estatísticos coletados são utilizados para avaliar o desempenho e precisão dos algoritmos: [ORB](), [BRISK](), [AKAZE](), [SIFT]() e [SURF]() em re... | 87fbfe87c525bed6262ef8937f3cdec00c7007cc | 20,051 | ipynb | Jupyter Notebook | Features.ipynb | oraphaBorges/detectors_comparison | e63ccddcf8ca8b6f5c0daa85b4b6f5491f82d288 | [
"Unlicense"
] | null | null | null | Features.ipynb | oraphaBorges/detectors_comparison | e63ccddcf8ca8b6f5c0daa85b4b6f5491f82d288 | [
"Unlicense"
] | null | null | null | Features.ipynb | oraphaBorges/detectors_comparison | e63ccddcf8ca8b6f5c0daa85b4b6f5491f82d288 | [
"Unlicense"
] | null | null | null | 39.862823 | 1,615 | 0.579173 | true | 2,890 | Qwen/Qwen-72B | 1. YES
2. YES | 0.795658 | 0.692642 | 0.551106 | __label__por_Latn | 0.346808 | 0.118734 |
```python
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import scipy
sns.set_context('notebook', font_scale=1.5)
```
```python
import warnings
warnings.simplefilter('ignore', FutureWarning)
```
**1**. (25 points)
In this ex... | ec3eb999b468de04a42d65de361970cc71efe3f8 | 21,288 | ipynb | Jupyter Notebook | HW03.ipynb | lyz1206/STA663-Statistical-Computation-Python- | ed36e9a76746afdf5800ebb6d6632def0964f21b | [
"MIT"
] | null | null | null | HW03.ipynb | lyz1206/STA663-Statistical-Computation-Python- | ed36e9a76746afdf5800ebb6d6632def0964f21b | [
"MIT"
] | null | null | null | HW03.ipynb | lyz1206/STA663-Statistical-Computation-Python- | ed36e9a76746afdf5800ebb6d6632def0964f21b | [
"MIT"
] | null | null | null | 24.30137 | 331 | 0.460071 | true | 3,452 | Qwen/Qwen-72B | 1. YES
2. YES | 0.851953 | 0.810479 | 0.69049 | __label__eng_Latn | 0.944772 | 0.44257 |
# Part 3: Softmax Regression
```
# Execute this code block to install dependencies when running on colab
try:
import torch
except:
from os.path import exists
from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag
platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag()... | 8c92d073da9901855b4194df2df12f93ef2a920a | 27,276 | ipynb | Jupyter Notebook | 1_3_softmax_regression_submit_29299675.ipynb | mjjackey/DL_Lab_Soton | 5df0dc3124e6fae6c27bfb99d70c457dd77935c5 | [
"Apache-2.0"
] | 1 | 2021-11-09T09:49:16.000Z | 2021-11-09T09:49:16.000Z | 1_3_softmax_regression_submit_29299675.ipynb | mjjackey/DL_Lab_Soton | 5df0dc3124e6fae6c27bfb99d70c457dd77935c5 | [
"Apache-2.0"
] | null | null | null | 1_3_softmax_regression_submit_29299675.ipynb | mjjackey/DL_Lab_Soton | 5df0dc3124e6fae6c27bfb99d70c457dd77935c5 | [
"Apache-2.0"
] | null | null | null | 27,276 | 27,276 | 0.666483 | true | 5,738 | Qwen/Qwen-72B | 1. YES
2. YES | 0.896251 | 0.798187 | 0.715376 | __label__eng_Latn | 0.918857 | 0.50039 |
# One-dimensional advection equation
We want to solve the following PDE:
\begin{equation}
\frac{\partial \phi}{\partial t} + u \frac{\partial \phi}{\partial x} = 0
\end{equation}
The independen variables (i.e, $x$ and $t$) are used as input values for the NN, and the solution (i.e. $\phi$) is the output. In orde... | d3ccc6ec93f75a60af6203729c4d2f298b03c58c | 625,051 | ipynb | Jupyter Notebook | examples/examples/01_adv1d.ipynb | smatkovi/nangs | b9ab6f32fe3632d9ee403f197742cc203670217d | [
"Apache-2.0"
] | 2 | 2021-02-26T17:44:52.000Z | 2021-04-05T10:27:44.000Z | examples/examples/01_adv1d.ipynb | smatkovi/nangs | b9ab6f32fe3632d9ee403f197742cc203670217d | [
"Apache-2.0"
] | null | null | null | examples/examples/01_adv1d.ipynb | smatkovi/nangs | b9ab6f32fe3632d9ee403f197742cc203670217d | [
"Apache-2.0"
] | null | null | null | 101.965905 | 61,900 | 0.851107 | true | 4,213 | Qwen/Qwen-72B | 1. YES
2. YES | 0.851953 | 0.746139 | 0.635675 | __label__yue_Hant | 0.320506 | 0.315217 |
```python
import numpy as np
from sympy.solvers import solve
from sympy import Symbol
import sympy
import matplotlib.pyplot as plt
```
#### Реализовать явный метод Эйлера
```python
def euler_method(f, t0, tn, tau, y0):
eps = tau / 10000
while t0 < tn and abs(t0 - tn) > eps:
y0 += ... | 5e8eecfaf78cfc8197f04d22b9f9a5749eea6328 | 114,099 | ipynb | Jupyter Notebook | Damarad_Viktor/differencial_systems.ipynb | droidroot1995/DAFE_Python_914 | 0de65a84ab7f4c8f24b83a5747f71f52d57ecc20 | [
"Unlicense"
] | null | null | null | Damarad_Viktor/differencial_systems.ipynb | droidroot1995/DAFE_Python_914 | 0de65a84ab7f4c8f24b83a5747f71f52d57ecc20 | [
"Unlicense"
] | 7 | 2021-05-08T22:02:59.000Z | 2021-05-13T22:44:27.000Z | Damarad_Viktor/differencial_systems.ipynb | droidroot1995/DAFE_Python_914 | 0de65a84ab7f4c8f24b83a5747f71f52d57ecc20 | [
"Unlicense"
] | 13 | 2021-02-13T07:32:10.000Z | 2021-05-15T09:09:08.000Z | 219 | 26,100 | 0.910131 | true | 1,448 | Qwen/Qwen-72B | 1. YES
2. YES | 0.935347 | 0.857768 | 0.80231 | __label__eng_Latn | 0.162611 | 0.702369 |
```python
from decodes.core import *
from decodes.io.jupyter_out import JupyterOut
out = JupyterOut.unit_square( )
```
# Alternate Coordinate Geometry
todo
\begin{align}
x = r \ cos\theta \\
y = r \ sin\theta
\end{align}
### Cylindrical Coordinates
\begin{eqnarray}
x &=& r \ cos\theta \\
y &=& r \ sin\th... | 01e426468b930e59a3e841bb870f26e27ac28a6e | 4,196 | ipynb | Jupyter Notebook | 103 - Points, Vectors, and Coordinate Systems/109 - Alternate Coordinate Geometry.ipynb | ksteinfe/decodes_ipynb | 2e4bb6b398472fc61ef8b88dad7babbdeb2a5754 | [
"MIT"
] | 1 | 2018-05-15T14:31:23.000Z | 2018-05-15T14:31:23.000Z | 103 - Points, Vectors, and Coordinate Systems/109 - Alternate Coordinate Geometry.ipynb | ksteinfe/decodes_ipynb | 2e4bb6b398472fc61ef8b88dad7babbdeb2a5754 | [
"MIT"
] | null | null | null | 103 - Points, Vectors, and Coordinate Systems/109 - Alternate Coordinate Geometry.ipynb | ksteinfe/decodes_ipynb | 2e4bb6b398472fc61ef8b88dad7babbdeb2a5754 | [
"MIT"
] | 2 | 2020-05-19T05:40:18.000Z | 2020-06-28T02:18:08.000Z | 22.928962 | 147 | 0.517636 | true | 326 | Qwen/Qwen-72B | 1. YES
2. YES | 0.946597 | 0.787931 | 0.745853 | __label__eng_Latn | 0.550333 | 0.571198 |
```python
%reload_ext nb_black
```
<IPython.core.display.Javascript object>
```python
import numpy as np
import matplotlib.pyplot as plt
from quantum_systems import ODQD, GeneralOrbitalSystem
```
<IPython.core.display.Javascript object>
```python
l = 10
grid_length = 10
num_grid_points = 2001
omega =... | 3aa1b9481b5f24b9afc2a4fde7762b672cb7c6d1 | 160,398 | ipynb | Jupyter Notebook | odho-example.ipynb | Schoyen/tdhf-project-fys4411 | b0231c0d759382c14257cc4572698aa80c1c94d0 | [
"MIT"
] | 1 | 2021-06-03T00:34:57.000Z | 2021-06-03T00:34:57.000Z | odho-example.ipynb | Schoyen/tdhf-project-fys4411 | b0231c0d759382c14257cc4572698aa80c1c94d0 | [
"MIT"
] | null | null | null | odho-example.ipynb | Schoyen/tdhf-project-fys4411 | b0231c0d759382c14257cc4572698aa80c1c94d0 | [
"MIT"
] | null | null | null | 275.597938 | 139,532 | 0.897804 | true | 2,123 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.692642 | 0.614515 | __label__eng_Latn | 0.244144 | 0.266055 |
# Travel Times in 1D Models
## Name:
## Date:
```python
# If using Anaconda3 on your machine you can do without this. This is for Azure people.
#!pip install obspy # TODO Uncomment if on Azure
```
## Computing the $X$ offset and travel time $T$ of the downgoing ray in a linear velocity gradient
To begin, recall... | 03d465c9f2f140b4163b3fe3d6a31173efe9fe36 | 254,057 | ipynb | Jupyter Notebook | rayPaths/rayPaths.ipynb | bakerb845/ess412_introToSeismo | ad87b6acdfb40ad63ac100b15dbe168e56cdb4dd | [
"MIT"
] | 2 | 2019-08-28T15:46:26.000Z | 2020-10-07T00:12:54.000Z | rayPaths/rayPaths.ipynb | bakerb845/ess412_introToSeismo | ad87b6acdfb40ad63ac100b15dbe168e56cdb4dd | [
"MIT"
] | null | null | null | rayPaths/rayPaths.ipynb | bakerb845/ess412_introToSeismo | ad87b6acdfb40ad63ac100b15dbe168e56cdb4dd | [
"MIT"
] | 5 | 2018-02-16T23:39:59.000Z | 2019-11-08T21:44:49.000Z | 248.588063 | 27,692 | 0.885203 | true | 9,479 | Qwen/Qwen-72B | 1. YES
2. YES | 0.90053 | 0.847968 | 0.76362 | __label__eng_Latn | 0.909239 | 0.612478 |
# 3.2.3 Multiple Regression From Simple Univariate Regression
Suppose we have a *univariate* (p = 1) model with no intercept (3.23):
$$Y=X\beta+\varepsilon$$
The least squares estimate and residuals are (3.24):
$$
\begin{equation}
\hat{\beta} = \cfrac{\sum_1^N {x_iy_i}}{\sum_1^N {x_i^2}} \\
r_i = y_i - x_i\hat{\beta}... | 6971d0b863fc8c0b2200f7a0c7f9d1c49c7178ec | 9,373 | ipynb | Jupyter Notebook | chapter-03/3.2.3-multiple-regression-from-simple-multivariate-regression.ipynb | leduran/ESL | fcb6c8268d6a64962c013006d9298c6f5a7104fe | [
"MIT"
] | 360 | 2019-01-28T14:05:02.000Z | 2022-03-27T00:11:21.000Z | chapter-03/3.2.3-multiple-regression-from-simple-multivariate-regression.ipynb | leduran/ESL | fcb6c8268d6a64962c013006d9298c6f5a7104fe | [
"MIT"
] | 1 | 2020-07-06T16:51:40.000Z | 2020-07-06T16:51:40.000Z | chapter-03/3.2.3-multiple-regression-from-simple-multivariate-regression.ipynb | leduran/ESL | fcb6c8268d6a64962c013006d9298c6f5a7104fe | [
"MIT"
] | 79 | 2019-03-21T23:48:35.000Z | 2022-03-31T13:05:10.000Z | 36.613281 | 331 | 0.506881 | true | 2,403 | Qwen/Qwen-72B | 1. YES
2. YES | 0.935347 | 0.718594 | 0.672135 | __label__eng_Latn | 0.611044 | 0.399925 |
# Jastrow Factor Derivatives
This notebook calculates the expressions for the derivatives of the following Jastrow function:
$$J(\mathbf{X}) = \prod_{i < j} \exp\left(\frac{\alpha r_{ij}}{1 + \beta r_{ij}}\right)$$
with $N$ particles in $D$ dimensions, $\mathbf{X}\in\mathbb{R}^{N\times D}$ and $r_{ij} = ||\mathbf{X_... | 285d05512dd894b6710a103f9ac9aae7cdc0e3b6 | 62,338 | ipynb | Jupyter Notebook | scripts/Jastrow-Pade-sympy.ipynb | johanere/qflow | 5453cd5c3230ad7f082adf9ec1aea63ab0a4312a | [
"MIT"
] | 5 | 2019-07-24T21:46:24.000Z | 2021-06-11T18:18:24.000Z | scripts/Jastrow-Pade-sympy.ipynb | johanere/qflow | 5453cd5c3230ad7f082adf9ec1aea63ab0a4312a | [
"MIT"
] | 22 | 2019-02-19T10:49:26.000Z | 2019-07-18T09:42:13.000Z | scripts/Jastrow-Pade-sympy.ipynb | bsamseth/FYS4411 | 72b879e7978364498c48fc855b5df676c205f211 | [
"MIT"
] | 2 | 2019-04-24T06:44:33.000Z | 2019-06-12T20:34:38.000Z | 120.111753 | 10,652 | 0.665148 | true | 917 | Qwen/Qwen-72B | 1. YES
2. YES | 0.926304 | 0.800692 | 0.741684 | __label__eng_Latn | 0.188493 | 0.561512 |
```python
%matplotlib inline
import numpy as np
import pylab as pl
import sympy as sym
from sympy.functions import Abs
#from sympy import Abs, Symbol, S
```
## Goals of today:
- Check how good or bad are the estimates given in the theoretical lecture
- Compare Equispaced with Chebyshev
- Compute errors, plot error ta... | 2b20beffcc7b1b008c733555dd96e0eef1daf155 | 131,618 | ipynb | Jupyter Notebook | python-lectures/03_error_estimation_template.ipynb | denocris/Introduction-to-Numerical-Analysis | 45b40a7743e11457b644fc6a7de17a0854ece4f0 | [
"CC-BY-4.0"
] | 8 | 2018-01-16T15:59:48.000Z | 2022-03-31T09:29:31.000Z | python-lectures/03_error_estimation_template.ipynb | denocris/Introduction-to-Numerical-Analysis | 45b40a7743e11457b644fc6a7de17a0854ece4f0 | [
"CC-BY-4.0"
] | null | null | null | python-lectures/03_error_estimation_template.ipynb | denocris/Introduction-to-Numerical-Analysis | 45b40a7743e11457b644fc6a7de17a0854ece4f0 | [
"CC-BY-4.0"
] | 8 | 2018-01-21T16:45:34.000Z | 2021-06-25T15:56:27.000Z | 200.331811 | 28,140 | 0.880457 | true | 2,346 | Qwen/Qwen-72B | 1. YES
2. YES | 0.76908 | 0.90053 | 0.69258 | __label__eng_Latn | 0.864343 | 0.447426 |
## Computing partition function of the $2-$D Ising model using tensor network
The partition function of $2-$D ferromagnetic Ising model can be written as
$$Z(\beta)=\sum_\mathbf{s}\prod_{ij}e^{\beta s_is_j}=\mathbf{Tr}\left( \mathcal{A^{(1)}} \times \mathcal{A^{(2)}}\times\cdots\times \mathcal{A^{(L\times L)}}\right).$... | 63f4ea977463dc6711e0a81812102bf0b2061867 | 97,534 | ipynb | Jupyter Notebook | 2_tensor_network/tensor_contraction_simple.ipynb | Ben1008611/SSSS | ae2932da2096216032789144e95e353f8801d4e0 | [
"MIT"
] | 165 | 2019-03-28T08:46:17.000Z | 2022-03-20T11:09:52.000Z | 2_tensor_network/tensor_contraction_simple.ipynb | Ben1008611/SSSS | ae2932da2096216032789144e95e353f8801d4e0 | [
"MIT"
] | 2 | 2019-03-31T12:15:55.000Z | 2019-05-09T09:59:47.000Z | 2_tensor_network/tensor_contraction_simple.ipynb | Ben1008611/SSSS | ae2932da2096216032789144e95e353f8801d4e0 | [
"MIT"
] | 64 | 2019-04-22T14:41:07.000Z | 2022-03-03T13:25:09.000Z | 265.038043 | 36,152 | 0.908934 | true | 2,909 | Qwen/Qwen-72B | 1. YES
2. YES | 0.934395 | 0.803174 | 0.750482 | __label__eng_Latn | 0.454905 | 0.581952 |
# Controlling a system
**automated irrigation system**
* input: hydration level of soil
* desired reference: "kinda damp"
* disturbances: rain (do nothing until water evaporates); sun (output water); fauna
```python
# "magic" commands, prefaced with "%", changes settings in the notebook
# this ensures plot... | ce157e39e130844fa982bef2df9d33bfbaf0d7d6 | 26,791 | ipynb | Jupyter Notebook | tutorial3.ipynb | my-13/447 | aa3da2440b42ac9f28b5b7d2a2da0ef43404322f | [
"CC0-1.0"
] | null | null | null | tutorial3.ipynb | my-13/447 | aa3da2440b42ac9f28b5b7d2a2da0ef43404322f | [
"CC0-1.0"
] | null | null | null | tutorial3.ipynb | my-13/447 | aa3da2440b42ac9f28b5b7d2a2da0ef43404322f | [
"CC0-1.0"
] | null | null | null | 26,791 | 26,791 | 0.866037 | true | 1,510 | Qwen/Qwen-72B | 1. YES
2. YES | 0.845942 | 0.803174 | 0.679439 | __label__eng_Latn | 0.673378 | 0.416895 |
# Linear Algebra using SymPy
## Introduction
This notebook is a short tutorial of Linear Algebra calculation using SymPy. For further information refer to SymPy official [tutorial](http://docs.sympy.org/latest/tutorial/index.html).
You can also check the [SymPy in 10 minutes](./SymPy_in_10_minutes.ipynb) tutorial.
... | 503dab8963b7b367a390b5dbaecd26d88378b788 | 41,577 | ipynb | Jupyter Notebook | notebooks/sympy/linear_algebra.ipynb | nicoguaro/AdvancedMath | 2749068de442f67b89d3f57827367193ce61a09c | [
"MIT"
] | 26 | 2017-06-29T17:45:20.000Z | 2022-02-06T20:14:29.000Z | notebooks/sympy/linear_algebra.ipynb | nicoguaro/AdvancedMath | 2749068de442f67b89d3f57827367193ce61a09c | [
"MIT"
] | null | null | null | notebooks/sympy/linear_algebra.ipynb | nicoguaro/AdvancedMath | 2749068de442f67b89d3f57827367193ce61a09c | [
"MIT"
] | 13 | 2019-04-22T08:08:56.000Z | 2022-01-27T08:15:53.000Z | 31.593465 | 2,346 | 0.516247 | true | 4,632 | Qwen/Qwen-72B | 1. YES
2. YES | 0.941654 | 0.90053 | 0.847988 | __label__eng_Latn | 0.888082 | 0.808493 |
## Quantum circuit for an exponential of pauli strings
For SUSY QM, the Hamiltonian, $H$ can be qubitized, which results in the Hamiltonian being written as a sum of terms, with each term containing a produce of pauli matrices acting on the qubits. Given some initial state, we can apply the time evolution operator,
... | 81a404ffcd1719bd4ea01cecc643e5f1576ba02a | 39,178 | ipynb | Jupyter Notebook | tutorials/LadderCircuits.ipynb | daschaich/SUSY_QuantumComputing | fdf2b50c2e80a1bd5d1ebdf36629dfdd0aaf69aa | [
"MIT"
] | null | null | null | tutorials/LadderCircuits.ipynb | daschaich/SUSY_QuantumComputing | fdf2b50c2e80a1bd5d1ebdf36629dfdd0aaf69aa | [
"MIT"
] | null | null | null | tutorials/LadderCircuits.ipynb | daschaich/SUSY_QuantumComputing | fdf2b50c2e80a1bd5d1ebdf36629dfdd0aaf69aa | [
"MIT"
] | null | null | null | 135.564014 | 11,992 | 0.859462 | true | 1,215 | Qwen/Qwen-72B | 1. YES
2. YES | 0.944995 | 0.857768 | 0.810586 | __label__eng_Latn | 0.736635 | 0.721596 |
```python
from sympy import pi, cos, sin, symbols
from sympy.utilities.lambdify import implemented_function
import pytest
from sympde.calculus import grad, dot
from sympde.calculus import laplace
from sympde.topology import ScalarFunctionSpace
from sympde.topology import element_of
from sympde.topology import NormalVe... | 4a662005b40e142c7746bb89a9886c3fba699548 | 34,575 | ipynb | Jupyter Notebook | lessons/Chapter3/01_nonlinear_poisson_2d.ipynb | pyccel/IGA-Python | e3604ba3d76a20e3d30ed3c7c952dcd2dc8147bb | [
"MIT"
] | 2 | 2022-01-21T08:51:30.000Z | 2022-03-17T12:14:02.000Z | lessons/Chapter3/01_nonlinear_poisson_2d.ipynb | pyccel/IGA-Python | e3604ba3d76a20e3d30ed3c7c952dcd2dc8147bb | [
"MIT"
] | null | null | null | lessons/Chapter3/01_nonlinear_poisson_2d.ipynb | pyccel/IGA-Python | e3604ba3d76a20e3d30ed3c7c952dcd2dc8147bb | [
"MIT"
] | 1 | 2022-03-01T06:41:54.000Z | 2022-03-01T06:41:54.000Z | 62.297297 | 10,964 | 0.799103 | true | 2,080 | Qwen/Qwen-72B | 1. YES
2. YES | 0.890294 | 0.672332 | 0.598573 | __label__eng_Latn | 0.515405 | 0.229016 |
# Resolución de sistemas de ecuaciones lineales
Juan Pablo Echeagaray González
Data Science Club Academy
27 de septiembre del 2021
## Librerías básicas
Siempre vale la pena tener estas 2 librerías a la mano, numpy suele ser mucho más eficaz que las funciones nativas de python en cuanto a operaciones matemá... | 18684edb8feb7ea0e63f8c08f6684f5d6be38982 | 17,179 | ipynb | Jupyter Notebook | Linear Algebra/lin_eq.ipynb | JuanEcheagaray75/DSC-scripts | f38ebcf274234fd969e0fb153ae5e756509bf1c3 | [
"Apache-2.0"
] | null | null | null | Linear Algebra/lin_eq.ipynb | JuanEcheagaray75/DSC-scripts | f38ebcf274234fd969e0fb153ae5e756509bf1c3 | [
"Apache-2.0"
] | null | null | null | Linear Algebra/lin_eq.ipynb | JuanEcheagaray75/DSC-scripts | f38ebcf274234fd969e0fb153ae5e756509bf1c3 | [
"Apache-2.0"
] | null | null | null | 60.066434 | 1,898 | 0.640841 | true | 1,070 | Qwen/Qwen-72B | 1. YES
2. YES | 0.917303 | 0.853913 | 0.783296 | __label__spa_Latn | 0.961378 | 0.658193 |
```python
%matplotlib inline
from typing import List
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import scipy.linalg
import scipy.ndimage
import scipy.optimize
import scipy.special
import sklearn.datasets
from chmp.ds import mpl_set, get_color_cycle, Loop
```
```python
# helper for gradi... | bc628b3ca3179ad86810259aa7ead89ff3856cc2 | 101,661 | ipynb | Jupyter Notebook | BuildingBlocks/Bishop_Notes_04.ipynb | chmp/misc-exp | 2edc2ed598eb59f4ccb426e7a5c1a23343a6974b | [
"MIT"
] | 6 | 2017-10-31T20:54:37.000Z | 2020-10-23T19:03:00.000Z | BuildingBlocks/Bishop_Notes_04.ipynb | chmp/misc-exp | 2edc2ed598eb59f4ccb426e7a5c1a23343a6974b | [
"MIT"
] | 7 | 2020-03-24T16:14:34.000Z | 2021-03-18T20:51:37.000Z | BuildingBlocks/Bishop_Notes_04.ipynb | chmp/misc-exp | 2edc2ed598eb59f4ccb426e7a5c1a23343a6974b | [
"MIT"
] | 1 | 2019-07-29T07:55:49.000Z | 2019-07-29T07:55:49.000Z | 121.604067 | 32,472 | 0.856749 | true | 3,949 | Qwen/Qwen-72B | 1. YES
2. YES | 0.867036 | 0.853913 | 0.740373 | __label__eng_Latn | 0.383927 | 0.558466 |
| |Pierre Proulx, ing, professeur|
|:---|:---|
|Département de génie chimique et de génie biotechnologique |** GCH200-Phénomènes d'échanges I **|
#### Détails de la transformation d'équation partielle en équation différentielle ordinaire.
#### Pour commencer la partie b), je fais calculer les dérivées qui me servir... | f771da70ae4c1e99d487d6903ad3cf16bdb375a2 | 62,772 | ipynb | Jupyter Notebook | Chap-18-Section-18-5-details.ipynb | pierreproulx/GCH200 | 66786aa96ceb2124b96c93ee3d928a295f8e9a03 | [
"MIT"
] | 1 | 2018-02-26T16:29:58.000Z | 2018-02-26T16:29:58.000Z | Chap-18-Section-18-5-details.ipynb | pierreproulx/GCH200 | 66786aa96ceb2124b96c93ee3d928a295f8e9a03 | [
"MIT"
] | null | null | null | Chap-18-Section-18-5-details.ipynb | pierreproulx/GCH200 | 66786aa96ceb2124b96c93ee3d928a295f8e9a03 | [
"MIT"
] | 2 | 2018-02-27T15:04:33.000Z | 2021-06-03T16:38:07.000Z | 128.895277 | 14,104 | 0.83507 | true | 893 | Qwen/Qwen-72B | 1. YES
2. YES | 0.884039 | 0.833325 | 0.736692 | __label__fra_Latn | 0.258472 | 0.549913 |
# Catching Heuristics Are Robust to Systematic Disturbances and Can Be Found by Reinforcement Learning
## Supplementary Material: Proofs for Chapman's Strategy (Section 4)
This material accompanies my doctoral thesis *On Decomposability in Robot Reinforcement Learning* and the paper *Catching Heuristics Are Robust t... | 82ed02660602a05cc692669e411e789521201bb0 | 405,819 | ipynb | Jupyter Notebook | notebook/proofs-chapman.ipynb | shoefer/ball_catching | 46b2e95894659347b563123c1c23742437755993 | [
"MIT"
] | 1 | 2017-07-22T11:36:02.000Z | 2017-07-22T11:36:02.000Z | notebook/proofs-chapman.ipynb | shoefer/ball_catching | 46b2e95894659347b563123c1c23742437755993 | [
"MIT"
] | null | null | null | notebook/proofs-chapman.ipynb | shoefer/ball_catching | 46b2e95894659347b563123c1c23742437755993 | [
"MIT"
] | null | null | null | 177.834794 | 115,164 | 0.876854 | true | 10,582 | Qwen/Qwen-72B | 1. YES
2. YES | 0.672332 | 0.79053 | 0.531499 | __label__eng_Latn | 0.734012 | 0.073179 |
## Histograms of Oriented Gradients (HOG)
As we saw with the ORB algorithm, we can use keypoints in images to do keypoint-based matching to detect objects in images. These type of algorithms work great when you want to detect objects that have a lot of consistent internal features that are not affected by the backgrou... | ee9fedb137f4ad61d044671174af88869c357eaa | 368,002 | ipynb | Jupyter Notebook | 1_4_Feature_Vectors/3_1. HOG.ipynb | mariabardon/nanodegree_computer_vision | 03cc7cdc1fba65732f8b2a1f7eaca62f71c24f4c | [
"MIT"
] | null | null | null | 1_4_Feature_Vectors/3_1. HOG.ipynb | mariabardon/nanodegree_computer_vision | 03cc7cdc1fba65732f8b2a1f7eaca62f71c24f4c | [
"MIT"
] | null | null | null | 1_4_Feature_Vectors/3_1. HOG.ipynb | mariabardon/nanodegree_computer_vision | 03cc7cdc1fba65732f8b2a1f7eaca62f71c24f4c | [
"MIT"
] | null | null | null | 250.001359 | 292,399 | 0.885316 | true | 7,815 | Qwen/Qwen-72B | 1. YES
2. YES
| 0.76908 | 0.757794 | 0.582805 | __label__eng_Latn | 0.997504 | 0.192381 |
# Exploring Data with Python
A significant part of a data scientist's role is to explore, analyze, and visualize data. There's a wide range of tools and programming languages that they can use to do this, and of the most popular approaches is to use Jupyter notebooks (like this one) and Python.
Python is a flexible p... | ff9eede1be89672ea3a1ab75e75cfcef8058f747 | 1,032,858 | ipynb | Jupyter Notebook | 01 - Data Exploration.ipynb | TJ156TJ/ML_Basics | 5799c3f6ccee36582b5a3df34ce1b40d17b653d8 | [
"MIT"
] | null | null | null | 01 - Data Exploration.ipynb | TJ156TJ/ML_Basics | 5799c3f6ccee36582b5a3df34ce1b40d17b653d8 | [
"MIT"
] | null | null | null | 01 - Data Exploration.ipynb | TJ156TJ/ML_Basics | 5799c3f6ccee36582b5a3df34ce1b40d17b653d8 | [
"MIT"
] | null | null | null | 405.360283 | 48,493 | 0.693454 | true | 24,285 | Qwen/Qwen-72B | 1. YES
2. YES | 0.835484 | 0.785309 | 0.656112 | __label__eng_Latn | 0.964723 | 0.3627 |
# Map, Filter, Reduce, and Groupby
本部分展示高阶函数应用
```python
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
```
```python
def square(x):
return x ** 2
def iseven(n):
return n % 2 == 0
def add(x, y):
return x + y
def mul(x, y):
return x * y
def lesser(x, y):
if x < y:
return x
else:
... | f00001392cf3c6c4c3fc326088d3b28ac887d6b6 | 19,350 | ipynb | Jupyter Notebook | 1-map-filter-reduce-groupby.ipynb | PyDriven/pydata-toolz | 3fc09b93a1aa5e4e0807b8ec2d1f6d0716c8cfde | [
"MIT"
] | null | null | null | 1-map-filter-reduce-groupby.ipynb | PyDriven/pydata-toolz | 3fc09b93a1aa5e4e0807b8ec2d1f6d0716c8cfde | [
"MIT"
] | null | null | null | 1-map-filter-reduce-groupby.ipynb | PyDriven/pydata-toolz | 3fc09b93a1aa5e4e0807b8ec2d1f6d0716c8cfde | [
"MIT"
] | null | null | null | 20.093458 | 270 | 0.471628 | true | 2,713 | Qwen/Qwen-72B | 1. YES
2. YES | 0.803174 | 0.861538 | 0.691965 | __label__eng_Latn | 0.326098 | 0.445998 |
```python
import numpy as np
import matplotlib.pyplot as plt
from scipy import sparse, fftpack
from math import factorial as fact
try:
plt.style.use("jupyter")
except OSerror:
print("Using default ploting style")
# L-p norm function
norm = lambda v, p=2 : (len(v)**(-p)*np.einsum('i->', np.abs(v)**2))**(1./p)
``... | 026d2d26706625776cffd939d36d6d4a374a629a | 114,203 | ipynb | Jupyter Notebook | docs/notebooks/Compact-Schemes-for-Poisson-Equation.ipynb | marinlauber/marinlauber.github.io | 851f421c788152e2809b77b907c75bc0bada974d | [
"MIT"
] | 1 | 2020-12-16T09:18:39.000Z | 2020-12-16T09:18:39.000Z | docs/notebooks/Compact-Schemes-for-Poisson-Equation.ipynb | marinlauber/marinlauber.github.io | 851f421c788152e2809b77b907c75bc0bada974d | [
"MIT"
] | null | null | null | docs/notebooks/Compact-Schemes-for-Poisson-Equation.ipynb | marinlauber/marinlauber.github.io | 851f421c788152e2809b77b907c75bc0bada974d | [
"MIT"
] | 1 | 2020-12-16T09:18:56.000Z | 2020-12-16T09:18:56.000Z | 189.077815 | 32,824 | 0.879408 | true | 4,875 | Qwen/Qwen-72B | 1. YES
2. YES | 0.931463 | 0.877477 | 0.817337 | __label__eng_Latn | 0.50535 | 0.73728 |
# Trying to understand what metrics where used
```python
! pip install sympy
```
Collecting sympy
Using cached https://files.pythonhosted.org/packages/dd/f6/ed485ff22efdd7b371d0dbbf6d77ad61c3b3b7e0815a83c89cbb38ce35de/sympy-1.3.tar.gz
Collecting mpmath>=0.19 (from sympy)
[?25l Downloading https:/... | 5dafa9c946f115be4c012768032056f90ae7fdfd | 23,532 | ipynb | Jupyter Notebook | code/ynacc/09 Replicate Reported Baseline/Replicate Metrics.ipynb | jfilter/masters-thesis | 39a3d9b862444507982cc4ccd98b6809cab72d82 | [
"MIT"
] | 5 | 2019-04-24T19:45:07.000Z | 2020-12-29T06:40:58.000Z | code/ynacc/09 Replicate Reported Baseline/Replicate Metrics.ipynb | jfilter/masters-thesis | 39a3d9b862444507982cc4ccd98b6809cab72d82 | [
"MIT"
] | 2 | 2019-11-05T17:17:38.000Z | 2019-11-05T17:17:39.000Z | code/ynacc/09 Replicate Reported Baseline/Replicate Metrics.ipynb | jfilter/masters-thesis | 39a3d9b862444507982cc4ccd98b6809cab72d82 | [
"MIT"
] | null | null | null | 26.893714 | 385 | 0.517763 | true | 5,095 | Qwen/Qwen-72B | 1. YES
2. YES | 0.874077 | 0.79053 | 0.690985 | __label__yue_Hant | 0.090693 | 0.44372 |
```
import sympy as sy
from dolfin import *
```
```
```
```
```
```
```
```
x = sy.symbols('x')
y = sy.symbols('y')
G = 10.
Ha = 0.01
nu = 1.
kappa = 1e4
```
```
```
```
sy.ccode
```
```
b = G/kappa*(sy.sinh(y*Ha)/sy.sinh(Ha)-y)
d = 1
p = -G*x - (kappa/2)*b**2
u = G/(nu*Ha*sy.tanh(Ha))*(1-sy.cosh(y*Ha... | 44bea0be23ff60105cada96d6c85ddca41511fda | 27,038 | ipynb | Jupyter Notebook | MHD/FEniCS/Classes/Hartman2D/Untitled1.ipynb | wathen/PhD | 35524f40028541a4d611d8c78574e4cf9ddc3278 | [
"MIT"
] | 3 | 2020-10-25T13:30:20.000Z | 2021-08-10T21:27:30.000Z | MHD/FEniCS/Classes/Hartman2D/Untitled1.ipynb | wathen/PhD | 35524f40028541a4d611d8c78574e4cf9ddc3278 | [
"MIT"
] | null | null | null | MHD/FEniCS/Classes/Hartman2D/Untitled1.ipynb | wathen/PhD | 35524f40028541a4d611d8c78574e4cf9ddc3278 | [
"MIT"
] | 3 | 2019-10-28T16:12:13.000Z | 2020-01-13T13:59:44.000Z | 38.189266 | 2,005 | 0.565168 | true | 844 | Qwen/Qwen-72B | 1. YES
2. YES | 0.933431 | 0.654895 | 0.611299 | __label__yue_Hant | 0.128006 | 0.258583 |
```python
import matplotlib.pyplot as plt
import scipy.stats as st
import seaborn as sns
import pandas as pd
from scipy.stats import norm, uniform, expon
from scipy.integrate import quad
from sympy.solvers import solve
from sympy import Symbol
import numpy as np
from pandas import Series, DataFrame
```
```python
wert... | d5558c9c47613f2ec460bfb789d1e9f74c2f5868 | 9,089 | ipynb | Jupyter Notebook | Lernphase/SW04/.ipynb_checkpoints/Skript-checkpoint.ipynb | florianbaer/STAT | 7cb86406ed99b88055c92c1913b46e8995835cbb | [
"MIT"
] | null | null | null | Lernphase/SW04/.ipynb_checkpoints/Skript-checkpoint.ipynb | florianbaer/STAT | 7cb86406ed99b88055c92c1913b46e8995835cbb | [
"MIT"
] | null | null | null | Lernphase/SW04/.ipynb_checkpoints/Skript-checkpoint.ipynb | florianbaer/STAT | 7cb86406ed99b88055c92c1913b46e8995835cbb | [
"MIT"
] | null | null | null | 53.152047 | 5,876 | 0.795907 | true | 245 | Qwen/Qwen-72B | 1. YES
2. YES | 0.901921 | 0.689306 | 0.621699 | __label__eng_Latn | 0.492787 | 0.282746 |
# Two Dimensional Fractal Integration
To generalize the concepts given in the previous section for FIFs to the FISs, consider that $\Omega = \{\tilde{P}_j = (\tilde{x}_j, \tilde{y}_j), \; j = 1, 2, 3\}$ is a triangular domain in the plane. Let $P = \{P_i = (x_i, y_i), \; i = 1, \ldots, N\}$ be given points in the pla... | c46526fbcd151448f548b2967551aeaa72d0e7fa | 877,385 | ipynb | Jupyter Notebook | notebooks/stash/.ipynb_checkpoints/two_dimensional_interpolation-checkpoint.ipynb | zekeriyasari/FractalTools.jl | 9896b88d30b3a22e1f808f812ce60d23d2a27013 | [
"MIT"
] | 3 | 2020-09-08T12:20:52.000Z | 2021-03-26T12:50:16.000Z | notebooks/stash/.ipynb_checkpoints/two_dimensional_interpolation-checkpoint.ipynb | zekeriyasari/FractalTools.jl | 9896b88d30b3a22e1f808f812ce60d23d2a27013 | [
"MIT"
] | 30 | 2020-09-05T18:22:43.000Z | 2021-07-26T10:09:46.000Z | notebooks/stash/.ipynb_checkpoints/two_dimensional_interpolation-checkpoint.ipynb | zekeriyasari/FractalTools.jl | 9896b88d30b3a22e1f808f812ce60d23d2a27013 | [
"MIT"
] | null | null | null | 2,232.531807 | 216,838 | 0.95964 | true | 3,765 | Qwen/Qwen-72B | 1. YES
2. YES | 0.908618 | 0.749087 | 0.680634 | __label__eng_Latn | 0.605301 | 0.419672 |
# Particle in one-dimensional potential well
## Period of oscillations in potential well
Dynamics of a particle of mass $m$ moving in one dimension $OX$ is described the Newton equation
$$m\ddot x =m\dot v = F(x) = -U'(x),$$
where $F(x)$ is a force acting on tha particle and $U(x)$ is potential energy of the p... | 7f7f755c9b262e1286dd6f7e81e491b365331074 | 26,100 | ipynb | Jupyter Notebook | 012-1d_potential_well.ipynb | marcinofulus/Mechanics_with_SageMath | 6d13cb2e83cd4be063c9cfef6ce536564a25cf57 | [
"MIT"
] | null | null | null | 012-1d_potential_well.ipynb | marcinofulus/Mechanics_with_SageMath | 6d13cb2e83cd4be063c9cfef6ce536564a25cf57 | [
"MIT"
] | 1 | 2022-01-30T16:45:58.000Z | 2022-01-30T16:45:58.000Z | 012-1d_potential_well.ipynb | marcinofulus/Mechanics_with_SageMath | 6d13cb2e83cd4be063c9cfef6ce536564a25cf57 | [
"MIT"
] | 3 | 2020-11-15T08:26:14.000Z | 2022-02-12T13:07:16.000Z | 25.996016 | 578 | 0.52046 | true | 4,766 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91118 | 0.923039 | 0.841055 | __label__eng_Latn | 0.973566 | 0.792385 |
# Random Signals and LTI-Systems
*This jupyter notebook is part of a [collection of notebooks](../index.ipynb) on various topics of Digital Signal Processing. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*
## Auto-Correlation Function
The auto-correlati... | c0e136f1573e1e39a9173419dccfdc7ad053e73b | 383,520 | ipynb | Jupyter Notebook | random_signals_LTI_systems/correlation_functions.ipynb | TA1DB/digital-signal-processing-lecture | fc2219d9ab2217ce96c59e6e8be1f1e270bae08d | [
"MIT"
] | 630 | 2016-01-05T17:11:43.000Z | 2022-03-30T07:48:27.000Z | random_signals_LTI_systems/correlation_functions.ipynb | SeunghyunOh-Daniel/digital-signal-processing-lecture | eea6f46284a903297452d2c6fc489cb4d26a4a54 | [
"MIT"
] | 12 | 2016-11-07T15:49:55.000Z | 2022-03-10T13:05:50.000Z | random_signals_LTI_systems/correlation_functions.ipynb | SeunghyunOh-Daniel/digital-signal-processing-lecture | eea6f46284a903297452d2c6fc489cb4d26a4a54 | [
"MIT"
] | 172 | 2015-12-26T21:05:40.000Z | 2022-03-10T23:13:30.000Z | 67.628284 | 22,654 | 0.62402 | true | 2,851 | Qwen/Qwen-72B | 1. YES
2. YES | 0.715424 | 0.839734 | 0.600766 | __label__eng_Latn | 0.956317 | 0.234111 |
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