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# Gaussian Process Distribution of Relaxation Times
## In this tutorial we will reproduce Figure 7 of the article https://doi.org/10.1016/j.electacta.2019.135316
GP-DRT is our newly developed approach that can be used to obtain both the mean and covariance of the DRT from EIS data by assuming that the DRT is a Gaussi... | eb88848b4b82d459b5b2abdd47eafd99aa36c1c9 | 89,966 | ipynb | Jupyter Notebook | tutorials/ex1_single_ZARC.ipynb | jiapeng-liu/GP-DRT | 1bd8232dc07b84293ea6960a669f04e5a875d982 | [
"MIT"
] | 21 | 2019-11-26T16:58:01.000Z | 2022-02-23T10:27:07.000Z | tutorials/ex1_single_ZARC.ipynb | jiapeng-liu/GP-DRT | 1bd8232dc07b84293ea6960a669f04e5a875d982 | [
"MIT"
] | 1 | 2021-02-07T17:10:35.000Z | 2021-08-06T04:20:22.000Z | tutorials/ex1_single_ZARC.ipynb | jiapeng-liu/GP-DRT | 1bd8232dc07b84293ea6960a669f04e5a875d982 | [
"MIT"
] | 11 | 2019-11-27T02:38:39.000Z | 2022-03-18T08:17:47.000Z | 174.352713 | 29,556 | 0.871007 | true | 5,117 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91848 | 0.760651 | 0.698643 | __label__eng_Latn | 0.606891 | 0.461512 |
# Algebra Lineal con Python
## Introducción
Una de las herramientas matemáticas más utilizadas en [machine learning](http://es.wikipedia.org/wiki/Machine_learning) y [data mining](http://es.wikipedia.org/wiki/Miner%C3%ADa_de_datos) es el [Álgebra lineal](http://es.wikipedia.org/wiki/%C3%81lgebra_lineal); por tanto,... | 523a24404f34f580ac69f5f36ca14bb6385fd52c | 260,768 | ipynb | Jupyter Notebook | 2-EDA/4-Mates para DS/Algebra_Lineal.ipynb | erfederuiz/thebridge_ft_nov21 | 00f7216024ac0cf05e564eb8b1be6e888f277ea4 | [
"MIT"
] | null | null | null | 2-EDA/4-Mates para DS/Algebra_Lineal.ipynb | erfederuiz/thebridge_ft_nov21 | 00f7216024ac0cf05e564eb8b1be6e888f277ea4 | [
"MIT"
] | null | null | null | 2-EDA/4-Mates para DS/Algebra_Lineal.ipynb | erfederuiz/thebridge_ft_nov21 | 00f7216024ac0cf05e564eb8b1be6e888f277ea4 | [
"MIT"
] | null | null | null | 59.932889 | 15,666 | 0.733687 | true | 28,379 | Qwen/Qwen-72B | 1. YES
2. YES | 0.890294 | 0.907312 | 0.807775 | __label__spa_Latn | 0.810399 | 0.715064 |
# Modeling Magnetic Anomalies
In this section we discuss how to solve PDE for magnetic field anomalies due to a variation of
the magnetic susceptibility in the subsurface using `esys.escript`. It is assumed that you have
worked through the [introduction section on `esys.escript`](escriptBasics.ipynb).
First we will ... | 1a76782065536ca09defdd1fbe28c2f06500c859 | 247,010 | ipynb | Jupyter Notebook | B_GeophyicalModeling/Magnetic.ipynb | uqzzhao/Programming-Geophysics-in-Python | e6e8299116b4698892921b78927b71fc47ee018a | [
"Apache-2.0"
] | 20 | 2019-11-06T09:08:54.000Z | 2021-12-03T08:37:47.000Z | B_GeophyicalModeling/Magnetic.ipynb | uqzzhao/Programming-Geophysics-in-Python | e6e8299116b4698892921b78927b71fc47ee018a | [
"Apache-2.0"
] | null | null | null | B_GeophyicalModeling/Magnetic.ipynb | uqzzhao/Programming-Geophysics-in-Python | e6e8299116b4698892921b78927b71fc47ee018a | [
"Apache-2.0"
] | 3 | 2020-11-23T14:16:06.000Z | 2022-03-31T14:45:46.000Z | 64.191788 | 39,619 | 0.655556 | true | 3,043 | Qwen/Qwen-72B | 1. YES
2. YES | 0.79053 | 0.682574 | 0.539595 | __label__eng_Latn | 0.984309 | 0.09199 |
| |Pierre Proulx, ing, professeur|
|:---|:---|
|Département de génie chimique et de génie biotechnologique |** GCH200-Phénomènes d'échanges I **|
## Exemple 7-6.1
#### Solution
##### Bilan de masse
$\begin{equation*}
\boxed{\rho v_1 S_1 - \rho v_2 S_2 = 0}
\end{equation*}$ (1)
##### Bilan de quantité de mou... | 7ac35466b84d4c38015ff298b04213097502ec7f | 3,618 | ipynb | Jupyter Notebook | Chap-7-ex-7-6-1.ipynb | pierreproulx/GCH200 | 66786aa96ceb2124b96c93ee3d928a295f8e9a03 | [
"MIT"
] | 1 | 2018-02-26T16:29:58.000Z | 2018-02-26T16:29:58.000Z | Chap-7-ex-7-6-1.ipynb | pierreproulx/GCH200 | 66786aa96ceb2124b96c93ee3d928a295f8e9a03 | [
"MIT"
] | null | null | null | Chap-7-ex-7-6-1.ipynb | pierreproulx/GCH200 | 66786aa96ceb2124b96c93ee3d928a295f8e9a03 | [
"MIT"
] | 2 | 2018-02-27T15:04:33.000Z | 2021-06-03T16:38:07.000Z | 30.403361 | 318 | 0.528745 | true | 848 | Qwen/Qwen-72B | 1. YES
2. YES | 0.867036 | 0.782662 | 0.678596 | __label__fra_Latn | 0.479789 | 0.414938 |
# Solwing the Solow model with R&D
## Intro
In this project we will try to solve the solow model with R&D and furthermore extend the model by including human capital.
Our goal will be to dig into the following aspects:
* Finding the steady state rates by solveing for several transition equation
* Finding numeric va... | 74c2486c14e7e2e739875daafd734b038a3851d0 | 76,354 | ipynb | Jupyter Notebook | modelproject/model_project.ipynb | NumEconCopenhagen/projects-2019-pickles | 7f4d66612bf8f575b745a2c1c32477938e3dcbb6 | [
"MIT"
] | null | null | null | modelproject/model_project.ipynb | NumEconCopenhagen/projects-2019-pickles | 7f4d66612bf8f575b745a2c1c32477938e3dcbb6 | [
"MIT"
] | 13 | 2019-04-08T17:31:57.000Z | 2019-05-14T18:47:13.000Z | modelproject/model_project.ipynb | NumEconCopenhagen/projects-2019-pickles | 7f4d66612bf8f575b745a2c1c32477938e3dcbb6 | [
"MIT"
] | 2 | 2019-05-14T08:10:26.000Z | 2019-12-09T09:29:09.000Z | 63.154673 | 8,832 | 0.665571 | true | 6,233 | Qwen/Qwen-72B | 1. YES
2. YES | 0.841826 | 0.859664 | 0.723687 | __label__eng_Latn | 0.931846 | 0.519699 |
# the German tank problem
the Germans have a population of $n$ tanks labeled with serial numbers $1,2,...,n$ on the tanks. The number of tanks $n$ is unknown and of interest to the Allied forces. The Allied forces randomly capture $k$ tanks from the Germans with replacement and observe their serial numbers $\{x_1, x_2... | a6f97a896d5a264829a39d1db70792df9ef864d5 | 85,428 | ipynb | Jupyter Notebook | In-Class Notes/German Tank Problem/German tank problem_sparse.ipynb | cartemic/CHE-599-intro-to-data-science | a2afe72b51a3b9e844de94d59961bedc3534a405 | [
"MIT"
] | null | null | null | In-Class Notes/German Tank Problem/German tank problem_sparse.ipynb | cartemic/CHE-599-intro-to-data-science | a2afe72b51a3b9e844de94d59961bedc3534a405 | [
"MIT"
] | null | null | null | In-Class Notes/German Tank Problem/German tank problem_sparse.ipynb | cartemic/CHE-599-intro-to-data-science | a2afe72b51a3b9e844de94d59961bedc3534a405 | [
"MIT"
] | 2 | 2019-10-02T16:11:36.000Z | 2019-10-15T20:10:40.000Z | 159.380597 | 53,726 | 0.894426 | true | 1,695 | Qwen/Qwen-72B | 1. YES
2. YES | 0.913677 | 0.867036 | 0.79219 | __label__eng_Latn | 0.965016 | 0.678856 |
# Кластерный анализ
## Метод к-средних
Дана матрица данных $X$ и дано число $k$ предполагаемых кластеров. Цель кластеризации представить данные в виде групп кластеров $C=\{C_1, C_2, \ldots, C_k\}$. Каждый кластер имеет свой центр:
\begin{equation}
\mu_i = \frac{1}{n_i} \sum \limits_{x_j \in C_i} x_j
\end{equation}
... | b03c91f3ad2e1a72afd558af796601be1e215645 | 169,524 | ipynb | Jupyter Notebook | DA-LR6-Kabanov.ipynb | ghspbravo/Data-Analysis | 25a0102378bf73bfd775ab15a2ad64fcd658ad73 | [
"MIT"
] | null | null | null | DA-LR6-Kabanov.ipynb | ghspbravo/Data-Analysis | 25a0102378bf73bfd775ab15a2ad64fcd658ad73 | [
"MIT"
] | null | null | null | DA-LR6-Kabanov.ipynb | ghspbravo/Data-Analysis | 25a0102378bf73bfd775ab15a2ad64fcd658ad73 | [
"MIT"
] | null | null | null | 117.317647 | 23,816 | 0.815047 | true | 10,291 | Qwen/Qwen-72B | 1. YES
2. YES | 0.932453 | 0.815232 | 0.760166 | __label__kor_Hang | 0.079642 | 0.604453 |
# Physics 420/580 Midterm Exam
## October 19, 2017 1pm-2pm
Do the following problems. Use the Jupyter notebook, inserting your code and any textual answers/explanations in cells between the questions. (Feel free to add additional cells!) Marks will be given based on how clearly you demonstrate your understanding. ... | 56722069cd1d6e945bac1f368991e53a8823f819 | 298,937 | ipynb | Jupyter Notebook | Assignments/Finished/.ipynb_checkpoints/Midterm 2018 (1)-checkpoint.ipynb | hanzhihua72/phys-420 | 748d29b55d57680212b15bb70879a24b79cb16a9 | [
"MIT"
] | null | null | null | Assignments/Finished/.ipynb_checkpoints/Midterm 2018 (1)-checkpoint.ipynb | hanzhihua72/phys-420 | 748d29b55d57680212b15bb70879a24b79cb16a9 | [
"MIT"
] | null | null | null | Assignments/Finished/.ipynb_checkpoints/Midterm 2018 (1)-checkpoint.ipynb | hanzhihua72/phys-420 | 748d29b55d57680212b15bb70879a24b79cb16a9 | [
"MIT"
] | null | null | null | 406.164402 | 90,132 | 0.936428 | true | 3,522 | Qwen/Qwen-72B | 1. YES
2. YES | 0.831143 | 0.901921 | 0.749625 | __label__eng_Latn | 0.856859 | 0.579962 |
# Sweep Signals and their Spectra
*This Jupyter notebook is part of a [collection of notebooks](../index.ipynb) in the masters module Selected Topics in Audio Signal Processing, Communications Engineering, Universität Rostock. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors... | fde5143298e1d254127b57b95b908e62597dbbb9 | 491,829 | ipynb | Jupyter Notebook | electroacoustics/sweep_spectrum.ipynb | spatialaudio/-selected-topics-in-audio-signal-processing-lecture- | d56c54401ad15f72042baeba88a22809c6c9f85c | [
"MIT"
] | 14 | 2017-10-19T14:54:02.000Z | 2021-12-30T12:39:02.000Z | electroacoustics/sweep_spectrum.ipynb | spatialaudio/-selected-topics-in-audio-signal-processing-lecture- | d56c54401ad15f72042baeba88a22809c6c9f85c | [
"MIT"
] | null | null | null | electroacoustics/sweep_spectrum.ipynb | spatialaudio/-selected-topics-in-audio-signal-processing-lecture- | d56c54401ad15f72042baeba88a22809c6c9f85c | [
"MIT"
] | null | null | null | 1,083.323789 | 146,024 | 0.957579 | true | 1,672 | Qwen/Qwen-72B | 1. YES
2. YES | 0.941654 | 0.884039 | 0.832459 | __label__eng_Latn | 0.966119 | 0.772415 |
<b>Construir o gráfico e encontrar o foco e uma equação da diretriz.</b>
<b>4. $x^2 + y = 0$</b>
<b>Arrumando a equação</b><br><br>
$x^2 = -y$<br><br>
$2p = -1$,<b>logo</b><br><br>
$p = -\frac{1}{2}$<br><br><br>
<b>Calculando o foco</b><br><br>
$F = -\frac{p}{2}$<br><br>
$F = \frac{-\frac{1}{2}}{2}$<br><br>
$F = -\fr... | b25aa0622b84fb0a6385a733ac4343d9173e4313 | 12,111 | ipynb | Jupyter Notebook | Problemas Propostos. Pag. 172 - 175/04.ipynb | mateuschaves/GEOMETRIA-ANALITICA | bc47ece7ebab154e2894226c6d939b7e7f332878 | [
"MIT"
] | 1 | 2020-02-03T16:40:45.000Z | 2020-02-03T16:40:45.000Z | Problemas Propostos. Pag. 172 - 175/04.ipynb | mateuschaves/GEOMETRIA-ANALITICA | bc47ece7ebab154e2894226c6d939b7e7f332878 | [
"MIT"
] | null | null | null | Problemas Propostos. Pag. 172 - 175/04.ipynb | mateuschaves/GEOMETRIA-ANALITICA | bc47ece7ebab154e2894226c6d939b7e7f332878 | [
"MIT"
] | null | null | null | 130.225806 | 10,060 | 0.878623 | true | 393 | Qwen/Qwen-72B | 1. YES
2. YES | 0.896251 | 0.800692 | 0.717621 | __label__por_Latn | 0.361745 | 0.505606 |
# GPS
Based on D. Kalman's method, I expand the four constraint equations as following:
$
\begin{align}
2.4x + 4.6y + 0.4z - 2(0.047^2)9.9999 t &= 1.2^2 + 2.3^2 + 0.2^2 + x^2 + y^2 + z^2 - 0.047^2 t^2 \\
-1x + 3y + 3.6z - 2(0.047^2)13.0681 t &= 0.5^2 + 1.5^2 + 1.8^2 + x^2 + y^2 + z^2 - 0.047^2 t^2 \\
-3.4x + 1.6y + ... | 3a8bc4e4aa901efc53c60b136a6fc6843ffb08c1 | 6,698 | ipynb | Jupyter Notebook | HW08/3.ipynb | mahdiarsadeghi/NumericalAnalysis | 95a0914c06963b0510971388f006a6b2fc0c4ef9 | [
"MIT"
] | null | null | null | HW08/3.ipynb | mahdiarsadeghi/NumericalAnalysis | 95a0914c06963b0510971388f006a6b2fc0c4ef9 | [
"MIT"
] | null | null | null | HW08/3.ipynb | mahdiarsadeghi/NumericalAnalysis | 95a0914c06963b0510971388f006a6b2fc0c4ef9 | [
"MIT"
] | null | null | null | 23.584507 | 132 | 0.415796 | true | 1,884 | Qwen/Qwen-72B | 1. YES
2. YES | 0.826712 | 0.727975 | 0.601826 | __label__yue_Hant | 0.1684 | 0.236573 |
<a href="https://colab.research.google.com/github/MathewsJosh/mecanica-estruturas-ufjf/blob/main/%5BMAC023%5D_Trabalho_02.ipynb" target="_parent"></a>
# **MAC023 - Mecânica das Estruturas**
# ME-02 - Segunda Avaliação de Conhecimentos
Alunos:
Brian Luis Coimbra Maia
Mathews Edwirds Gomes Almeida
# Condições Gerai... | 30445e2e790d54efda02f55fa75417c733cd9f67 | 251,590 | ipynb | Jupyter Notebook | [MAC023]_Trabalho_02.ipynb | MathewsJosh/mecanica-estruturas-ufjf | 7f94b9a7cdacdb5a22b8fc491959f309be65acfb | [
"MIT"
] | null | null | null | [MAC023]_Trabalho_02.ipynb | MathewsJosh/mecanica-estruturas-ufjf | 7f94b9a7cdacdb5a22b8fc491959f309be65acfb | [
"MIT"
] | null | null | null | [MAC023]_Trabalho_02.ipynb | MathewsJosh/mecanica-estruturas-ufjf | 7f94b9a7cdacdb5a22b8fc491959f309be65acfb | [
"MIT"
] | null | null | null | 323.796654 | 56,162 | 0.895894 | true | 4,776 | Qwen/Qwen-72B | 1. YES
2. YES | 0.754915 | 0.812867 | 0.613646 | __label__por_Latn | 0.994021 | 0.264035 |
# Direct Optimal Control Plotting
```python
import sys; sys.path.append(2*'../') # go n dirs back
from src import *
# Change device according to your configuration
# device = torch.device('cuda:1') if torch.cuda.is_available() else torch.device('cpu')
device = torch.device('cpu') # feel free to change :)
```
... | d52e17236757a27674e2d9f16da41d778c784fa0 | 503,325 | ipynb | Jupyter Notebook | hypersolvers-control/experiments/pendulum/03c_plot.ipynb | Juju-botu/diffeqml-research | aa796c87447e5299ec4f25a07fc4d032afb1f63e | [
"Apache-2.0"
] | null | null | null | hypersolvers-control/experiments/pendulum/03c_plot.ipynb | Juju-botu/diffeqml-research | aa796c87447e5299ec4f25a07fc4d032afb1f63e | [
"Apache-2.0"
] | null | null | null | hypersolvers-control/experiments/pendulum/03c_plot.ipynb | Juju-botu/diffeqml-research | aa796c87447e5299ec4f25a07fc4d032afb1f63e | [
"Apache-2.0"
] | null | null | null | 1,014.768145 | 245,130 | 0.950142 | true | 3,768 | Qwen/Qwen-72B | 1. YES
2. YES | 0.824462 | 0.798187 | 0.658075 | __label__eng_Latn | 0.357218 | 0.367259 |
Before you turn this problem in, make sure everything runs as expected. First, **restart the kernel** (in the menubar, select Kernel$\rightarrow$Restart) and then **run all cells** (in the menubar, select Cell$\rightarrow$Run All).
Make sure you fill in any place that says `YOUR CODE HERE` or "YOUR ANSWER HERE", as we... | 2cfc6b18a9465c463bf20d6d07db11d4c4509b8a | 136,578 | ipynb | Jupyter Notebook | Forward_Backward_and_Central_Differentiation.ipynb | PrabalChowdhury/CSE330-NUMERICAL-METHODS | aabfea01f4ceaecfbb50d771ee990777d6e1122c | [
"MIT"
] | null | null | null | Forward_Backward_and_Central_Differentiation.ipynb | PrabalChowdhury/CSE330-NUMERICAL-METHODS | aabfea01f4ceaecfbb50d771ee990777d6e1122c | [
"MIT"
] | null | null | null | Forward_Backward_and_Central_Differentiation.ipynb | PrabalChowdhury/CSE330-NUMERICAL-METHODS | aabfea01f4ceaecfbb50d771ee990777d6e1122c | [
"MIT"
] | null | null | null | 213.737089 | 31,666 | 0.894244 | true | 1,522 | Qwen/Qwen-72B | 1. YES
2. YES | 0.907312 | 0.91118 | 0.826724 | __label__eng_Latn | 0.580442 | 0.759091 |
# Artificial Intelligence in Finance
## Normative Finance
Dr Yves J Hilpisch | The AI Machine
http://aimachine.io | http://twitter.com/dyjh
## Uncertainty and Risk
```python
import numpy as np
```
```python
S0 = 10
B0 = 10
```
```python
S1 = np.array((20, 5))
B1 = np.array((11, 11))
```
```python
M0 = np.... | a811b7554bd4caa263f653a260bb437e5977626b | 24,674 | ipynb | Jupyter Notebook | 03_normative_finance.ipynb | pepelawycliffe/AI_in_Finance | 5eb29afed137c809955d116e7a7764b5914add96 | [
"MIT"
] | 3 | 2021-03-15T05:30:50.000Z | 2021-12-14T07:28:44.000Z | 03_normative_finance.ipynb | pepelawycliffe/AI_in_Finance | 5eb29afed137c809955d116e7a7764b5914add96 | [
"MIT"
] | null | null | null | 03_normative_finance.ipynb | pepelawycliffe/AI_in_Finance | 5eb29afed137c809955d116e7a7764b5914add96 | [
"MIT"
] | null | null | null | 20.109209 | 191 | 0.445043 | true | 3,159 | Qwen/Qwen-72B | 1. YES
2. YES | 0.877477 | 0.841826 | 0.738682 | __label__eng_Latn | 0.187549 | 0.554539 |
```python
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import pathlib
import os
import pwd
import figformat
fig_width,fig_height,params=figformat.figure_format(fig_width=3.4,fig_height=3.4)
mpl.rcParams.update(params)
#mpl.rcParams.keys()
#help(figformat)
def get_user... | 50e2344d3b2074c3bc38239697b38779643e966c | 51,502 | ipynb | Jupyter Notebook | examples/01_Python_emission_qfactor.ipynb | StevE-Ong/LEC | df479b38de83f6629ad5453760c166f9289b38e6 | [
"BSD-2-Clause"
] | 3 | 2020-07-02T14:57:31.000Z | 2021-11-19T09:44:40.000Z | examples/01_Python_emission_qfactor.ipynb | StevE-Ong/LEC | df479b38de83f6629ad5453760c166f9289b38e6 | [
"BSD-2-Clause"
] | 2 | 2020-07-23T15:05:27.000Z | 2021-11-19T12:12:53.000Z | examples/01_Python_emission_qfactor.ipynb | StevE-Ong/LEC | df479b38de83f6629ad5453760c166f9289b38e6 | [
"BSD-2-Clause"
] | 4 | 2020-07-24T08:11:32.000Z | 2022-01-10T02:58:14.000Z | 52.876797 | 11,603 | 0.585336 | true | 1,077 | Qwen/Qwen-72B | 1. YES
2. YES
| 0.839734 | 0.712232 | 0.598086 | __label__yue_Hant | 0.159924 | 0.227883 |
# Anomaly Detection with LSTM in Keras
Predict Anomalies using Confidence Intervals
https://towardsdatascience.com/anomaly-detection-with-lstm-in-keras-8d8d7e50ab1b
Detection of anomaly is useful in every business and the difficultness to detect these observations depends on the field of applications. If you are enga... | a3ef2227b3f473ac04f3771bce6927d07a372ba4 | 851,419 | ipynb | Jupyter Notebook | misc/Anomaly-Detection-LSTM.ipynb | OleBo/Stock-Prediction-Models | 3abd726d57b5d588d560c6a27db19b589cdae52f | [
"Apache-2.0"
] | null | null | null | misc/Anomaly-Detection-LSTM.ipynb | OleBo/Stock-Prediction-Models | 3abd726d57b5d588d560c6a27db19b589cdae52f | [
"Apache-2.0"
] | null | null | null | misc/Anomaly-Detection-LSTM.ipynb | OleBo/Stock-Prediction-Models | 3abd726d57b5d588d560c6a27db19b589cdae52f | [
"Apache-2.0"
] | 2 | 2020-03-27T15:25:41.000Z | 2020-12-17T10:51:15.000Z | 460.226486 | 215,116 | 0.930659 | true | 12,551 | Qwen/Qwen-72B | 1. YES
2. YES | 0.826712 | 0.787931 | 0.651392 | __label__eng_Latn | 0.764459 | 0.351733 |
# Euler angle worksheet
This is a [jupyter notebook](https://jupyter.org/). Jupyter Notebooks allows you to combine notes, code, and output into a single document. You can even export your document as a presentation or presentation.
In this worksheet, we will use the python3 SymPy package to derive expressions for co... | e6851d2a32d1cee8e3520944e094d933b8bfb427 | 19,607 | ipynb | Jupyter Notebook | Labs/EulerAngles.ipynb | isaacwasserman/website | c052e1e8b28b9a600623589768691585eeda774d | [
"MIT"
] | null | null | null | Labs/EulerAngles.ipynb | isaacwasserman/website | c052e1e8b28b9a600623589768691585eeda774d | [
"MIT"
] | null | null | null | Labs/EulerAngles.ipynb | isaacwasserman/website | c052e1e8b28b9a600623589768691585eeda774d | [
"MIT"
] | 1 | 2021-09-28T20:41:54.000Z | 2021-09-28T20:41:54.000Z | 26.895748 | 298 | 0.387209 | true | 4,905 | Qwen/Qwen-72B | 1. YES
2. YES | 0.930458 | 0.919643 | 0.855689 | __label__eng_Latn | 0.73384 | 0.826386 |
# 逆行列を求める方法
- `np.linalg` に `inv` という関数がある
```python
import numpy as np
```
```python
a = np.array([[3, 1, 1], [1, 2, 1], [0, -1, 1]])
```
```python
np.linalg.inv(a)
```
array([[ 0.42857143, -0.28571429, -0.14285714],
[-0.14285714, 0.42857143, -0.28571429],
[-0.14285714, 0.4285714... | 3c7d876a06b6931b103f45efa16f6673a3092cdc | 4,575 | ipynb | Jupyter Notebook | notebooks/linear_equations.ipynb | 515hikaru/essence-of-machine-learning | 7f46be9316d227626f27a06deac64b43191cb4d7 | [
"MIT"
] | null | null | null | notebooks/linear_equations.ipynb | 515hikaru/essence-of-machine-learning | 7f46be9316d227626f27a06deac64b43191cb4d7 | [
"MIT"
] | 11 | 2018-10-04T14:33:15.000Z | 2018-10-09T13:40:35.000Z | notebooks/linear_equations.ipynb | 515hikaru/essence-of-machine-learning | 7f46be9316d227626f27a06deac64b43191cb4d7 | [
"MIT"
] | null | null | null | 18.983402 | 99 | 0.460109 | true | 913 | Qwen/Qwen-72B | 1. YES
2. YES | 0.959154 | 0.887205 | 0.850966 | __label__yue_Hant | 0.494295 | 0.815413 |
# Matérn Spectral Mixture (MSM) kernel
## Gaussian process priors for pitch detection in polyphonic music
### Learning kernels in frequency domain
#### Written by Pablo A. Alvarado, Centre for Digital Music, Queen Mary University of London.
*Last updated Friday, 12 May 2017.*
The aim of this notebook is to ilustr... | ec4ec47787a11f33be7f9b33cdd5982e2033c3be | 422,921 | ipynb | Jupyter Notebook | demo_MSMK.ipynb | PabloAlvarado/MSMK | 9976429cf0a7f38e52d890e1cf6e6fc982fe2882 | [
"Apache-2.0"
] | 3 | 2018-02-20T20:38:40.000Z | 2021-12-12T14:26:12.000Z | demo_MSMK.ipynb | PabloAlvarado/MSMK | 9976429cf0a7f38e52d890e1cf6e6fc982fe2882 | [
"Apache-2.0"
] | 1 | 2018-11-16T15:00:03.000Z | 2019-04-08T08:27:10.000Z | demo_MSMK.ipynb | PabloAlvarado/MSMK | 9976429cf0a7f38e52d890e1cf6e6fc982fe2882 | [
"Apache-2.0"
] | 1 | 2020-06-01T07:21:59.000Z | 2020-06-01T07:21:59.000Z | 624.698671 | 79,588 | 0.942817 | true | 4,512 | Qwen/Qwen-72B | 1. YES
2. YES | 0.808067 | 0.695958 | 0.562381 | __label__eng_Latn | 0.717111 | 0.144929 |
# Music Machine Learning - Probability distributions
### Author: Philippe Esling (esling@ircam.fr)
In this course we will cover
1. A [quick recap](#recap) on simple probability concepts
2. An introduction to [probability distributions](#distributions)
3. An explanation on how to [sample](#sampling) from distributions... | 8cd934b6febdda2186cdda4118dea1246ccf472a | 22,251 | ipynb | Jupyter Notebook | 04b_distributions.ipynb | piptouque/atiam_ml | 9da637eae179237d30a15dd9ce3e95a2a956c385 | [
"MIT"
] | null | null | null | 04b_distributions.ipynb | piptouque/atiam_ml | 9da637eae179237d30a15dd9ce3e95a2a956c385 | [
"MIT"
] | null | null | null | 04b_distributions.ipynb | piptouque/atiam_ml | 9da637eae179237d30a15dd9ce3e95a2a956c385 | [
"MIT"
] | null | null | null | 39.312721 | 713 | 0.58694 | true | 4,281 | Qwen/Qwen-72B | 1. YES
2. YES | 0.893309 | 0.855851 | 0.76454 | __label__eng_Latn | 0.986619 | 0.614614 |
```python
from IPython.display import Image
from IPython.core.display import HTML
from sympy import *; x,h,y = symbols("x h y")
Image(url= "https://i.imgur.com/bNY57ZF.png")
```
```python
expr = 4/(x-2)
def F(x):
return expr
F(x)
#first step is to find dF(x)
#then we do slope point form with dF(x) as m ... | c415478cb7a1e1ff5835b65d17226060fe43e693 | 3,291 | ipynb | Jupyter Notebook | Calculus_Homework/WWB08.16.ipynb | NSC9/Sample_of_Work | 8f8160fbf0aa4fd514d4a5046668a194997aade6 | [
"MIT"
] | null | null | null | Calculus_Homework/WWB08.16.ipynb | NSC9/Sample_of_Work | 8f8160fbf0aa4fd514d4a5046668a194997aade6 | [
"MIT"
] | null | null | null | Calculus_Homework/WWB08.16.ipynb | NSC9/Sample_of_Work | 8f8160fbf0aa4fd514d4a5046668a194997aade6 | [
"MIT"
] | null | null | null | 18.385475 | 76 | 0.449711 | true | 256 | Qwen/Qwen-72B | 1. YES
2. YES | 0.891811 | 0.771843 | 0.688339 | __label__yue_Hant | 0.310087 | 0.437572 |
# Custom Gradient
This is a brief demonstration of tensorflow [custom gradients](https://www.tensorflow.org/api_docs/python/tf/custom_gradient)
## Chain rule
Lets say we have a function $f(x) = x^2$. If we now compose this function such that $y = f(f(f(x)))$. Now we want to find the gradient $\frac{dy}{dx}$.
We fir... | 714b44be1b5c9e4648a47909c84381abb78f802d | 8,436 | ipynb | Jupyter Notebook | notebooks/custom-gradient.ipynb | Ghost---Shadow/gradient-tape-experiments | 1ded25074defe2d5936c97d2b8d570a3d3614a18 | [
"MIT"
] | 4 | 2020-10-24T19:07:45.000Z | 2021-12-23T20:23:43.000Z | notebooks/custom-gradient.ipynb | Ghost---Shadow/gradient-tape-experiments | 1ded25074defe2d5936c97d2b8d570a3d3614a18 | [
"MIT"
] | null | null | null | notebooks/custom-gradient.ipynb | Ghost---Shadow/gradient-tape-experiments | 1ded25074defe2d5936c97d2b8d570a3d3614a18 | [
"MIT"
] | 1 | 2021-04-06T10:51:35.000Z | 2021-04-06T10:51:35.000Z | 26.528302 | 241 | 0.491465 | true | 1,724 | Qwen/Qwen-72B | 1. YES
2. YES | 0.926304 | 0.870597 | 0.806437 | __label__eng_Latn | 0.542879 | 0.711957 |
# Extension of DV Formula
$\def\abs#1{\left\lvert #1 \right\rvert}
\def\Set#1{\left\{ #1 \right\}}
\def\mc#1{\mathcal{#1}}
\def\M#1{\boldsymbol{#1}}
\def\R#1{\mathsf{#1}}
\def\RM#1{\boldsymbol{\mathsf{#1}}}
\def\op#1{\operatorname{#1}}
\def\E{\op{E}}
\def\d{\mathrm{\mathstrut d}}$
## $f$-divergence
Consider the more... | e3a873e943c12d2450ba903fe091342920d33afb | 133,110 | ipynb | Jupyter Notebook | part1/f-Divergence.ipynb | ccha23/cscit21 | 87de8c48c640406d9a9d282fc10a238122814f53 | [
"MIT"
] | null | null | null | part1/f-Divergence.ipynb | ccha23/cscit21 | 87de8c48c640406d9a9d282fc10a238122814f53 | [
"MIT"
] | null | null | null | part1/f-Divergence.ipynb | ccha23/cscit21 | 87de8c48c640406d9a9d282fc10a238122814f53 | [
"MIT"
] | null | null | null | 179.393531 | 69,604 | 0.901149 | true | 2,490 | Qwen/Qwen-72B | 1. YES
2. YES | 0.782662 | 0.849971 | 0.665241 | __label__eng_Latn | 0.954804 | 0.383908 |
### SETUP
Paste the code in [coupled_euler.py](../coupled_euler.py) in a python file in the working directory and make another new .py file
Now, use this command to import all the functions and class definitions in coupled_euler.py
```python
from coupled_euler import *
```
Also make the following imports,
```py... | 38f152cd41d70e26d632540e0cd1ee47af39eec9 | 4,153 | ipynb | Jupyter Notebook | coupled_DE/.ipynb_checkpoints/LC_oscillations-checkpoint.ipynb | plancky/mathematical_physics_II | c912dca1a58c218ddb06dc6cbca021b03a703540 | [
"CC0-1.0"
] | null | null | null | coupled_DE/.ipynb_checkpoints/LC_oscillations-checkpoint.ipynb | plancky/mathematical_physics_II | c912dca1a58c218ddb06dc6cbca021b03a703540 | [
"CC0-1.0"
] | null | null | null | coupled_DE/.ipynb_checkpoints/LC_oscillations-checkpoint.ipynb | plancky/mathematical_physics_II | c912dca1a58c218ddb06dc6cbca021b03a703540 | [
"CC0-1.0"
] | null | null | null | 27.143791 | 144 | 0.521551 | true | 828 | Qwen/Qwen-72B | 1. YES
2. YES | 0.868827 | 0.763484 | 0.663335 | __label__eng_Latn | 0.850675 | 0.379481 |
# Stochastic Processes: <br>Data Analysis and Computer Simulation
<br>
# Brownian motion 2: computer simulation
<br>
# 3. Simulations with on-the-fly animation
<br>
# 3.1. Simulation code with on-the-fly animation
## Import libraries
```python
% matplotlib nbagg
import numpy as np # import numpy library as np
i... | 1c5c3ebc46d1f53a3c42e0ef78db157173c0a84c | 1,003,781 | ipynb | Jupyter Notebook | edx-stochastic-data-analysis/downloaded_files/04/009x_43.ipynb | mirandagil/extra-courses | 51858f5089b10b070de43ea3809697760aa261ec | [
"MIT"
] | null | null | null | edx-stochastic-data-analysis/downloaded_files/04/009x_43.ipynb | mirandagil/extra-courses | 51858f5089b10b070de43ea3809697760aa261ec | [
"MIT"
] | null | null | null | edx-stochastic-data-analysis/downloaded_files/04/009x_43.ipynb | mirandagil/extra-courses | 51858f5089b10b070de43ea3809697760aa261ec | [
"MIT"
] | null | null | null | 856.46843 | 957,092 | 0.942863 | true | 2,525 | Qwen/Qwen-72B | 1. YES
2. YES | 0.896251 | 0.721743 | 0.646863 | __label__eng_Latn | 0.65105 | 0.341211 |
###### Content under Creative Commons Attribution license CC-BY 4.0, code under MIT license (c)2014 L.A. Barba, G.F. Forsyth.
# Relax and hold steady
Ready for more relaxing? This is the third lesson of **Module 5** of the course, exploring solutions to elliptic PDEs.
In [Lesson 1](http://nbviewer.ipython.org/githu... | 4180c554be123d4a53ce2a397bd1fb65d4d0fa9d | 102,331 | ipynb | Jupyter Notebook | lessons/05_relax/05_03_Iterate.This.ipynb | sergiommr/numerical-mooc | b088e9d205f15dbc22f83e45c2181a2c5809365f | [
"CC-BY-3.0"
] | 11 | 2018-04-12T08:05:58.000Z | 2022-03-31T17:24:14.000Z | lessons/05_relax/05_03_Iterate.This.ipynb | sergiommr/numerical-mooc | b088e9d205f15dbc22f83e45c2181a2c5809365f | [
"CC-BY-3.0"
] | 1 | 2017-01-16T20:53:59.000Z | 2017-01-16T20:53:59.000Z | lessons/05_relax/05_03_Iterate.This.ipynb | sergiommr/numerical-mooc | b088e9d205f15dbc22f83e45c2181a2c5809365f | [
"CC-BY-3.0"
] | 12 | 2016-05-02T16:47:17.000Z | 2020-03-24T16:24:16.000Z | 66.839321 | 51,376 | 0.751278 | true | 9,168 | Qwen/Qwen-72B | 1. YES
2. YES | 0.805632 | 0.740174 | 0.596308 | __label__eng_Latn | 0.980821 | 0.223754 |
```python
%matplotlib inline
```
使用 PyTorch 进行 深度学习
**************************
翻译者： http://www.studyai.com/antares
深度学习构建块: 仿射映射, 非线性单元 和 目标函数
==========================================================================
深度学习包括以聪明的方式组合线性和非线性。非线性的引入使模型变得很强大。
在本节中，我们将使用这些核心组件，构造一个目标函数，并查看模型是如何训练的。
仿射映射
~~~~~~~~~~~
深度... | 7afa85ed19e52ac2795c3f25b5a250923b348144 | 25,408 | ipynb | Jupyter Notebook | build/_downloads/97d5fed33a2c5bb8f1875babdea02f4c/deep_learning_tutorial.ipynb | ScorpioDoctor/antares02 | 631b817d2e98f351d1173b620d15c4a5efed11da | [
"BSD-3-Clause"
] | null | null | null | build/_downloads/97d5fed33a2c5bb8f1875babdea02f4c/deep_learning_tutorial.ipynb | ScorpioDoctor/antares02 | 631b817d2e98f351d1173b620d15c4a5efed11da | [
"BSD-3-Clause"
] | null | null | null | build/_downloads/97d5fed33a2c5bb8f1875babdea02f4c/deep_learning_tutorial.ipynb | ScorpioDoctor/antares02 | 631b817d2e98f351d1173b620d15c4a5efed11da | [
"BSD-3-Clause"
] | null | null | null | 138.84153 | 3,203 | 0.721662 | true | 4,639 | Qwen/Qwen-72B | 1. YES
2. YES | 0.839734 | 0.774583 | 0.650444 | __label__yue_Hant | 0.490528 | 0.34953 |
```python
from IPython.display import HTML
tag = HTML('''
Promijeni vidljivost <a href="javascript:code_toggle()">ovdje</a>.''')
display(tag)
```
Promijeni vidljivost <a href="javascript:code_toggle()">ovdje</a>.
```python
# Erasmus+ ICCT project (2018-1-SI01-KA203-047081)
%matplotlib notebook
import numpy as np... | e1970d321f3e7c1d3aa28440562909c8832dc37b | 142,447 | ipynb | Jupyter Notebook | ICCT_hr/examples/02/TD-12-Aproksimacija_dominantnim_polom.ipynb | ICCTerasmus/ICCT | fcd56ab6b5fddc00f72521cc87accfdbec6068f6 | [
"BSD-3-Clause"
] | 6 | 2021-05-22T18:42:14.000Z | 2021-10-03T14:10:22.000Z | ICCT_hr/examples/02/.ipynb_checkpoints/TD-12-Aproksimacija_dominantnim_polom-checkpoint.ipynb | ICCTerasmus/ICCT | fcd56ab6b5fddc00f72521cc87accfdbec6068f6 | [
"BSD-3-Clause"
] | null | null | null | ICCT_hr/examples/02/.ipynb_checkpoints/TD-12-Aproksimacija_dominantnim_polom-checkpoint.ipynb | ICCTerasmus/ICCT | fcd56ab6b5fddc00f72521cc87accfdbec6068f6 | [
"BSD-3-Clause"
] | 2 | 2021-05-24T11:40:09.000Z | 2021-08-29T16:36:18.000Z | 128.794756 | 96,483 | 0.810659 | true | 2,304 | Qwen/Qwen-72B | 1. YES
2. YES | 0.766294 | 0.70253 | 0.538344 | __label__hrv_Latn | 0.178864 | 0.089084 |
# Using DQN to cross a bridge in World of Warcraft
## The bridge
The bridge can be found at coordinates X:1669.62, Y:-3731.47, Z:148.3 in the zone Howling Fjord. The bridge have no rails thus the agent can easily fall off. The goal is to cross the bridge without falling down with the help of DQN. A trained agent shoul... | 2bbd43c3db81ee7eefdd67a20d59d7f62052634d | 58,385 | ipynb | Jupyter Notebook | wow_rl_sim.ipynb | Jacobth/wow_sim_notebook | bf398442e2f6d9ddf7ea8ae02ebe563252db4c2d | [
"MIT"
] | null | null | null | wow_rl_sim.ipynb | Jacobth/wow_sim_notebook | bf398442e2f6d9ddf7ea8ae02ebe563252db4c2d | [
"MIT"
] | null | null | null | wow_rl_sim.ipynb | Jacobth/wow_sim_notebook | bf398442e2f6d9ddf7ea8ae02ebe563252db4c2d | [
"MIT"
] | null | null | null | 75.530401 | 18,104 | 0.780235 | true | 4,269 | Qwen/Qwen-72B | 1. YES
2. YES | 0.828939 | 0.66888 | 0.554461 | __label__eng_Latn | 0.958315 | 0.126528 |
# Data Fitting Exercises
# 2. Linear Least-Squares
### How to define a line of “best fit”
In the previous exercise, you used the `linregress` function to calculate a line of best fit to your experimental data. But what is this function actually doing, and how do we define the quality of fit for a particu... | 21e4f0482649d0a6c83292947cdc588e651fc1b3 | 12,854 | ipynb | Jupyter Notebook | Data_Fitting_Exercise_S2_2.ipynb | pythoninchemistry/chem_data_analysis_jupyter | 4af545f1a8acdded28d96508bb5adc8929da92cc | [
"CC-BY-4.0"
] | 3 | 2019-05-05T00:21:55.000Z | 2021-09-16T14:15:15.000Z | Data_Fitting_Exercise_S2_2.ipynb | pythoninchemistry/chem_data_analysis_jupyter | 4af545f1a8acdded28d96508bb5adc8929da92cc | [
"CC-BY-4.0"
] | null | null | null | Data_Fitting_Exercise_S2_2.ipynb | pythoninchemistry/chem_data_analysis_jupyter | 4af545f1a8acdded28d96508bb5adc8929da92cc | [
"CC-BY-4.0"
] | null | null | null | 50.015564 | 544 | 0.642213 | true | 2,435 | Qwen/Qwen-72B | 1. YES
2. YES | 0.939025 | 0.851953 | 0.800005 | __label__eng_Latn | 0.998579 | 0.697012 |
# Entscheidungsbäume
Alice beobachtet die Tennisspieler auf dem Tennisplatz vor ihrem Haus. Sie möchte herausfinden, wie die Spieler entscheiden, bei welchem Wetter die Spieler Tennis spielen und wann nicht. Sie macht die folgenden Beobachtungen:
Erstellen Sie einen Entscheidungsbaum auf Basis dieser Trainingsdate... | 096a43efe076eb1af87aa754c3eaf94665f43ae3 | 122,819 | ipynb | Jupyter Notebook | 05-Weitere-Klassifikatoren.ipynb | stefanluedtke/AI-II-Exercises | a1e816375c58f52609c3f7683b17a35fed64bc1d | [
"MIT"
] | null | null | null | 05-Weitere-Klassifikatoren.ipynb | stefanluedtke/AI-II-Exercises | a1e816375c58f52609c3f7683b17a35fed64bc1d | [
"MIT"
] | null | null | null | 05-Weitere-Klassifikatoren.ipynb | stefanluedtke/AI-II-Exercises | a1e816375c58f52609c3f7683b17a35fed64bc1d | [
"MIT"
] | null | null | null | 871.056738 | 118,424 | 0.950024 | true | 904 | Qwen/Qwen-72B | 1. YES
2. YES
| 0.893309 | 0.845942 | 0.755688 | __label__deu_Latn | 0.955825 | 0.594049 |
<div style='background-image: url("title01.png") ; padding: 0px ; background-size: cover ; border-radius: 5px ; height: 200px'>
<div style="float: right ; margin: 50px ; padding: 20px ; background: rgba(255 , 255 , 255 , 0.7) ; width: 50% ; height: 150px">
<div style="position: relative ; top: 50% ; transform: translat... | ea6fc4c8cf9813f58ddace380cb6266864a22b93 | 30,711 | ipynb | Jupyter Notebook | PDE's/Using Taylor.ipynb | MonitSharma/Computational-Methods-in-Physics | e3b2db36c37dd5f64b9a37ba39e9bb267ba27d85 | [
"MIT"
] | null | null | null | PDE's/Using Taylor.ipynb | MonitSharma/Computational-Methods-in-Physics | e3b2db36c37dd5f64b9a37ba39e9bb267ba27d85 | [
"MIT"
] | null | null | null | PDE's/Using Taylor.ipynb | MonitSharma/Computational-Methods-in-Physics | e3b2db36c37dd5f64b9a37ba39e9bb267ba27d85 | [
"MIT"
] | null | null | null | 119.034884 | 22,469 | 0.837387 | true | 1,534 | Qwen/Qwen-72B | 1. YES
2. YES | 0.904651 | 0.880797 | 0.796814 | __label__eng_Latn | 0.959784 | 0.689597 |
<a href="https://colab.research.google.com/github/anathnath/EDA/blob/master/CBCS_V_SchrodingerEquationPartI.ipynb" target="_parent"></a>
# $ \color{green}{ CoreP11-Quantum~ Mechanics~ and~ Application ~lab} $
## $ \color{green}{West~Bengal ~ State~ University} $
# Quantum Mechanics and Application: 60 class Hours 2 c... | ba9090260504921d07725e6eb929bcfac9f8d9c0 | 660,040 | ipynb | Jupyter Notebook | CBCS_V_SchrodingerEquationPartI.ipynb | anathnath/EDA | 26b24ccfffe70747b84074598a069cdc96300163 | [
"MIT"
] | null | null | null | CBCS_V_SchrodingerEquationPartI.ipynb | anathnath/EDA | 26b24ccfffe70747b84074598a069cdc96300163 | [
"MIT"
] | null | null | null | CBCS_V_SchrodingerEquationPartI.ipynb | anathnath/EDA | 26b24ccfffe70747b84074598a069cdc96300163 | [
"MIT"
] | null | null | null | 61.273672 | 171,162 | 0.632153 | true | 89,254 | Qwen/Qwen-72B | 1. YES
2. YES | 0.867036 | 0.831143 | 0.720631 | __label__eng_Latn | 0.56528 | 0.512598 |
# Loading and Plotting Data
For the first part, we'll be doing linear regression with one variable, and so we'll use only two fields from the daily data set: the normalized high temperature in C, and the total number of bike rentals. The values for rentals are scaled by a factor of a thousand, given the difference i... | 8b1641c3692f61ac9799e2a0578ce27680cf1af5 | 272,519 | ipynb | Jupyter Notebook | ml-linear-regression.ipynb | Jerry671/natural-language-project | 943415ac6085a74363b4f7e881454ebcfccc7689 | [
"Apache-2.0"
] | null | null | null | ml-linear-regression.ipynb | Jerry671/natural-language-project | 943415ac6085a74363b4f7e881454ebcfccc7689 | [
"Apache-2.0"
] | null | null | null | ml-linear-regression.ipynb | Jerry671/natural-language-project | 943415ac6085a74363b4f7e881454ebcfccc7689 | [
"Apache-2.0"
] | 1 | 2020-11-28T07:41:42.000Z | 2020-11-28T07:41:42.000Z | 255.167603 | 138,432 | 0.895831 | true | 9,770 | Qwen/Qwen-72B | 1. YES
2. YES | 0.83762 | 0.675765 | 0.566034 | __label__eng_Latn | 0.386092 | 0.153416 |
```python
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
```
# The frequency of a Ricker wavelet
We often use Ricker wavelets to model seismic, for example when making a synthetic seismogram with which to help tie a well. One simple way to guesstimate the peak or... | 6d3dd9a9d6328af6673124db842a76ef6f03aaa4 | 192,663 | ipynb | Jupyter Notebook | The_frequency_of_a_Ricker.ipynb | agilescientific/notebooks | cc5ef92f218d39d1d31f9c5e89aaeb8c6d466c20 | [
"Apache-2.0"
] | 100 | 2015-01-02T16:45:25.000Z | 2022-01-29T14:01:00.000Z | The_frequency_of_a_Ricker.ipynb | afcarl/notebooks-agile-geoscience | 446a992bf670ddbae7d2524a41e667a56db09bff | [
"Apache-2.0"
] | 1 | 2018-01-02T15:25:06.000Z | 2018-01-02T15:25:06.000Z | The_frequency_of_a_Ricker.ipynb | afcarl/notebooks-agile-geoscience | 446a992bf670ddbae7d2524a41e667a56db09bff | [
"Apache-2.0"
] | 69 | 2015-01-02T16:45:25.000Z | 2022-02-25T01:18:45.000Z | 319.507463 | 59,344 | 0.921215 | true | 2,422 | Qwen/Qwen-72B | 1. YES
2. YES | 0.901921 | 0.904651 | 0.815923 | __label__eng_Latn | 0.993132 | 0.733995 |
## Teoría de perturbaciones
Consiste en resolver un sistema perturbado(se conoce la solución al no perturbado), y donde el interés es conocer la contribución de la parte perturbada $H'$ al nuevo sistema total.
$$ H = H^{0} + H'$$
Para sistemas no degenerados, la corrección a la energía a primer orden se calcula com... | 2f78a038361aa710d2708c8a8776481c4f713f38 | 33,664 | ipynb | Jupyter Notebook | Perturbaciones/Perturbaciones lalo.ipynb | lazarusA/Density-functional-theory | c74fd44a66f857de570dc50471b24391e3fa901f | [
"MIT"
] | null | null | null | Perturbaciones/Perturbaciones lalo.ipynb | lazarusA/Density-functional-theory | c74fd44a66f857de570dc50471b24391e3fa901f | [
"MIT"
] | null | null | null | Perturbaciones/Perturbaciones lalo.ipynb | lazarusA/Density-functional-theory | c74fd44a66f857de570dc50471b24391e3fa901f | [
"MIT"
] | null | null | null | 76.335601 | 20,376 | 0.767051 | true | 2,404 | Qwen/Qwen-72B | 1. YES
2. YES | 0.757794 | 0.746139 | 0.56542 | __label__spa_Latn | 0.696464 | 0.15199 |
## Heat Transfer problem with linear initial temperature and steady surface temperature
```python
import numpy as np
import math
import matplotlib.pyplot as plt
%matplotlib inline
from scipy.optimize import newton
from mpl_toolkits.mplot3d import axes3d
from matplotlib import cm
import numpy.ma as ma
from scipy.integ... | 8cd1f6a76bb8d0952c6af65787270520e59595d6 | 194,505 | ipynb | Jupyter Notebook | presentations/11_04_19_Renyu.ipynb | uw-cheme512/uw-cheme512.github.io | 6dad7a9554eafb6eba347462d30c62bf9c0ec4da | [
"BSD-3-Clause"
] | null | null | null | presentations/11_04_19_Renyu.ipynb | uw-cheme512/uw-cheme512.github.io | 6dad7a9554eafb6eba347462d30c62bf9c0ec4da | [
"BSD-3-Clause"
] | null | null | null | presentations/11_04_19_Renyu.ipynb | uw-cheme512/uw-cheme512.github.io | 6dad7a9554eafb6eba347462d30c62bf9c0ec4da | [
"BSD-3-Clause"
] | null | null | null | 750.984556 | 167,752 | 0.948963 | true | 1,327 | Qwen/Qwen-72B | 1. YES
2. YES | 0.931463 | 0.863392 | 0.804217 | __label__eng_Latn | 0.691246 | 0.706798 |
```python
# Libraries Sympy and Numpy
from sympy import*
import numpy as np
# To define automatic printing mode (Not needed anymore)
# init_printing()
# For priting with text
from IPython.display import display, Latex
# Plotting Libraries
from mpl_toolkits import mplot3d
import matplotlib.pyplot as plt
from matplot... | 3746cff2a0ac284d2be70101aaf1f9ee848ec599 | 797,036 | ipynb | Jupyter Notebook | Active_CN_Model.ipynb | SanTT19/Symbolic_Python | 3bfaec4fc62de52e6a592cf3db333e605a35fcbc | [
"MIT"
] | null | null | null | Active_CN_Model.ipynb | SanTT19/Symbolic_Python | 3bfaec4fc62de52e6a592cf3db333e605a35fcbc | [
"MIT"
] | null | null | null | Active_CN_Model.ipynb | SanTT19/Symbolic_Python | 3bfaec4fc62de52e6a592cf3db333e605a35fcbc | [
"MIT"
] | null | null | null | 308.928682 | 144,144 | 0.921547 | true | 14,425 | Qwen/Qwen-72B | 1. YES
2. YES | 0.872347 | 0.899121 | 0.784346 | __label__eng_Latn | 0.343928 | 0.660631 |
<a href="https://colab.research.google.com/github/colbrydi/Scientific_Image_Understanding/blob/master/05-Registration-pre-class-assignment.ipynb" target="_parent"></a>
# Pre-Class Assignment: Image Registration
# Goals for today's pre-class assignment
1. [Image Registration](#Image-Registration)
2. [Basic Rigid T... | b4a973a529ea760a226a73e32a230e2ac0bf05dd | 10,367 | ipynb | Jupyter Notebook | 05-Registration-pre-class-assignment.ipynb | colbrydi/Scientific_Image_Understandin | c4c931fd3bf20f899b8ebaaeef24c15eaca43867 | [
"MIT"
] | 3 | 2021-02-24T15:23:42.000Z | 2022-01-10T20:36:11.000Z | 05-Registration-pre-class-assignment.ipynb | colbrydi/Scientific_Image_Understandin | c4c931fd3bf20f899b8ebaaeef24c15eaca43867 | [
"MIT"
] | null | null | null | 05-Registration-pre-class-assignment.ipynb | colbrydi/Scientific_Image_Understandin | c4c931fd3bf20f899b8ebaaeef24c15eaca43867 | [
"MIT"
] | 4 | 2021-03-01T16:54:31.000Z | 2022-01-24T20:42:36.000Z | 30.671598 | 394 | 0.57056 | true | 1,806 | Qwen/Qwen-72B | 1. YES
2. YES | 0.743168 | 0.839734 | 0.624063 | __label__eng_Latn | 0.791945 | 0.288239 |
# Balancer Simulations Math Challenge - Advanced
This notebook provides a collection of challenges for an advanced understanding of Balancer Math.
```python
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import math
```
... | c7a5a424e6233ef9e90b16030822d07a0715158b | 114,137 | ipynb | Jupyter Notebook | Math Challenges-Advanced.ipynb | bloxmove-com/Token_Engineering_Math_Challenge_All | 73fbff799cc4aa31dbea95cc80e2345219864aa5 | [
"MIT"
] | null | null | null | Math Challenges-Advanced.ipynb | bloxmove-com/Token_Engineering_Math_Challenge_All | 73fbff799cc4aa31dbea95cc80e2345219864aa5 | [
"MIT"
] | null | null | null | Math Challenges-Advanced.ipynb | bloxmove-com/Token_Engineering_Math_Challenge_All | 73fbff799cc4aa31dbea95cc80e2345219864aa5 | [
"MIT"
] | null | null | null | 26.076536 | 624 | 0.379439 | true | 12,070 | Qwen/Qwen-72B | 1. YES
2. YES | 0.903294 | 0.815232 | 0.736395 | __label__eng_Latn | 0.730221 | 0.549224 |
# [HW10] Simple Linear Regression
## 1. Linear regression
Linear regression은 종속 변수 $y$와 한개 이상의 독립 변수 $X$와의 선형 관계를 모델링하는 방법론입니다. 여기서 독립 변수는 입력 값이나 원인을 나타내고, 종속 변수는 독립 변수에 의해 영향을 받는 변수입니다. 종속 변수는 보통 결과물을 나타냅니다.
선형 관계를 모델링한다는 것은 1차로 이루어진 직선을 구하는 것입니다. 우리의 데이터를 가장 잘 설명하는 최적의 직선을 찾아냄으로써 독립 변수와 종속 변수 사이의 관계를 도출해 내는... | 50e4482ffb529e6b342b8c353d02d9fce71f8ea0 | 236,026 | ipynb | Jupyter Notebook | 03_Machine_Learning/sol/[HW10]_Simple_Linear_Regression.ipynb | wjh1065/goormNLP | ed6aeef6f76507f3e1a2abb15abdad33074bdaaa | [
"MIT"
] | null | null | null | 03_Machine_Learning/sol/[HW10]_Simple_Linear_Regression.ipynb | wjh1065/goormNLP | ed6aeef6f76507f3e1a2abb15abdad33074bdaaa | [
"MIT"
] | null | null | null | 03_Machine_Learning/sol/[HW10]_Simple_Linear_Regression.ipynb | wjh1065/goormNLP | ed6aeef6f76507f3e1a2abb15abdad33074bdaaa | [
"MIT"
] | null | null | null | 226.078544 | 52,730 | 0.905909 | true | 3,517 | Qwen/Qwen-72B | 1. YES
2. YES | 0.897695 | 0.819893 | 0.736014 | __label__kor_Hang | 0.999993 | 0.54834 |
# Best Approximation in Hilbert Spaces
```python
%matplotlib inline
import sympy as sym
import pylab as pl
import numpy as np
import numpy.polynomial.polynomial as n_poly
import numpy.polynomial.legendre as leg
```
## Mindflow
We want the best approximation (in Hilbert Spaces) of the function $f$, on the space $V =... | 556e173c830749844ac5bf5b9208c0adcc551aa1 | 176,934 | ipynb | Jupyter Notebook | python-lectures/04_best_approximation.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/04_best_approximation.ipynb | denocris/Introduction-to-Numerical-Analysis | 45b40a7743e11457b644fc6a7de17a0854ece4f0 | [
"CC-BY-4.0"
] | null | null | null | python-lectures/04_best_approximation.ipynb | denocris/Introduction-to-Numerical-Analysis | 45b40a7743e11457b644fc6a7de17a0854ece4f0 | [
"CC-BY-4.0"
] | 8 | 2018-01-21T16:45:34.000Z | 2021-06-25T15:56:27.000Z | 196.812013 | 35,886 | 0.888326 | true | 4,346 | Qwen/Qwen-72B | 1. YES
2. YES | 0.83762 | 0.868827 | 0.727747 | __label__eng_Latn | 0.520464 | 0.529131 |
# Examples of image reconstruction using PCA
Data classification in high dimensional spaces can be challenging and the results often lack robustness.
This well-known problem has its own name; <i>the curse of dimensionality</i>. Principal Component
Analysis is a popular method for dimensionality reduction. It can al... | 7451263beba85f8bafd8c4413d4143be5e64d0f1 | 500,780 | ipynb | Jupyter Notebook | 13.1_Generate_image_reconstructions_using_PCA.ipynb | AstroPierre/Scripts-for-figures-courses-GIF-4101-GIF-7005 | a38ad6f960cc6b8155fad00e4c4562f5e459f248 | [
"BSD-2-Clause"
] | null | null | null | 13.1_Generate_image_reconstructions_using_PCA.ipynb | AstroPierre/Scripts-for-figures-courses-GIF-4101-GIF-7005 | a38ad6f960cc6b8155fad00e4c4562f5e459f248 | [
"BSD-2-Clause"
] | null | null | null | 13.1_Generate_image_reconstructions_using_PCA.ipynb | AstroPierre/Scripts-for-figures-courses-GIF-4101-GIF-7005 | a38ad6f960cc6b8155fad00e4c4562f5e459f248 | [
"BSD-2-Clause"
] | null | null | null | 625.975 | 162,136 | 0.944511 | true | 5,140 | Qwen/Qwen-72B | 1. YES
2. YES | 0.937211 | 0.903294 | 0.846577 | __label__eng_Latn | 0.971282 | 0.805215 |
<a href="https://colab.research.google.com/github/neurologic/MotorSystems_BIOL358_SP22/blob/main/Tutorial_GeometricViewOfData.ipynb" target="_parent"></a>
# Tutorial: Geometric view of data
---
# Objectives
In this notebook we'll explore how multivariate data can be represented in different orthonormal bases (dimen... | 8f62b2d84c4462256124646b707888ea7b132be9 | 27,054 | ipynb | Jupyter Notebook | Tutorial_GeometricViewOfData.ipynb | neurologic/MotorSystems_BIOL358_SP22 | ddec85c10e2bbc08a24cba0b6ff7b58466172b21 | [
"CC0-1.0"
] | null | null | null | Tutorial_GeometricViewOfData.ipynb | neurologic/MotorSystems_BIOL358_SP22 | ddec85c10e2bbc08a24cba0b6ff7b58466172b21 | [
"CC0-1.0"
] | null | null | null | Tutorial_GeometricViewOfData.ipynb | neurologic/MotorSystems_BIOL358_SP22 | ddec85c10e2bbc08a24cba0b6ff7b58466172b21 | [
"CC0-1.0"
] | null | null | null | 40.621622 | 432 | 0.505249 | true | 4,807 | Qwen/Qwen-72B | 1. YES
2. YES | 0.851953 | 0.76908 | 0.65522 | __label__eng_Latn | 0.9521 | 0.360627 |
```python
import numpy as np
import scipy
import sympy as sym
import pandas as pd
from scipy import linalg
from scipy import optimize
from scipy import interpolate
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
sym.init_printing(use_unicode=True)
```
# 1. Human capita... | 5d1c53b3c6fdf8a60221195ce4cd360fd5ad82f4 | 311,951 | ipynb | Jupyter Notebook | examproject/examproject/examproject.ipynb | NumEconCopenhagen/projects-2019-wp | 01c7730beba383f59efc73a70cebf1fd2b8be301 | [
"MIT"
] | null | null | null | examproject/examproject/examproject.ipynb | NumEconCopenhagen/projects-2019-wp | 01c7730beba383f59efc73a70cebf1fd2b8be301 | [
"MIT"
] | 8 | 2019-04-15T16:23:44.000Z | 2019-05-21T07:35:17.000Z | examproject/examproject/examproject.ipynb | NumEconCopenhagen/projects-2019-wp | 01c7730beba383f59efc73a70cebf1fd2b8be301 | [
"MIT"
] | 2 | 2019-05-12T14:44:57.000Z | 2020-03-15T10:59:04.000Z | 144.288159 | 118,612 | 0.874458 | true | 13,284 | Qwen/Qwen-72B | 1. YES
2. YES | 0.885631 | 0.787931 | 0.697817 | __label__eng_Latn | 0.880847 | 0.459593 |
```python
from sympy.abc import s, t
from sympy.integrals.transforms import inverse_laplace_transform
from cardioLPN import A_R, A_L, A_C
from sympy import symbols
from sympy import *
import matplotlib.pyplot as plt
import numpy as np
```
```python
R_p, R_d, R, L, C = symbols('R_p, R_d R L C', positive=True)
U_2, I... | 4e06f24bb1265ad1ee4be52a7afef99b502539c3 | 47,994 | ipynb | Jupyter Notebook | example_config.ipynb | xi2pi/cardioLPN | 34759fea55f73312ccb8fb645ce2d04a0e2dddea | [
"MIT"
] | null | null | null | example_config.ipynb | xi2pi/cardioLPN | 34759fea55f73312ccb8fb645ce2d04a0e2dddea | [
"MIT"
] | null | null | null | example_config.ipynb | xi2pi/cardioLPN | 34759fea55f73312ccb8fb645ce2d04a0e2dddea | [
"MIT"
] | null | null | null | 155.824675 | 12,564 | 0.85619 | true | 1,105 | Qwen/Qwen-72B | 1. YES
2. YES | 0.952574 | 0.83762 | 0.797895 | __label__eng_Latn | 0.295814 | 0.69211 |
# Diffusion Theory
```python
# Our numerical workhorses
import numpy as np
import pandas as pd
# Import matplotlib stuff for plotting
import matplotlib.pyplot as plt
import matplotlib.cm as cm
# Seaborn, useful for graphics
import seaborn as sns
# favorite Seaborn settings for notebooks
rc={'lines.linewidth': 2, ... | 43fbbcb149e49d6d265a46cae40718345907f890 | 446,869 | ipynb | Jupyter Notebook | code/classic_diffusion/diffusion_theory.ipynb | mrazomej/stat_gen | abafd9ecc63ae8a804c8df5b9658e47cabf951fa | [
"MIT"
] | null | null | null | code/classic_diffusion/diffusion_theory.ipynb | mrazomej/stat_gen | abafd9ecc63ae8a804c8df5b9658e47cabf951fa | [
"MIT"
] | 1 | 2019-03-05T00:17:26.000Z | 2019-03-05T00:17:26.000Z | code/classic_diffusion/diffusion_theory.ipynb | mrazomej/pop_gen | abafd9ecc63ae8a804c8df5b9658e47cabf951fa | [
"MIT"
] | null | null | null | 45.138283 | 201 | 0.517208 | true | 3,634 | Qwen/Qwen-72B | 1. YES
2. YES | 0.907312 | 0.83762 | 0.759983 | __label__eng_Latn | 0.69741 | 0.604027 |
# Lecture 30: Chi-Square, Student's t, Multivariate Normal
## Stat 110, Prof. Joe Blitzstein, Harvard University
----
## $\chi^2$ Distribution
The Chi-square Distribution is denoted as $\chi^2(n)$ or sometimes $\chi_{n}^2$, where $n$ indicates the _degrees of freedom_. It used everywhere (I think you used it befor... | 3b3e7d45c8d7acb41253367cb003e48daf15c98c | 512,148 | ipynb | Jupyter Notebook | Lecture_30.ipynb | abhra-nilIITKgp/stats-110 | 258461cdfbdcf99de5b96bcf5b4af0dd98d48f85 | [
"BSD-3-Clause"
] | 113 | 2016-04-29T07:27:33.000Z | 2022-02-27T18:32:47.000Z | Lecture_30.ipynb | snoop2head/stats-110 | 88d0cc56ede406a584f6ba46368e548010f2b14a | [
"BSD-3-Clause"
] | null | null | null | Lecture_30.ipynb | snoop2head/stats-110 | 88d0cc56ede406a584f6ba46368e548010f2b14a | [
"BSD-3-Clause"
] | 65 | 2016-12-24T02:02:25.000Z | 2022-02-13T13:20:02.000Z | 951.947955 | 155,904 | 0.935657 | true | 5,092 | Qwen/Qwen-72B | 1. YES
2. YES | 0.903294 | 0.868827 | 0.784806 | __label__eng_Latn | 0.819345 | 0.6617 |
# CHEM 1000 - Spring 2022
Prof. Geoffrey Hutchison, University of Pittsburgh
## Graded Homework 3
For this homework, we'll focus on:
- vector arithmetic
- scalar dot product
- vector cross product
- simple operators
---
As a reminder, you do not need to use Python to solve the problems. If you want, you can use othe... | 85e77515445faeadfc1dcd1232af1afe46b105b4 | 17,590 | ipynb | Jupyter Notebook | homework/ps3/ps3.ipynb | ghutchis/chem1000 | 07a7eac20cc04ee9a1bdb98339fbd5653a02a38d | [
"CC-BY-4.0"
] | 12 | 2020-06-23T18:44:37.000Z | 2022-03-14T10:13:05.000Z | homework/ps3/ps3.ipynb | ghutchis/chem1000 | 07a7eac20cc04ee9a1bdb98339fbd5653a02a38d | [
"CC-BY-4.0"
] | null | null | null | homework/ps3/ps3.ipynb | ghutchis/chem1000 | 07a7eac20cc04ee9a1bdb98339fbd5653a02a38d | [
"CC-BY-4.0"
] | 4 | 2021-07-29T10:45:23.000Z | 2021-10-16T09:51:00.000Z | 29.316667 | 1,608 | 0.559409 | true | 1,642 | Qwen/Qwen-72B | 1. YES
2. YES | 0.803174 | 0.73412 | 0.589626 | __label__eng_Latn | 0.948469 | 0.208228 |
```python
%matplotlib inline
```
```python
# Write your imports here
import numpy as np
import math
import matplotlib.pyplot as plt
```
# Basic Algebra Exercise
## Functions, Polynomials, Complex Numbers. Applications of Abstract Algebra
### Problem 1. Polynomial Interpolation
We know that if we have a set of $n$ d... | 37361c9cb790d1b87657fcb38bb915a90930e6c6 | 323,417 | ipynb | Jupyter Notebook | Basic_Algebra/.ipynb_checkpoints/Basic-Algebra-Exercise-checkpoint.ipynb | ivaylokanov/Math_Concepts_for_Developers | 646d4d5de48535c22b9a8fcb624973b917661c5e | [
"MIT"
] | null | null | null | Basic_Algebra/.ipynb_checkpoints/Basic-Algebra-Exercise-checkpoint.ipynb | ivaylokanov/Math_Concepts_for_Developers | 646d4d5de48535c22b9a8fcb624973b917661c5e | [
"MIT"
] | null | null | null | Basic_Algebra/.ipynb_checkpoints/Basic-Algebra-Exercise-checkpoint.ipynb | ivaylokanov/Math_Concepts_for_Developers | 646d4d5de48535c22b9a8fcb624973b917661c5e | [
"MIT"
] | null | null | null | 265.531199 | 24,836 | 0.904275 | true | 6,916 | Qwen/Qwen-72B | 1. YES
2. YES | 0.931463 | 0.879147 | 0.818892 | __label__eng_Latn | 0.996495 | 0.740894 |
# Animating a simple wave
We'll plot at various times a wave $u(x,t)$ that starts as a triangular shape as in Taylor Example 16.1, and then animate it. We can imagine this as simulating a wave on a taut string. Here $u$ is the transverse displacement (i.e., $y$ in our two-dimensional plots). We are not solving the ... | c7c7f5e1fd9a5aa8c4872ece225ae95648b6fdb7 | 744,797 | ipynb | Jupyter Notebook | 2020_week_11/Problem_16.11.ipynb | CLima86/Physics_5300_CDL | d9e8ee0861d408a85b4be3adfc97e98afb4a1149 | [
"MIT"
] | null | null | null | 2020_week_11/Problem_16.11.ipynb | CLima86/Physics_5300_CDL | d9e8ee0861d408a85b4be3adfc97e98afb4a1149 | [
"MIT"
] | null | null | null | 2020_week_11/Problem_16.11.ipynb | CLima86/Physics_5300_CDL | d9e8ee0861d408a85b4be3adfc97e98afb4a1149 | [
"MIT"
] | null | null | null | 90.049208 | 27,628 | 0.815783 | true | 2,300 | Qwen/Qwen-72B | 1. YES
2. YES | 0.896251 | 0.880797 | 0.789416 | __label__eng_Latn | 0.894351 | 0.672409 |
<a href="https://colab.research.google.com/github/julianovale/simulacao_python/blob/master/0008_ex_trem_kronecker_artigo.ipynb" target="_parent"></a>
```
from sympy import I, Matrix, symbols, Symbol, eye
from datetime import datetime
import numpy as np
import pandas as pd
```
```
'''
Rotas
'''
R1 = Matrix([[0,"L1p... | 6099cbb8a5863f1d85338b638115b451c5b9ea87 | 54,797 | ipynb | Jupyter Notebook | 0008_ex_trem_kronecker_artigo.ipynb | julianovale/simulacao_python | 9d29fe05d1580ca46311fc6fb6ab41b1b1c7ca5d | [
"MIT"
] | null | null | null | 0008_ex_trem_kronecker_artigo.ipynb | julianovale/simulacao_python | 9d29fe05d1580ca46311fc6fb6ab41b1b1c7ca5d | [
"MIT"
] | null | null | null | 0008_ex_trem_kronecker_artigo.ipynb | julianovale/simulacao_python | 9d29fe05d1580ca46311fc6fb6ab41b1b1c7ca5d | [
"MIT"
] | null | null | null | 34.57224 | 256 | 0.227458 | true | 9,830 | Qwen/Qwen-72B | 1. YES
2. YES | 0.90053 | 0.672332 | 0.605455 | __label__lmo_Latn | 0.228766 | 0.245004 |
```python
import os
from galgebra_ipython_helpers import check as check_latex, run
os.chdir('../Old Format')
```
```python
run('bad_example')
```
3*e_x + 4*e_y
5
25
3*e_x/5 + 4*e_y/5
3*e_x/25 + 4*e_y/25
1
3*e_x/25 + 4*e_y/25
bad_example.py:4: DeprecationWarning: The `galgebra.deprec... | 72caade86484e5b766085d9f1b96f7036bee8578 | 141,922 | ipynb | Jupyter Notebook | examples/ipython/Old Format.ipynb | waldyrious/galgebra | b5eb070340434d030dd737a5656fbf709538b0b1 | [
"BSD-3-Clause"
] | null | null | null | examples/ipython/Old Format.ipynb | waldyrious/galgebra | b5eb070340434d030dd737a5656fbf709538b0b1 | [
"BSD-3-Clause"
] | null | null | null | examples/ipython/Old Format.ipynb | waldyrious/galgebra | b5eb070340434d030dd737a5656fbf709538b0b1 | [
"BSD-3-Clause"
] | null | null | null | 77.722892 | 1,032 | 0.550218 | true | 49,098 | Qwen/Qwen-72B | 1. YES
2. YES | 0.798187 | 0.644225 | 0.514212 | __label__eng_Latn | 0.112134 | 0.033016 |
```python
# Front matter
import os
import glob
import re
import pandas as pd
import numpy as np
import scipy.constants as constants
import sympy as sp
from sympy import Matrix, Symbol
from sympy.utilities.lambdify import lambdify
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoMinorL... | 61b2cb067d98b926c4332efd1a5a82ff9ce64202 | 245,704 | ipynb | Jupyter Notebook | 010_XRDAnalysis/Check_P_Error_Propagation.ipynb | r-a-morrison/fe_alloy_sound_velocities | 8da1b0d073e93fb4b4be3d61b73e58b7a7a3097b | [
"MIT"
] | null | null | null | 010_XRDAnalysis/Check_P_Error_Propagation.ipynb | r-a-morrison/fe_alloy_sound_velocities | 8da1b0d073e93fb4b4be3d61b73e58b7a7a3097b | [
"MIT"
] | null | null | null | 010_XRDAnalysis/Check_P_Error_Propagation.ipynb | r-a-morrison/fe_alloy_sound_velocities | 8da1b0d073e93fb4b4be3d61b73e58b7a7a3097b | [
"MIT"
] | null | null | null | 542.392936 | 50,176 | 0.929492 | true | 3,460 | Qwen/Qwen-72B | 1. YES
2. YES | 0.855851 | 0.72487 | 0.620381 | __label__eng_Latn | 0.279211 | 0.279684 |
# MACD Analysis and Buy/Sell Signals
> MACD, short for moving average convergence/divergence, is a trading indicator used in technical analysis of stock prices.
Source: https://en.wikipedia.org/wiki/MACD
We analyse stock data using MACD and generate buy and sell signals.
```python
# Parameters for MACD computatio... | 52041c139bd9692820d9db7a6baeacd231b2898b | 241,126 | ipynb | Jupyter Notebook | LoSTanSiBLE/notebooks/MACD_BuySell.ipynb | cdeck3r/LoSTanSiBLE | 1bacee79ed6213b59ca4387f45ac539fb7ac9f16 | [
"MIT"
] | 1 | 2019-07-03T10:05:14.000Z | 2019-07-03T10:05:14.000Z | LoSTanSiBLE/notebooks/MACD_BuySell.ipynb | cdeck3r/LoSTanSiBLE | 1bacee79ed6213b59ca4387f45ac539fb7ac9f16 | [
"MIT"
] | null | null | null | LoSTanSiBLE/notebooks/MACD_BuySell.ipynb | cdeck3r/LoSTanSiBLE | 1bacee79ed6213b59ca4387f45ac539fb7ac9f16 | [
"MIT"
] | null | null | null | 318.108179 | 126,392 | 0.920237 | true | 4,551 | Qwen/Qwen-72B | 1. YES
2. YES | 0.853913 | 0.805632 | 0.68794 | __label__eng_Latn | 0.488516 | 0.436645 |
```python
from logicqubit.logic import *
from cmath import *
import numpy as np
import sympy as sp
import scipy
from random import randrange
from scipy.optimize import *
import matplotlib.pyplot as plt
```
```python
gates = Gates(1)
ID = gates.ID()
X = gates.X()
Y = gates.Y()
Z = gates.Z()
```
```python
III = ID.k... | 1c04d7cae718b046743bf9b2a2c28159b0c81eb7 | 13,842 | ipynb | Jupyter Notebook | vqe_3q.ipynb | clnrp/quantum_machine_learning | 5528a440d230b0613f1bd44a81a2a352441c76e5 | [
"MIT"
] | null | null | null | vqe_3q.ipynb | clnrp/quantum_machine_learning | 5528a440d230b0613f1bd44a81a2a352441c76e5 | [
"MIT"
] | null | null | null | vqe_3q.ipynb | clnrp/quantum_machine_learning | 5528a440d230b0613f1bd44a81a2a352441c76e5 | [
"MIT"
] | null | null | null | 31.316742 | 145 | 0.494148 | true | 3,646 | Qwen/Qwen-72B | 1. YES
2. YES | 0.865224 | 0.695958 | 0.60216 | __label__yue_Hant | 0.114032 | 0.237349 |
$$
\sqrt{2}+\sqrt{3}=\sqrt{\left(\sqrt{2}+\sqrt{3}\right)^2}=\sqrt{2\sqrt{6}+5}
=\sqrt{\sqrt{\left(2\sqrt{6}+5\right)^2}} = \sqrt{\sqrt{20\sqrt{6}+49}}
$$
```python
import sympy as S
S.init_printing()
a = S.sqrt( S.sqrt(49+20*S.sqrt(6)))
a
```
```python
S.sqrtdenest(a)
```
$$
\sqrt{2}+\sqrt{3}=\sqrt{2\sqrt{6}+5... | bfbf37d8ec53794a41d896a7ab159d1e9e10d977 | 11,020 | ipynb | Jupyter Notebook | sympy_sqrtdenest.ipynb | hamukazu/notebook-misc | 1b39d137f99dcf0495dc101f82997e669ff6dead | [
"MIT"
] | null | null | null | sympy_sqrtdenest.ipynb | hamukazu/notebook-misc | 1b39d137f99dcf0495dc101f82997e669ff6dead | [
"MIT"
] | null | null | null | sympy_sqrtdenest.ipynb | hamukazu/notebook-misc | 1b39d137f99dcf0495dc101f82997e669ff6dead | [
"MIT"
] | null | null | null | 72.5 | 2,510 | 0.80971 | true | 242 | Qwen/Qwen-72B | 1. YES
2. YES | 0.950411 | 0.857768 | 0.815232 | __label__azj_Latn | 0.230006 | 0.73239 |
Author: Drishika Nadella
Date: 4th March 2021
```python
import numpy as np
from sympy import *
```
```python
def func(x):
return x*(x-1)
```
```python
def derivative(x, delta):
f_ = (func(x+delta) - func(x))/delta
return f_
```
```python
# Analytical derivative
x = Symbol('x')
y = func(x)
yprime =... | 35813893029e342accb4b028f4437cae4d4d00ca | 3,801 | ipynb | Jupyter Notebook | Week 2/HW2_3.ipynb | drkndl/PH354-IISc | e1b40a1ed11fb1967cfb5204d81ee237df453d39 | [
"MIT"
] | null | null | null | Week 2/HW2_3.ipynb | drkndl/PH354-IISc | e1b40a1ed11fb1967cfb5204d81ee237df453d39 | [
"MIT"
] | null | null | null | Week 2/HW2_3.ipynb | drkndl/PH354-IISc | e1b40a1ed11fb1967cfb5204d81ee237df453d39 | [
"MIT"
] | null | null | null | 17.356164 | 222 | 0.470666 | true | 375 | Qwen/Qwen-72B | 1. YES
2. YES | 0.933431 | 0.919643 | 0.858423 | __label__eng_Latn | 0.79141 | 0.832737 |
# Estimating alcohol content in red wines
* Author: Martin Rožnovják
* Last edited: 2019-02-11
* Organization: Metropolia University of Applied Sciences
## What is this?
This notebook is a school assignment for a course called *Cognitive Systems - Mathematics and Methods*.
Its objective is to conduct linear regress... | 9c2bd69d9657c7aec08e2234d834dc1893a0dd46 | 951,068 | ipynb | Jupyter Notebook | Cognitive_Systems-Mathematics_and_Methods/week04/Roznovjak_Assignment_4-Linear_regression_on_red_wines.ipynb | rozni/uni-ml | 0667c7504927ea3bd1850d118708ea72b4b43430 | [
"MIT"
] | null | null | null | Cognitive_Systems-Mathematics_and_Methods/week04/Roznovjak_Assignment_4-Linear_regression_on_red_wines.ipynb | rozni/uni-ml | 0667c7504927ea3bd1850d118708ea72b4b43430 | [
"MIT"
] | null | null | null | Cognitive_Systems-Mathematics_and_Methods/week04/Roznovjak_Assignment_4-Linear_regression_on_red_wines.ipynb | rozni/uni-ml | 0667c7504927ea3bd1850d118708ea72b4b43430 | [
"MIT"
] | null | null | null | 624.88042 | 128,920 | 0.939818 | true | 6,163 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.793106 | 0.703647 | __label__eng_Latn | 0.679614 | 0.47314 |
```python
import numpy as np
import control
import matplotlib.pyplot as plt # plotting library
from sympy import symbols
from sympy.physics.control.lti import TransferFunction, Feedback, Series
from sympy.physics.control.control_plots import pole_zero_plot, step_response_plot
```
# Equations of motion
The EOM for $... | e73985f108c4f8949bda1d25928faeb549bbeb50 | 86,248 | ipynb | Jupyter Notebook | Jupyter Notebooks/Car_suspension.ipynb | gge0866/MCHE474---Control-Systems | 8b3c6212223d104d098e8f306d46ccbba2b5082f | [
"BSD-3-Clause"
] | null | null | null | Jupyter Notebooks/Car_suspension.ipynb | gge0866/MCHE474---Control-Systems | 8b3c6212223d104d098e8f306d46ccbba2b5082f | [
"BSD-3-Clause"
] | null | null | null | Jupyter Notebooks/Car_suspension.ipynb | gge0866/MCHE474---Control-Systems | 8b3c6212223d104d098e8f306d46ccbba2b5082f | [
"BSD-3-Clause"
] | null | null | null | 223.440415 | 29,624 | 0.917934 | true | 1,363 | Qwen/Qwen-72B | 1. YES
2. YES | 0.882428 | 0.76908 | 0.678658 | __label__eng_Latn | 0.373459 | 0.415081 |
# Bond Pricing with Vasicek Model
Author:<br>
Stanislav Khrapov<br>
<a href="mailto:khrapovs@gmail.com">khrapovs@gmail.com</a><br>
http://sites.google.com/site/khrapovs/<br>
## Introduction
The following code is the example of adapting methodology of<br>
<a href = "http://onlinelibrary.wiley.com/doi/10.1111/1468-026... | baae5af6e65a4ec165129909f6ed9b7b00b44f1b | 173,016 | ipynb | Jupyter Notebook | Vasicek.ipynb | khrapovs/finmetrix-code | f278df1c15a225385846c2f0d7a6700c5737e901 | [
"MIT"
] | 4 | 2015-07-03T16:34:29.000Z | 2019-05-09T13:10:26.000Z | Vasicek.ipynb | khrapovs/finmetrix-code | f278df1c15a225385846c2f0d7a6700c5737e901 | [
"MIT"
] | null | null | null | Vasicek.ipynb | khrapovs/finmetrix-code | f278df1c15a225385846c2f0d7a6700c5737e901 | [
"MIT"
] | 2 | 2016-04-01T05:33:44.000Z | 2020-07-12T06:58:25.000Z | 222.671815 | 54,523 | 0.89431 | true | 3,222 | Qwen/Qwen-72B | 1. YES
2. YES | 0.951863 | 0.849971 | 0.809056 | __label__eng_Latn | 0.597799 | 0.718042 |
---
# Section 3.2: Orthogonal Matrices
---
## Inner-product notation
We will use the following notation for the **inner-product** between vectors $x, y \in \mathbb{R}^n$:
$$
\langle x, y \rangle = \sum_{i=1}^n x_i y_i = x^T y = \|x\|_2 \|y\|_2 \cos\theta,
$$
where $0 \leq \theta \leq \pi$ is the **angle** between $... | 0868e17bff84f605c38891d3566e551015c61ef4 | 54,463 | ipynb | Jupyter Notebook | Section 3.2 - Orthogonal Matrices.ipynb | math434/fall2021math434 | 6317ce76de1eb7dbfdc3ea37a21dc5e1e3228316 | [
"MIT"
] | 1 | 2021-08-31T21:01:22.000Z | 2021-08-31T21:01:22.000Z | Section 3.2 - Orthogonal Matrices.ipynb | math434/fall2021math434 | 6317ce76de1eb7dbfdc3ea37a21dc5e1e3228316 | [
"MIT"
] | null | null | null | Section 3.2 - Orthogonal Matrices.ipynb | math434/fall2021math434 | 6317ce76de1eb7dbfdc3ea37a21dc5e1e3228316 | [
"MIT"
] | 1 | 2021-11-16T19:28:56.000Z | 2021-11-16T19:28:56.000Z | 21.324589 | 334 | 0.424967 | true | 11,090 | Qwen/Qwen-72B | 1. YES
2. YES | 0.879147 | 0.872347 | 0.766921 | __label__eng_Latn | 0.69783 | 0.620148 |
<font size = 12> Calibration Notebook </font>
Author: Leonardo Assis Morais
Calibrate the TES detector using area measurements.
The output of this notebook is a .csv file with the counting thresholds <br>
required to convert TES area information to photon-number information.
Use the notebook Counting Photons.ipy... | 005ca05c7dd497fc9c001ca8af510906f9089877 | 256,655 | ipynb | Jupyter Notebook | Jupyter Notebooks/TES Calibration.ipynb | Leo-am/tespackage | 1e3447951532411eb3596c6dbeaf781c4b006676 | [
"MIT"
] | null | null | null | Jupyter Notebooks/TES Calibration.ipynb | Leo-am/tespackage | 1e3447951532411eb3596c6dbeaf781c4b006676 | [
"MIT"
] | null | null | null | Jupyter Notebooks/TES Calibration.ipynb | Leo-am/tespackage | 1e3447951532411eb3596c6dbeaf781c4b006676 | [
"MIT"
] | null | null | null | 507.22332 | 95,964 | 0.946952 | true | 2,166 | Qwen/Qwen-72B | 1. YES
2. YES | 0.774583 | 0.749087 | 0.580231 | __label__eng_Latn | 0.936751 | 0.1864 |
```python
%config InlineBackend.figure_format = 'retina'
from matplotlib import rcParams
rcParams["savefig.dpi"] = 96
rcParams["figure.dpi"] = 96
```
# The Shin (2015) model
## Introduction
The model proposed by [Shin (2015)](http://dx.doi.org/10.1007/s12665-015-4588-z)
is an equivalent circuit that aims to reprodu... | cebfef328a4048205ef3a4db570cdac5cd94e40b | 566,535 | ipynb | Jupyter Notebook | docs/tutorials/shin.ipynb | clberube/BISIP2 | 810c70bc04cba016b6f3fbe6e2412bd689acf1a8 | [
"MIT"
] | 9 | 2017-04-21T20:17:05.000Z | 2021-12-03T07:06:02.000Z | docs/tutorials/shin.ipynb | clberube/BISIP2 | 810c70bc04cba016b6f3fbe6e2412bd689acf1a8 | [
"MIT"
] | 4 | 2017-09-28T07:06:56.000Z | 2019-08-24T18:36:25.000Z | docs/tutorials/shin.ipynb | clberube/BISIP2 | 810c70bc04cba016b6f3fbe6e2412bd689acf1a8 | [
"MIT"
] | 2 | 2018-03-05T14:52:32.000Z | 2018-03-26T14:19:33.000Z | 1,967.135417 | 465,052 | 0.960906 | true | 735 | Qwen/Qwen-72B | 1. YES
2. YES | 0.843895 | 0.737158 | 0.622084 | __label__eng_Latn | 0.746143 | 0.28364 |
# Modeling the Time Evolution of the Annualized Rate of Public Mass Shootings with Gaussian Processes
Nathan Sanders, Victor Lei (Legendary Entertainment)
January, 2017
## Abstract
Much of the public policy debate over gun control and gun rights in the United States hinges on the alarming incidence of public mass ... | 699caf3d0e6c0e451ae1db5e1c76331752db605a | 872,717 | ipynb | Jupyter Notebook | 2017/Contributed-Talks/09_sanders/Annualized Rate of Mass Shootings; Sanders & Lei (StanCon2017 - revised).ipynb | simeond/stancon_talks | 5a2a94ea056dd3c05c4a0e48532769dc8dc7f9ac | [
"CC-BY-4.0",
"BSD-3-Clause"
] | 238 | 2017-01-23T23:15:19.000Z | 2022-03-06T09:26:49.000Z | 2017/Contributed-Talks/09_sanders/Annualized Rate of Mass Shootings; Sanders & Lei (StanCon2017 - revised).ipynb | simeond/stancon_talks | 5a2a94ea056dd3c05c4a0e48532769dc8dc7f9ac | [
"CC-BY-4.0",
"BSD-3-Clause"
] | 5 | 2017-01-24T01:57:34.000Z | 2020-10-22T23:13:04.000Z | 2017/Contributed-Talks/09_sanders/Annualized Rate of Mass Shootings; Sanders & Lei (StanCon2017 - revised).ipynb | simeond/stancon_talks | 5a2a94ea056dd3c05c4a0e48532769dc8dc7f9ac | [
"CC-BY-4.0",
"BSD-3-Clause"
] | 103 | 2017-01-24T04:10:41.000Z | 2022-02-05T16:39:40.000Z | 481.100882 | 132,450 | 0.922103 | true | 15,594 | Qwen/Qwen-72B | 1. YES
2. YES | 0.909907 | 0.835484 | 0.760212 | __label__eng_Latn | 0.966887 | 0.60456 |
$ \newcommand{\pd}[2]{ \frac{\partial #1}{\partial #2} }
\newcommand{\od}[2]{\frac{d #1}{d #2}}
\newcommand{\td}[2]{\frac{D #1}{D #2}}
\newcommand{\ab}[1]{\langle #1 \rangle}
\newcommand{\bss}[1]{\textsf{\textbf{#1}}}
\newcommand{\ol}{\overline}
\newcommand{\olx}[1]{\overline{#1}^x}
$
# Advection, Diffusion, and Conse... | 788c20239bdecf3b25643397548436de734f5f17 | 208,976 | ipynb | Jupyter Notebook | book/04_advection_diffusion_continuity.ipynb | monocilindro/intro_to_physical_oceanography | 1cd76829d94dcbd13e5e81c923db924ff0798c1b | [
"MIT"
] | 82 | 2015-09-18T02:01:53.000Z | 2022-02-28T01:43:48.000Z | book/04_advection_diffusion_continuity.ipynb | monocilindro/intro_to_physical_oceanography | 1cd76829d94dcbd13e5e81c923db924ff0798c1b | [
"MIT"
] | 5 | 2015-09-19T01:35:28.000Z | 2022-02-28T17:23:53.000Z | book/04_advection_diffusion_continuity.ipynb | monocilindro/intro_to_physical_oceanography | 1cd76829d94dcbd13e5e81c923db924ff0798c1b | [
"MIT"
] | 51 | 2015-09-12T00:30:33.000Z | 2022-02-08T19:37:51.000Z | 408.95499 | 115,524 | 0.928408 | true | 5,205 | Qwen/Qwen-72B | 1. YES
2. YES | 0.867036 | 0.79053 | 0.685418 | __label__eng_Latn | 0.994886 | 0.430787 |
```python
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('apw-notebook.mplstyle')
%matplotlib inline
from scipy.integrate import quad
from scipy.interpolate import interp1d
```
Computers can generate (pseudo)-random, uniformly distributed random numbers (using, e.g., the [Mersenne Twister](https://e... | 700f47d13728e3f9715a91bab4e106d159fb38a3 | 509,117 | ipynb | Jupyter Notebook | notebooks/Sampling from probability distributions.ipynb | adrn/AST542 | 3a633cde68235cae95093e9f080dc0f3429705cd | [
"MIT"
] | 17 | 2017-03-30T20:13:38.000Z | 2021-07-12T00:55:13.000Z | notebooks/Sampling from probability distributions.ipynb | adrn/AST542 | 3a633cde68235cae95093e9f080dc0f3429705cd | [
"MIT"
] | null | null | null | notebooks/Sampling from probability distributions.ipynb | adrn/AST542 | 3a633cde68235cae95093e9f080dc0f3429705cd | [
"MIT"
] | 2 | 2021-05-28T15:26:52.000Z | 2021-07-09T15:34:41.000Z | 641.20529 | 332,976 | 0.938154 | true | 2,581 | Qwen/Qwen-72B | 1. YES
2. YES | 0.867036 | 0.899121 | 0.77957 | __label__eng_Latn | 0.902479 | 0.649536 |
<b>Traçar um esboço do gráfico e obter uma equação da parábola que satisfaça as condições dadas.</b>
<b>25. Vértice: $V(4,-3)$, eixo paralelo ao eixo dos x, passando pelo ponto $P(2,1)$</b>
<b>Se a parábola é paralela ao eixo dos $x$, temos que sua equação é dada por $(y-k)^2 = 2p(x-h)$</b><br><br>
<b>Descobrindo o v... | 5d77878cc93d8801d2f4befa1eba77f2ccba0c40 | 14,612 | ipynb | Jupyter Notebook | Problemas Propostos. Pag. 172 - 175/25.ipynb | mateuschaves/GEOMETRIA-ANALITICA | bc47ece7ebab154e2894226c6d939b7e7f332878 | [
"MIT"
] | 1 | 2020-02-03T16:40:45.000Z | 2020-02-03T16:40:45.000Z | Problemas Propostos. Pag. 172 - 175/25.ipynb | mateuschaves/GEOMETRIA-ANALITICA | bc47ece7ebab154e2894226c6d939b7e7f332878 | [
"MIT"
] | null | null | null | Problemas Propostos. Pag. 172 - 175/25.ipynb | mateuschaves/GEOMETRIA-ANALITICA | bc47ece7ebab154e2894226c6d939b7e7f332878 | [
"MIT"
] | null | null | null | 176.048193 | 12,512 | 0.897208 | true | 453 | Qwen/Qwen-72B | 1. YES
2. YES | 0.948155 | 0.859664 | 0.815094 | __label__por_Latn | 0.977394 | 0.732069 |
# Showcase
This notebooks shows general features of interface. For examples please see:
1. [Quantum Stadium](examples/stadium.ipynb)
2. [Edge states in HgTe](examples/qsh.ipynb)
```python
import sympy
sympy.init_printing(use_latex='mathjax')
```
# Imports discretizer
```python
from discretizer import Discretizer... | 872d508d0c8bada97d533e5dac57d29ebd63df45 | 7,740 | ipynb | Jupyter Notebook | examples/showcase.ipynb | basnijholt/discretizer | 0866107994282c39fd84d712373e7e55fe15cfa2 | [
"BSD-2-Clause"
] | null | null | null | examples/showcase.ipynb | basnijholt/discretizer | 0866107994282c39fd84d712373e7e55fe15cfa2 | [
"BSD-2-Clause"
] | null | null | null | examples/showcase.ipynb | basnijholt/discretizer | 0866107994282c39fd84d712373e7e55fe15cfa2 | [
"BSD-2-Clause"
] | 1 | 2020-04-05T03:08:37.000Z | 2020-04-05T03:08:37.000Z | 26.597938 | 601 | 0.391473 | true | 1,087 | Qwen/Qwen-72B | 1. YES
2. YES | 0.894789 | 0.731059 | 0.654144 | __label__eng_Latn | 0.344946 | 0.358125 |
## Single Index Quantile Regression
Author: @Suoer Xu （Supervised by Prof. J. Zhang)
August 18th, 2019
This is a tutorial on how to use the Single Index Quantile Regression model package. The packages almost identically replicates the profile optimization discussed in Ma and He (2016). See the paper as
> https://pd... | 3aa276d55755dbe5c21989d67575c1287a93c1b3 | 192,144 | ipynb | Jupyter Notebook | Single Index Quantile Regression.ipynb | Topaceminem/SIQ | e20dce1cbae7fb253fbf6c75160f0eba09c6fd8a | [
"Apache-2.0"
] | null | null | null | Single Index Quantile Regression.ipynb | Topaceminem/SIQ | e20dce1cbae7fb253fbf6c75160f0eba09c6fd8a | [
"Apache-2.0"
] | null | null | null | Single Index Quantile Regression.ipynb | Topaceminem/SIQ | e20dce1cbae7fb253fbf6c75160f0eba09c6fd8a | [
"Apache-2.0"
] | null | null | null | 280.912281 | 84,808 | 0.918759 | true | 3,811 | Qwen/Qwen-72B | 1. YES
2. YES | 0.782662 | 0.766294 | 0.599749 | __label__eng_Latn | 0.546431 | 0.231749 |
# Chapter 4
______
## The greatest theorem never told
This chapter focuses on an idea that is always bouncing around our minds, but is rarely made explicit outside books devoted to statistics. In fact, we've been using this simple idea in every example thus far.
### The Law of Large Numbers
Let $Z_i$ be $N$ indep... | 80f48f1e46f421a88134f3c7ff19698d697df2dc | 594,192 | ipynb | Jupyter Notebook | Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC2.ipynb | sandeepmanocha/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | cf47222f1a0afde29ff424162ae65913050de726 | [
"MIT"
] | 74 | 2016-07-22T19:03:32.000Z | 2022-03-24T04:23:28.000Z | Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC2.ipynb | Shzaidi/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | 465e7e17247f9f61a2dc85b6dbcfd1919196e679 | [
"MIT"
] | 7 | 2016-08-02T08:17:15.000Z | 2016-10-03T21:48:59.000Z | Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC2.ipynb | Shzaidi/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | 465e7e17247f9f61a2dc85b6dbcfd1919196e679 | [
"MIT"
] | 39 | 2016-07-23T01:42:20.000Z | 2022-02-11T14:55:26.000Z | 493.105394 | 130,304 | 0.917508 | true | 12,604 | Qwen/Qwen-72B | 1. YES
2. YES | 0.851953 | 0.937211 | 0.798459 | __label__eng_Latn | 0.995106 | 0.693421 |
# 10 Ordinary Differential Equations (ODEs)
[ODE](http://mathworld.wolfram.com/OrdinaryDifferentialEquation.html)s describe many phenomena in physics. They describe the changes of a **dependent variable** $y(t)$ as a function of a **single independent variable** (e.g. $t$ or $x$).
An ODE of **order** $n$
$$
F(t, y^{... | ee38575d21d062f321c3cac87ca57060d8c75bb6 | 247,322 | ipynb | Jupyter Notebook | 10_ODEs/10-ODEs.ipynb | Py4Phy/PHY432-resources | c26d95eaf5c28e25da682a61190e12ad6758a938 | [
"CC-BY-4.0"
] | null | null | null | 10_ODEs/10-ODEs.ipynb | Py4Phy/PHY432-resources | c26d95eaf5c28e25da682a61190e12ad6758a938 | [
"CC-BY-4.0"
] | 1 | 2022-03-03T21:47:56.000Z | 2022-03-03T21:47:56.000Z | 10_ODEs/10-ODEs.ipynb | Py4Phy/PHY432-resources | c26d95eaf5c28e25da682a61190e12ad6758a938 | [
"CC-BY-4.0"
] | null | null | null | 229.85316 | 61,984 | 0.914262 | true | 4,854 | Qwen/Qwen-72B | 1. YES
2. YES | 0.793106 | 0.839734 | 0.665998 | __label__eng_Latn | 0.856038 | 0.385668 |
- - - -
# Mechpy Tutorials
a mechanical engineering toolbox
source code - https://github.com/nagordon/mechpy
documentation - https://nagordon.github.io/mechpy/web/
- - - -
Neal Gordon
2017-02-20
- - - -
## Composite Plate Mechanics with Python
reference: hyer page 584. 617
The motivation behind this ta... | 22d478c344305fe81892507b412ae64daddc2a41 | 44,223 | ipynb | Jupyter Notebook | tutorials/Composite_Plate_Mechanics_with_Python_Theory.ipynb | nagordon/mechpy | aae2315b883f6af7cd90a8451d170744bbf1053a | [
"MIT"
] | 45 | 2017-01-27T04:40:30.000Z | 2021-12-03T03:46:07.000Z | tutorials/Composite_Plate_Mechanics_with_Python_Theory.ipynb | Lunreth/mechpy | aae2315b883f6af7cd90a8451d170744bbf1053a | [
"MIT"
] | 2 | 2016-03-01T00:42:38.000Z | 2020-03-04T15:45:39.000Z | tutorials/Composite_Plate_Mechanics_with_Python_Theory.ipynb | Lunreth/mechpy | aae2315b883f6af7cd90a8451d170744bbf1053a | [
"MIT"
] | 19 | 2016-04-25T14:12:34.000Z | 2021-07-07T17:46:35.000Z | 40.947222 | 1,884 | 0.370961 | true | 6,633 | Qwen/Qwen-72B | 1. YES
2. YES | 0.938124 | 0.810479 | 0.76033 | __label__eng_Latn | 0.6409 | 0.604833 |
# Network Models
Probably the easiest kinds of statistical models for us to think about are the *network models*. These types of models (like the name imples) describe the random processes which you'd find when you're only looking at one network. We can have models which assume all of the nodes connect to each other e... | 0f7d79f8e7fca8afd31c31bd2d58cefa622ce9ba | 1,018,366 | ipynb | Jupyter Notebook | network_machine_learning_in_python/_build/jupyter_execute/representations/ch5/single-network-models.ipynb | Laknath1996/graph-stats-book | 4b10c2f99dbfb5e05a72c98130f8c4338d7c9a21 | [
"MIT"
] | 10 | 2020-09-15T19:09:53.000Z | 2022-03-17T21:24:14.000Z | network_machine_learning_in_python/_build/jupyter_execute/representations/ch5/single-network-models.ipynb | Laknath1996/graph-stats-book | 4b10c2f99dbfb5e05a72c98130f8c4338d7c9a21 | [
"MIT"
] | 30 | 2020-09-15T19:15:11.000Z | 2022-03-10T15:33:24.000Z | network_machine_learning_in_python/_build/jupyter_execute/representations/ch5/single-network-models.ipynb | Laknath1996/graph-stats-book | 4b10c2f99dbfb5e05a72c98130f8c4338d7c9a21 | [
"MIT"
] | 2 | 2021-04-12T05:08:00.000Z | 2021-10-04T09:42:21.000Z | 497.249023 | 101,488 | 0.923547 | true | 27,271 | Qwen/Qwen-72B | 1. YES
2. YES | 0.896251 | 0.903294 | 0.809579 | __label__eng_Latn | 0.997611 | 0.719255 |
# Non-trivial Band Topology and the Chern Number
### Christina Lee
### Category: Graduate
### Topological Physics Series
* [Quantum Anomolous Hall Effect and the Chern Number](../Graduate/Chern-Number.ipynb)
* [SSH Model and the Winding Number](../Graduate/Winding-Number.ipynb)
## Overview
A Chern number tells us wh... | 60e5c242b036542362a33ae42d3d242aca54b8f2 | 972,689 | ipynb | Jupyter Notebook | Graduate/Chern-Number.ipynb | albi3ro/M4 | ccd27d4b8b24861e22fe806ebaecef70915081a8 | [
"MIT"
] | 22 | 2015-11-15T08:47:04.000Z | 2022-02-25T10:47:12.000Z | Graduate/Chern-Number.ipynb | albi3ro/M4 | ccd27d4b8b24861e22fe806ebaecef70915081a8 | [
"MIT"
] | 11 | 2016-02-23T12:18:26.000Z | 2019-09-14T07:14:26.000Z | Graduate/Chern-Number.ipynb | albi3ro/M4 | ccd27d4b8b24861e22fe806ebaecef70915081a8 | [
"MIT"
] | 6 | 2016-02-24T03:08:22.000Z | 2022-03-10T18:57:19.000Z | 1,050.420086 | 215,389 | 0.955452 | true | 4,434 | Qwen/Qwen-72B | 1. YES
2. YES | 0.913677 | 0.828939 | 0.757382 | __label__eng_Latn | 0.913609 | 0.597984 |
### Example 3 , part B: Diffusion for non uniform material properties
In this example we will look at the diffusion equation for non uniform material properties and how to handle second-order derivatives. For this, we will reuse Devito's `.laplace` short-hand expression outlined in the previous example and demonstrat... | 311e1da8a4a3b919849faea87deaeb9516816838 | 955,112 | ipynb | Jupyter Notebook | examples/cfd/03_diffusion_nonuniform.ipynb | kristiantorres/devito | 9357d69448698fd2b7a57be6fbb400058716b532 | [
"MIT"
] | 1 | 2020-01-31T10:35:49.000Z | 2020-01-31T10:35:49.000Z | examples/cfd/03_diffusion_nonuniform.ipynb | kristiantorres/devito | 9357d69448698fd2b7a57be6fbb400058716b532 | [
"MIT"
] | 53 | 2020-11-30T07:50:14.000Z | 2022-03-10T17:06:03.000Z | examples/cfd/03_diffusion_nonuniform.ipynb | kristiantorres/devito | 9357d69448698fd2b7a57be6fbb400058716b532 | [
"MIT"
] | 1 | 2020-06-02T03:31:11.000Z | 2020-06-02T03:31:11.000Z | 2,062.87689 | 168,644 | 0.964415 | true | 1,876 | Qwen/Qwen-72B | 1. YES
2. YES | 0.893309 | 0.927363 | 0.828422 | __label__eng_Latn | 0.952097 | 0.763036 |
# Elektrotechnisch integrieren mit Spulen und Kondensatoren
```python
# Bibliotheken importieren
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('classic')
```
Mathematisch ist das Verhalten von Strom und Spannung an Kondensatoren und Induktivitäten (Spulen) mit Integration bzw. Differentiation (Abl... | 6d811f0868180c156fd498693e717ffeaa64e991 | 26,630 | ipynb | Jupyter Notebook | 03GE2_elektrotechnisch_integrieren_differenzieren.ipynb | johannamay/GE2 | 63958cc1fd0500814aa5f701f84c63f996b28baf | [
"MIT"
] | null | null | null | 03GE2_elektrotechnisch_integrieren_differenzieren.ipynb | johannamay/GE2 | 63958cc1fd0500814aa5f701f84c63f996b28baf | [
"MIT"
] | null | null | null | 03GE2_elektrotechnisch_integrieren_differenzieren.ipynb | johannamay/GE2 | 63958cc1fd0500814aa5f701f84c63f996b28baf | [
"MIT"
] | 3 | 2020-03-14T22:27:31.000Z | 2020-08-20T16:41:48.000Z | 55.711297 | 12,852 | 0.748329 | true | 2,996 | Qwen/Qwen-72B | 1. YES
2. YES | 0.90053 | 0.73412 | 0.661096 | __label__deu_Latn | 0.906835 | 0.37428 |
# Generating the input-output function $P(g\mid R, c)$ for varying repressor copy number $R$.
```python
import pickle
import os
import glob
import datetime
# Our numerical workhorses
import numpy as np
from sympy import mpmath
import scipy.optimize
import scipy.special
import scipy.integrate
import pandas as pd
impo... | 5929e76aed11c9ee0e0c49af4f6a9292f817e2e3 | 15,517 | ipynb | Jupyter Notebook | src/theory/sandbox/generating_input_output_matrix.ipynb | RPGroup-PBoC/chann_cap | f2a826166fc2d47c424951c616c46d497ed74b39 | [
"MIT"
] | 2 | 2020-08-21T04:06:12.000Z | 2022-02-09T07:36:58.000Z | src/theory/sandbox/generating_input_output_matrix.ipynb | RPGroup-PBoC/chann_cap | f2a826166fc2d47c424951c616c46d497ed74b39 | [
"MIT"
] | null | null | null | src/theory/sandbox/generating_input_output_matrix.ipynb | RPGroup-PBoC/chann_cap | f2a826166fc2d47c424951c616c46d497ed74b39 | [
"MIT"
] | 2 | 2020-04-29T17:43:28.000Z | 2020-09-09T00:20:16.000Z | 39.184343 | 418 | 0.552555 | true | 2,775 | Qwen/Qwen-72B | 1. YES
2. YES | 0.743168 | 0.692642 | 0.514749 | __label__eng_Latn | 0.940568 | 0.034264 |
# Implementing Walsh and Haar Transforms Using Python
## Table of Contents
* [Walsh Transform](#Walsh)
* [Introduction](#WalshIntroduction)
* [Python Implementation](#WalshImplementation)
* [Testing](#WalshTesting)
* [Haar Transform](#Haar)
* [Introduction](#HaarIntroduction)
* [Python Implementati... | 901b0b0f78853e41f5a20f2ab469e74ab1b44bc7 | 179,921 | ipynb | Jupyter Notebook | Notebooks_Teoricos/Image-Processing-Operations/04-Implementing-Walsh-Haar-Transform-Using-Python.ipynb | lucas-althoff/PDI-UnB | eae5de886739807bd7f66d5cb9dbe7b541efa4ff | [
"MIT"
] | null | null | null | Notebooks_Teoricos/Image-Processing-Operations/04-Implementing-Walsh-Haar-Transform-Using-Python.ipynb | lucas-althoff/PDI-UnB | eae5de886739807bd7f66d5cb9dbe7b541efa4ff | [
"MIT"
] | null | null | null | Notebooks_Teoricos/Image-Processing-Operations/04-Implementing-Walsh-Haar-Transform-Using-Python.ipynb | lucas-althoff/PDI-UnB | eae5de886739807bd7f66d5cb9dbe7b541efa4ff | [
"MIT"
] | null | null | null | 175.704102 | 67,674 | 0.882471 | true | 4,679 | Qwen/Qwen-72B | 1. YES
2. YES | 0.879147 | 0.903294 | 0.794128 | __label__eng_Latn | 0.82109 | 0.683358 |
---
title: Monte Carlo Integration
summary: working out a variation metric for the IC using monte carlo integration of a toy problem
---
# toy 2D → 3D problem
```python
import numpy as np
import numba
```
### exact solution
Our map is a simple one, from 2D $\boldsymbol{z}$ space to 3D $\boldsymbol{x}$ space.... | e1679e51ebfdc1179f6a11e189046fd69aea1d0d | 6,693 | ipynb | Jupyter Notebook | assets/notebooks/2017-10-28-Monte_Carlo_Integration.ipynb | AllenCellModeling/AllenCellModeling.github.io | fcda8609d4840f5329560524516eab59a1699bc8 | [
"MIT"
] | 9 | 2018-07-21T14:16:23.000Z | 2020-08-10T20:52:55.000Z | assets/notebooks/2017-10-28-Monte_Carlo_Integration.ipynb | AllenCellModeling/AllenCellModeling.github.io | fcda8609d4840f5329560524516eab59a1699bc8 | [
"MIT"
] | 3 | 2018-08-15T17:37:13.000Z | 2020-07-09T08:49:11.000Z | assets/notebooks/2017-10-28-Monte_Carlo_Integration.ipynb | AllenCellModeling/AllenCellModeling.github.io | fcda8609d4840f5329560524516eab59a1699bc8 | [
"MIT"
] | 5 | 2018-08-21T19:44:29.000Z | 2021-03-12T19:43:24.000Z | 32.64878 | 208 | 0.452861 | true | 1,434 | Qwen/Qwen-72B | 1. YES
2. YES | 0.924142 | 0.857768 | 0.792699 | __label__kor_Hang | 0.133353 | 0.680039 |
# 非线性规划 Nonlinear Programming
xyfJASON
## 1 概述
若目标函数或约束条件包含非线性函数，则称这种规划问题是非线性规划问题。
没有通用的算法，各个方法都有自己特定的使用范围。
## 2 算法与代码
使用 `scipy.optimize.minimize`，提供了众多优化方法。
Documentation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
| 方法 | 约束条件 | 使用算法 ... | cea86800769d30a017beaf353bca6aa5701480eb | 9,536 | ipynb | Jupyter Notebook | Mathematical Programming/Nonlinear Programming.ipynb | FinCreWorld/Mathematical-Modeling-with-Python | d5206309bce32f2aa64fe94ab4e8a576add0e628 | [
"MIT"
] | null | null | null | Mathematical Programming/Nonlinear Programming.ipynb | FinCreWorld/Mathematical-Modeling-with-Python | d5206309bce32f2aa64fe94ab4e8a576add0e628 | [
"MIT"
] | 1 | 2021-08-21T09:36:54.000Z | 2021-08-21T09:36:54.000Z | Mathematical Programming/Nonlinear Programming.ipynb | FinCreWorld/Mathematical-Modeling-with-Python | d5206309bce32f2aa64fe94ab4e8a576add0e628 | [
"MIT"
] | 3 | 2021-08-21T09:25:22.000Z | 2021-08-29T12:04:49.000Z | 31.061889 | 178 | 0.369232 | true | 2,422 | Qwen/Qwen-72B | 1. YES
2. YES | 0.861538 | 0.857768 | 0.739 | __label__yue_Hant | 0.37403 | 0.555276 |
###### Content under Creative Commons Attribution license CC-BY 4.0, code under MIT license (c)2014 L.A. Barba, G.F. Forsyth, C. Cooper. Based on [CFDPython](https://github.com/barbagroup/CFDPython), (c)2013 L.A. Barba, also under CC-BY license.
# Space & Time
## Burgers' Equation
Hi there! We have reached the final... | 42ec8836a11109329a82b4121962cac7af8b07b5 | 254,165 | ipynb | Jupyter Notebook | lessons/02_spacetime/02_04_1DBurgers.ipynb | mcarpe/numerical-mooc | 62b3c14c2c56d85d65c6075f2d7eb44266b49c17 | [
"CC-BY-3.0"
] | 748 | 2015-01-04T22:50:56.000Z | 2022-03-30T20:42:16.000Z | lessons/02_spacetime/02_04_1DBurgers.ipynb | mcarpe/numerical-mooc | 62b3c14c2c56d85d65c6075f2d7eb44266b49c17 | [
"CC-BY-3.0"
] | 62 | 2015-02-02T01:06:07.000Z | 2020-11-09T12:27:41.000Z | lessons/02_spacetime/02_04_1DBurgers.ipynb | mcarpe/numerical-mooc | 62b3c14c2c56d85d65c6075f2d7eb44266b49c17 | [
"CC-BY-3.0"
] | 1,270 | 2015-01-02T19:19:52.000Z | 2022-02-27T01:02:44.000Z | 88.590101 | 19,340 | 0.818055 | true | 6,996 | Qwen/Qwen-72B | 1. YES
2. YES | 0.835484 | 0.815232 | 0.681113 | __label__eng_Latn | 0.931911 | 0.420786 |
NYC-TAXI-EDA-FEATURE-ENGINEERING<br>
https://www.kaggle.com/frednavruzov/nyc-taxi-eda-feature-engineering
```python
import pandas as pd
import numpy as np
import sympy
import datetime as dt
import time
from math import *
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
from ipyleaflet i... | 54ec0bf4196309fae8f63c6c072cbfb96ea99959 | 44,314 | ipynb | Jupyter Notebook | individual_dir/KSW/EDA_KSW.ipynb | novdov/dss7b5-nyctaxi | 2f1e538d0a25a9c299310b24564da71e9fe9e689 | [
"MIT"
] | null | null | null | individual_dir/KSW/EDA_KSW.ipynb | novdov/dss7b5-nyctaxi | 2f1e538d0a25a9c299310b24564da71e9fe9e689 | [
"MIT"
] | null | null | null | individual_dir/KSW/EDA_KSW.ipynb | novdov/dss7b5-nyctaxi | 2f1e538d0a25a9c299310b24564da71e9fe9e689 | [
"MIT"
] | null | null | null | 36.989983 | 1,489 | 0.568782 | true | 5,355 | Qwen/Qwen-72B | 1. YES
2. YES | 0.819893 | 0.699254 | 0.573314 | __label__kor_Hang | 0.276167 | 0.170331 |
```python
from IPython.display import Image
```
# 2D Turbulent Hot Free Jet
## Literature
[**"Physical and computational aspects of convective heat transfer"**](http://link.springer.com/book/10.1007%2F978-1-4612-3918-5)
T. CEBECI, P. BRADSHAW, Springer 1984
## Equations for the 2D hot free jet
Applying assumption... | f2021bec78fbd038ec25e61e5b8f29637689e893 | 111,825 | ipynb | Jupyter Notebook | jet.ipynb | chiefenne/jet | 82ae734186ced455b87182676ec714d834ae6b3e | [
"MIT"
] | null | null | null | jet.ipynb | chiefenne/jet | 82ae734186ced455b87182676ec714d834ae6b3e | [
"MIT"
] | null | null | null | jet.ipynb | chiefenne/jet | 82ae734186ced455b87182676ec714d834ae6b3e | [
"MIT"
] | null | null | null | 133.125 | 56,408 | 0.831898 | true | 8,916 | Qwen/Qwen-72B | 1. YES
2. YES | 0.661923 | 0.760651 | 0.503492 | __label__eng_Latn | 0.5154 | 0.00811 |
```
%matplotlib inline
from sympy import var, Matrix, eye, init_printing, roots
init_printing()
```
```
var("a:5")
var("b:5")
var("s");
```
# Formas canónicas observador y observabilidad
Tenemos un sistema de orden $4$ con una representación de estado:
```
Ao = Matrix([[0, 0, 0, -a4], [1, 0, 0, -a3], [0, 1, 0, -a... | 5b03b41dc5f4046f09ea3d528c9c85b4c036c7d5 | 145,650 | ipynb | Jupyter Notebook | IPythonNotebooks/Teoria de Control I/Formas canonicas observador y observabilidad.ipynb | chelizalde/DCA | 34fd4d500117a9c0a75b979b8b0f121c1992b9dc | [
"MIT"
] | null | null | null | IPythonNotebooks/Teoria de Control I/Formas canonicas observador y observabilidad.ipynb | chelizalde/DCA | 34fd4d500117a9c0a75b979b8b0f121c1992b9dc | [
"MIT"
] | null | null | null | IPythonNotebooks/Teoria de Control I/Formas canonicas observador y observabilidad.ipynb | chelizalde/DCA | 34fd4d500117a9c0a75b979b8b0f121c1992b9dc | [
"MIT"
] | 1 | 2021-03-20T12:44:13.000Z | 2021-03-20T12:44:13.000Z | 84.239445 | 21,973 | 0.770717 | true | 1,888 | Qwen/Qwen-72B | 1. YES
2. YES | 0.872347 | 0.865224 | 0.754776 | __label__spa_Latn | 0.786382 | 0.59193 |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W1D5-DimensionalityReduction/student/W1D5_Tutorial3.ipynb" target="_parent"></a>
# Neuromatch Academy: Week 1, Day 5, Tutorial 3
# Dimensionality Reduction and reconstruction
---
In this notebook we'll learn to ... | 9ebfc4e971ae1525060ba467a63777ea8e4c62ee | 382,961 | ipynb | Jupyter Notebook | tutorials/W1D5_DimensionalityReduction/student/W1D5_Tutorial3.ipynb | hyosubkim/course-content | 30370131c42fd3bf4f84c50e9c4eaf19f3193165 | [
"CC-BY-4.0"
] | null | null | null | tutorials/W1D5_DimensionalityReduction/student/W1D5_Tutorial3.ipynb | hyosubkim/course-content | 30370131c42fd3bf4f84c50e9c4eaf19f3193165 | [
"CC-BY-4.0"
] | null | null | null | tutorials/W1D5_DimensionalityReduction/student/W1D5_Tutorial3.ipynb | hyosubkim/course-content | 30370131c42fd3bf4f84c50e9c4eaf19f3193165 | [
"CC-BY-4.0"
] | null | null | null | 263.747245 | 90,056 | 0.914093 | true | 5,284 | Qwen/Qwen-72B | 1. YES
2. YES | 0.817574 | 0.763484 | 0.624205 | __label__eng_Latn | 0.933473 | 0.288568 |
# Content
* Libraries
* Introduction to Problem
* Loading Dataset
* Visualizing Raw Dataset
* Preprocessing
* Visualizing Proprocessed Dataset
* Logistic Regression with numpy
* Forward Propagation
* Backward Propagation
* Complete Propagation
* Combining All Together
* Training
... | f1d43d4ecd64f7bccaaf866ad242283b528d4ed7 | 176,450 | ipynb | Jupyter Notebook | Second Meetup/Binary Classification.ipynb | school-of-ai-rasht-chapter/Meetup-Materials | dd5bfb4b163ab07b83beb7038f062f18ca530f45 | [
"MIT"
] | 9 | 2019-04-03T13:01:17.000Z | 2019-09-09T09:01:00.000Z | Second Meetup/Binary Classification.ipynb | rasht-school-of-ai/Meetup-Materials | ec4074f24f837111cebb604bb7f27c4ec6045784 | [
"MIT"
] | null | null | null | Second Meetup/Binary Classification.ipynb | rasht-school-of-ai/Meetup-Materials | ec4074f24f837111cebb604bb7f27c4ec6045784 | [
"MIT"
] | 2 | 2019-07-25T08:36:57.000Z | 2019-09-19T07:24:55.000Z | 151.070205 | 25,848 | 0.88331 | true | 4,430 | Qwen/Qwen-72B | 1. YES
2. YES | 0.861538 | 0.824462 | 0.710305 | __label__eng_Latn | 0.890863 | 0.488609 |
```python
import numpy as np
import sympy as sm
import scipy as sp
from scipy import optimize
from scipy import interpolate
import matplotlib.pyplot as plt
import ipywidgets as widgets
from mpl_toolkits.mplot3d import Axes3D
%matplotlib inline
```
# 1. Human capital accumulation
Consider a worker living in **two per... | f9964cbc3cbe91d1975f2867aff7deea2289e6e2 | 880,695 | ipynb | Jupyter Notebook | examproject/exam_2019.ipynb | NumEconCopenhagen/projects-2019-bcg | bf3b5074359baf08533f858bf452ade2d56c5122 | [
"MIT"
] | null | null | null | examproject/exam_2019.ipynb | NumEconCopenhagen/projects-2019-bcg | bf3b5074359baf08533f858bf452ade2d56c5122 | [
"MIT"
] | 13 | 2019-04-10T10:40:21.000Z | 2019-05-22T16:03:01.000Z | examproject/exam_2019.ipynb | NumEconCopenhagen/projects-2019-bcg | bf3b5074359baf08533f858bf452ade2d56c5122 | [
"MIT"
] | 1 | 2021-05-13T09:28:29.000Z | 2021-05-13T09:28:29.000Z | 349.204996 | 392,504 | 0.926628 | true | 16,330 | Qwen/Qwen-72B | 1. YES
2. YES | 0.891811 | 0.766294 | 0.683389 | __label__eng_Latn | 0.880808 | 0.426073 |
```python
%matplotlib inline
from sympy import *
from sympy.utilities.lambdify import implemented_function
from sympy.abc import x, y, z
import numpy as np
import matplotlib.pyplot as plt
init_printing(use_unicode=True)
```
```python
r, u, v, c, r_c, u_c, v_c, E, p, r_p, u_p, v_p, e, a, b, q, b_0, b_1, b_2, b_3, q_0,... | 259bde4f381841dbe645239370f086b2871a012f | 7,624 | ipynb | Jupyter Notebook | Smectic/SimplePol.ipynb | brettavedisian/Liquid-Crystals | c7c6eaec594e0de8966408264ca7ee06c2fdb5d3 | [
"MIT"
] | null | null | null | Smectic/SimplePol.ipynb | brettavedisian/Liquid-Crystals | c7c6eaec594e0de8966408264ca7ee06c2fdb5d3 | [
"MIT"
] | null | null | null | Smectic/SimplePol.ipynb | brettavedisian/Liquid-Crystals | c7c6eaec594e0de8966408264ca7ee06c2fdb5d3 | [
"MIT"
] | null | null | null | 41.210811 | 1,734 | 0.692812 | true | 429 | Qwen/Qwen-72B | 1. YES
2. YES | 0.92523 | 0.822189 | 0.760714 | __label__yue_Hant | 0.130743 | 0.605726 |
```python
import math
from sympy import *
init_printing(use_latex='mathjax')
```
# Definitions and Functions
```python
## Define symbols
x, y, z = symbols('mu gamma psi')
### NOTE: THE CODE BELOW IS NOT BEING USED IN THE FINAL EXAMPLE ###
cx, sx = symbols('cos(x) sin(x)')
cy, sy = symbols('cos(y) sin(y)')
cz, sz = ... | 5784ce6662adbfcd846bd55a102536ef870fae50 | 33,925 | ipynb | Jupyter Notebook | MyScripts/042-SymPy-RotationMatrices.ipynb | diegoomataix/Curso_AeroPython | c2cf71a938062bc70dbbf7c2f21e09653fa2cedd | [
"CC-BY-4.0"
] | null | null | null | MyScripts/042-SymPy-RotationMatrices.ipynb | diegoomataix/Curso_AeroPython | c2cf71a938062bc70dbbf7c2f21e09653fa2cedd | [
"CC-BY-4.0"
] | null | null | null | MyScripts/042-SymPy-RotationMatrices.ipynb | diegoomataix/Curso_AeroPython | c2cf71a938062bc70dbbf7c2f21e09653fa2cedd | [
"CC-BY-4.0"
] | null | null | null | 41.676904 | 1,504 | 0.434046 | true | 5,825 | Qwen/Qwen-72B | 1. YES
2. YES | 0.877477 | 0.72487 | 0.636057 | __label__eng_Latn | 0.311995 | 0.316104 |
# Chapter 7 Solvers
```python
from sympy import *
x, y, z = symbols('x y z')
init_printing(use_unicode=True)
```
## 7.1 方程式についての注意
`Sympy`での方程式は`Eq`関数を使う
```python
Eq(x,y)
```
```python
solveset(Eq(x**2, 1), x) #Eq(左辺, 右辺)
```
```python
solveset(Eq(x**2 - 1, 0), x)
```
```python
solveset(x**2 - 1, x) #式 = 0... | 909de5c25a0de570454605cadb2a0ba1c783c4d7 | 37,898 | ipynb | Jupyter Notebook | Chapter7_Solvers.ipynb | hiroyuki827/SymPy_tutorial | 8423ceab49482dc83c90c4cb1d388cad100ced84 | [
"BSD-3-Clause"
] | 9 | 2018-01-02T16:53:11.000Z | 2021-05-05T13:48:49.000Z | Chapter7_Solvers.ipynb | hiroyuki827/SymPy_tutorial | 8423ceab49482dc83c90c4cb1d388cad100ced84 | [
"BSD-3-Clause"
] | 1 | 2018-06-12T03:51:09.000Z | 2018-06-13T08:15:45.000Z | Chapter7_Solvers.ipynb | hiroyuki827/SymPy_tutorial | 8423ceab49482dc83c90c4cb1d388cad100ced84 | [
"BSD-3-Clause"
] | null | null | null | 44.743802 | 2,134 | 0.733733 | true | 1,314 | Qwen/Qwen-72B | 1. YES
2. YES | 0.936285 | 0.831143 | 0.778187 | __label__yue_Hant | 0.709151 | 0.646321 |
## Elements of Machine Learning
We consider ML problems involving data points with real-valued labels $y$, which represent some quantity of interest. We often refer to such ML problems as **regression problems**. In this notebook, we will apply some basic ML methods to solve a simple regression problem. These m... | b859488ba824ea7fff5ba17c28c6564e7076f398 | 233,955 | ipynb | Jupyter Notebook | ComponentsML/PythonNotebook/ElementsofML.ipynb | alexjungaalto/ResearchPublic | 07a6b05f5a5f306aea8a625622f4736274f9f11e | [
"Unlicense"
] | 3 | 2019-11-06T17:43:31.000Z | 2020-04-27T19:20:46.000Z | ComponentsML/PythonNotebook/.ipynb_checkpoints/ElementsofML-checkpoint.ipynb | alexjungaalto/ResearchPublic | 07a6b05f5a5f306aea8a625622f4736274f9f11e | [
"Unlicense"
] | null | null | null | ComponentsML/PythonNotebook/.ipynb_checkpoints/ElementsofML-checkpoint.ipynb | alexjungaalto/ResearchPublic | 07a6b05f5a5f306aea8a625622f4736274f9f11e | [
"Unlicense"
] | 13 | 2019-11-09T01:58:59.000Z | 2021-11-01T11:18:15.000Z | 203.793554 | 32,980 | 0.890646 | true | 8,177 | Qwen/Qwen-72B | 1. YES
2. YES | 0.914901 | 0.709019 | 0.648682 | __label__eng_Latn | 0.97119 | 0.345437 |
```python
import numpy as np
import math as mt
import sympy as sym
```
```python
theta = sym.Symbol('theta')
alpha = sym.Symbol('alpha')
costheta = sym.Symbol('costheta')
cosalpha = sym.Symbol('cosalpha')
sintheta = sym.Symbol('sintheta')
sinalpha = sym.Symbol('sinalpha')
l1 = sym.Symbol('l1')
l2 = sym.Symbol('l2'... | 3e3c5470d5ac7822f268a66bdbf20a2d18647ad0 | 3,733 | ipynb | Jupyter Notebook | Jupyter/HomogeneousTransformations.ipynb | der-coder/CINVESTAV-System-Modeling-2019 | 9345444113885181560940b3b4467c9555b8a85a | [
"MIT"
] | null | null | null | Jupyter/HomogeneousTransformations.ipynb | der-coder/CINVESTAV-System-Modeling-2019 | 9345444113885181560940b3b4467c9555b8a85a | [
"MIT"
] | null | null | null | Jupyter/HomogeneousTransformations.ipynb | der-coder/CINVESTAV-System-Modeling-2019 | 9345444113885181560940b3b4467c9555b8a85a | [
"MIT"
] | null | null | null | 27.448529 | 245 | 0.436914 | true | 541 | Qwen/Qwen-72B | 1. YES
2. YES | 0.903294 | 0.757794 | 0.684511 | __label__kor_Hang | 0.200056 | 0.42868 |
# scqubits example: transmon qubit
J. Koch and P. Groszkowski
For further documentation of scqubits see https://scqubits.readthedocs.io/en/latest/.
---
Set up Matplotlib for plotting into notebook, and import scqubits and numpy:
```python
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
import numpy... | 2985aa8f0633401dccdc596b0b61b646bb4b1a24 | 227,672 | ipynb | Jupyter Notebook | examples/demo_transmon.ipynb | scqubits/scqubits-examples | 83e267b795f3c90de5543a5ca2b1c9d109acd52c | [
"BSD-3-Clause"
] | 8 | 2021-01-25T19:14:31.000Z | 2022-03-12T14:25:34.000Z | examples/demo_transmon.ipynb | scqubits/scqubits-examples | 83e267b795f3c90de5543a5ca2b1c9d109acd52c | [
"BSD-3-Clause"
] | null | null | null | examples/demo_transmon.ipynb | scqubits/scqubits-examples | 83e267b795f3c90de5543a5ca2b1c9d109acd52c | [
"BSD-3-Clause"
] | 9 | 2021-01-12T18:24:59.000Z | 2022-02-25T09:19:50.000Z | 47.830252 | 41,515 | 0.588039 | true | 1,701 | Qwen/Qwen-72B | 1. YES
2. YES | 0.843895 | 0.73412 | 0.61952 | __label__eng_Latn | 0.751779 | 0.277683 |
## Performance Indicator
It is fundamental for any algorithm to measure the performance. In a multi-objective scenario, we can not calculate the distance to the true global optimum but must consider a set of solutions. Moreover, sometimes the optimum is not even known, and other techniques must be used.
First, let u... | 82a055cfeaf67232b420773dfb80481174e19a60 | 45,961 | ipynb | Jupyter Notebook | doc/source/misc/performance_indicator.ipynb | Alaya-in-Matrix/pymoo | 02d6e7085f5fe88dbd56b2a9f5173abe20c54caf | [
"Apache-2.0"
] | 2 | 2021-03-28T03:06:35.000Z | 2021-03-28T03:40:08.000Z | doc/source/misc/performance_indicator.ipynb | Alaya-in-Matrix/pymoo | 02d6e7085f5fe88dbd56b2a9f5173abe20c54caf | [
"Apache-2.0"
] | null | null | null | doc/source/misc/performance_indicator.ipynb | Alaya-in-Matrix/pymoo | 02d6e7085f5fe88dbd56b2a9f5173abe20c54caf | [
"Apache-2.0"
] | 1 | 2022-03-31T08:19:13.000Z | 2022-03-31T08:19:13.000Z | 127.315789 | 36,476 | 0.88029 | true | 1,358 | Qwen/Qwen-72B | 1. YES
2. YES | 0.90053 | 0.880797 | 0.793184 | __label__eng_Latn | 0.97428 | 0.681165 |
###### Content under Creative Commons Attribution license CC-BY 4.0, code under MIT license (c) 2014 F.J.Gonzales. Portions of the code adopted from the #numericalmooc materials, also under CC-BY.
# The French Connec... eh solution
## Solving the 1-D wave equation
Welcome to this bonus notebook that ties into the se... | a3998d5ac27acb91942d852a4aa7f3ae55e42cf7 | 68,747 | ipynb | Jupyter Notebook | Lessons.and.Assignments/1D.Wave.Bar/1D.WaveBar.ipynb | udaypandit/black_scholes | 705c3e47e5fd441c30a38c1ab17a80a75441e7d5 | [
"MIT"
] | 12 | 2015-03-30T13:29:15.000Z | 2018-06-23T03:39:11.000Z | Lessons.and.Assignments/1D.Wave.Bar/1D.WaveBar.ipynb | udaypandit/black_scholes | 705c3e47e5fd441c30a38c1ab17a80a75441e7d5 | [
"MIT"
] | 8 | 2015-01-01T13:55:56.000Z | 2015-01-16T19:46:58.000Z | Lessons.and.Assignments/1D.Wave.Bar/1D.WaveBar.ipynb | udaypandit/black_scholes | 705c3e47e5fd441c30a38c1ab17a80a75441e7d5 | [
"MIT"
] | 32 | 2015-01-10T08:13:13.000Z | 2021-08-10T15:18:26.000Z | 84.87284 | 14,781 | 0.790696 | true | 3,652 | Qwen/Qwen-72B | 1. YES
2. YES | 0.863392 | 0.849971 | 0.733858 | __label__eng_Latn | 0.9549 | 0.54333 |
# Modeling a Ball Channel Pendulum
Below is a video of a simple cardboard pendulum that has a metal ball in a semi-circular channel mounted above the pendulum's rotational joint. It is an interesting dynamic system that can be constructed and experimented with. This system seems to behave like a single degree of freed... | 1ee0d822d6b178493ce0de45b4e96033dec83e51 | 312,147 | ipynb | Jupyter Notebook | notebooks/09-2020/modeling_a_ball_channel_pendulum.ipynb | gbrault/resonance | bf66993a98fbbb857511f83bc072449b98f0b4c2 | [
"MIT"
] | 31 | 2017-11-10T16:44:04.000Z | 2022-01-13T12:22:02.000Z | notebooks/09-2020/modeling_a_ball_channel_pendulum.ipynb | gbrault/resonance | bf66993a98fbbb857511f83bc072449b98f0b4c2 | [
"MIT"
] | 178 | 2017-07-19T20:16:13.000Z | 2020-03-10T04:13:46.000Z | notebooks/09-2020/modeling_a_ball_channel_pendulum.ipynb | gbrault/resonance | bf66993a98fbbb857511f83bc072449b98f0b4c2 | [
"MIT"
] | 12 | 2018-04-05T22:58:43.000Z | 2021-01-14T04:06:26.000Z | 154.451757 | 31,473 | 0.858781 | true | 4,479 | Qwen/Qwen-72B | 1. YES
2. YES | 0.935347 | 0.839734 | 0.785442 | __label__eng_Latn | 0.882258 | 0.663178 |
```python
# Required to load webpages
from IPython.display import IFrame
```
[Table of contents](../toc.ipynb)
# SymPy
* SymPy is a symbolic mathematics library for Python.
* It is a very powerful computer algebra system, which is easy to include in your Python scripts.
* Please find the documentation and a tutor... | 90f20aee25b5ac16277b055a8fb6481697c71f61 | 35,385 | ipynb | Jupyter Notebook | 02_tools-and-packages/04_sympy.ipynb | rico-mix/py-algorithms-4-automotive-engineering | 1da36207aa27f1dbfbd8e829c28de356f2456163 | [
"MIT"
] | null | null | null | 02_tools-and-packages/04_sympy.ipynb | rico-mix/py-algorithms-4-automotive-engineering | 1da36207aa27f1dbfbd8e829c28de356f2456163 | [
"MIT"
] | null | null | null | 02_tools-and-packages/04_sympy.ipynb | rico-mix/py-algorithms-4-automotive-engineering | 1da36207aa27f1dbfbd8e829c28de356f2456163 | [
"MIT"
] | null | null | null | 35.279163 | 16,616 | 0.694249 | true | 1,466 | Qwen/Qwen-72B | 1. YES
2. YES | 0.931463 | 0.857768 | 0.798979 | __label__eng_Latn | 0.831061 | 0.694628 |
# Tutorial: optimal piecewise binning with continuous target
## Basic
To get us started, let's load a well-known dataset from the UCI repository and transform the data into a ``pandas.DataFrame``.
```python
import pandas as pd
from sklearn.datasets import load_boston
```
```python
data = load_boston()
df = pd.Dat... | 9dd9ea6976328fb2d665db7f8d0b616e6a37ea3d | 107,141 | ipynb | Jupyter Notebook | doc/source/tutorials/tutorial_piecewise_continuous.ipynb | jensgk/optbinning | 5ccd892fa4ee0a745ab539cee10a2069b35de6da | [
"Apache-2.0"
] | 207 | 2020-01-23T21:32:59.000Z | 2022-03-30T06:33:21.000Z | doc/source/tutorials/tutorial_piecewise_continuous.ipynb | jensgk/optbinning | 5ccd892fa4ee0a745ab539cee10a2069b35de6da | [
"Apache-2.0"
] | 133 | 2020-01-23T22:14:35.000Z | 2022-03-29T14:05:04.000Z | doc/source/tutorials/tutorial_piecewise_continuous.ipynb | jensgk/optbinning | 5ccd892fa4ee0a745ab539cee10a2069b35de6da | [
"Apache-2.0"
] | 50 | 2020-01-27T15:37:08.000Z | 2022-03-30T06:33:25.000Z | 83.834898 | 23,888 | 0.753633 | true | 6,700 | Qwen/Qwen-72B | 1. YES
2. YES | 0.870597 | 0.831143 | 0.723591 | __label__eng_Latn | 0.255774 | 0.519476 |
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