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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
```python
%matplotlib inline
```
```python
import numpy as np
import matplotlib.pyplot as plt
from sympy.physics.hydrogen import R_nl
from sympy import integrate, oo, var
from numerov import radial_integral
from numerov.basis import generate_basis
```
```python
basis = list(generate_basis(range(4, 7)))
print(basis)... | ea348391ddc3b08b642f78f40f5911ed139adb77 | 46,208 | ipynb | Jupyter Notebook | notebooks/radial integral.ipynb | ad3ller/numerov | 78453cf66d0f300225e507daf9e4aeed5b9af5b9 | [
"BSD-3-Clause"
] | 1 | 2020-12-01T21:01:58.000Z | 2020-12-01T21:01:58.000Z | notebooks/radial integral.ipynb | ad3ller/numerov | 78453cf66d0f300225e507daf9e4aeed5b9af5b9 | [
"BSD-3-Clause"
] | null | null | null | notebooks/radial integral.ipynb | ad3ller/numerov | 78453cf66d0f300225e507daf9e4aeed5b9af5b9 | [
"BSD-3-Clause"
] | null | null | null | 137.52381 | 12,980 | 0.895732 | true | 1,029 | Qwen/Qwen-72B | 1. YES
2. YES | 0.853913 | 0.803174 | 0.68584 | __label__eng_Latn | 0.128863 | 0.431768 |
# PRAKTIKUM 13
`Solusi Persamaan Differensial Biasa (PDB) 2`
1. Runge-Kutta Fehlberg (RKF45)
2. Runge-Kutta untuk Sistem Persamaan Differensial Biasa
3. Persamaan Differensial Biasa (PDB) ordo tinggi (lebih dari 1)
4. PDB dengan Masalah Nilai Batas
1. Metode _Linear-Shooting_
2. Metode Beda-Hingga (_Finite-Dif... | 54b4b418ab811dce36b9064e611b855bcc8fb410 | 23,261 | ipynb | Jupyter Notebook | notebookpraktikum/Praktikum 13.ipynb | mkhoirun-najiboi/metnum.jl | a6e35d04dc277318e32256f9b432264157e9b8f4 | [
"MIT"
] | null | null | null | notebookpraktikum/Praktikum 13.ipynb | mkhoirun-najiboi/metnum.jl | a6e35d04dc277318e32256f9b432264157e9b8f4 | [
"MIT"
] | null | null | null | notebookpraktikum/Praktikum 13.ipynb | mkhoirun-najiboi/metnum.jl | a6e35d04dc277318e32256f9b432264157e9b8f4 | [
"MIT"
] | null | null | null | 31.60462 | 400 | 0.519023 | true | 6,113 | Qwen/Qwen-72B | 1. YES
2. YES | 0.849971 | 0.828939 | 0.704574 | __label__ind_Latn | 0.809685 | 0.475293 |
Function basis methods
======================
```
%matplotlib inline
```
```
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams.update({'font.size': 14})
```
Introduction to function basis methods
--------------------------------------
### Boundary Value Problems
Considerin... | 2f1c866a9bd75e2ba5ef8dfb5ae6df91c3127f09 | 20,557 | ipynb | Jupyter Notebook | Lectures/20 - Function Basis Methods.ipynb | josh-gree/NumericalMethods | 03cb91114b3f5eb1b56916920ad180d371fe5283 | [
"CC-BY-3.0"
] | 76 | 2015-02-12T19:51:52.000Z | 2022-03-26T15:34:11.000Z | Lectures/20 - Function Basis Methods.ipynb | josh-gree/NumericalMethods | 03cb91114b3f5eb1b56916920ad180d371fe5283 | [
"CC-BY-3.0"
] | 2 | 2017-05-24T19:49:52.000Z | 2018-01-23T21:40:42.000Z | Lectures/20 - Function Basis Methods.ipynb | josh-gree/NumericalMethods | 03cb91114b3f5eb1b56916920ad180d371fe5283 | [
"CC-BY-3.0"
] | 41 | 2015-01-05T13:30:47.000Z | 2022-02-15T09:59:39.000Z | 29.621037 | 137 | 0.457217 | true | 3,853 | Qwen/Qwen-72B | 1. YES
2. YES | 0.90599 | 0.859664 | 0.778847 | __label__eng_Latn | 0.935043 | 0.647854 |
# The Euler equations of gas dynamics
This is the first of two notebooks on the Euler equations. In this notebook, we discuss the equations and the structure of the exact solution to the Riemann problem. In [Euler_approximate_solvers.ipynb](Euler_approximate_solvers.ipynb), we will investigate approximate Riemann so... | 2d6a24a0e2e378d842d221ab164a6584d5e93eed | 32,443 | ipynb | Jupyter Notebook | Euler.ipynb | katrinleinweber/riemann_book | 0bd2320765a459249d938c6913cc39339cddb3fb | [
"BSD-3-Clause"
] | null | null | null | Euler.ipynb | katrinleinweber/riemann_book | 0bd2320765a459249d938c6913cc39339cddb3fb | [
"BSD-3-Clause"
] | null | null | null | Euler.ipynb | katrinleinweber/riemann_book | 0bd2320765a459249d938c6913cc39339cddb3fb | [
"BSD-3-Clause"
] | null | null | null | 46.281027 | 2,696 | 0.645501 | true | 7,260 | Qwen/Qwen-72B | 1. YES
2. YES | 0.835484 | 0.798187 | 0.666872 | __label__eng_Latn | 0.989198 | 0.387698 |
# PRAKTIKUM 9
`Turunan Numerik`
<hr style="border:2px solid black"> </hr>
```julia
using Plots
```
# Definisi Limit Turunan dan Hampiran Turunan
Diberikan suatu fungsi $f(x)$. Turunan fungsi $f$ pada suatu titik $x=a$ disimbolkan sebagai $f'(a)$, didefinisikan sebagai :
$$ f'(a)=\lim_{h\to 0} \frac{f(a+h)-f(a)}{h... | 9cb616598b33370d6a466bbba7539805127ae904 | 15,795 | ipynb | Jupyter Notebook | notebookpraktikum/Praktikum 09.ipynb | mkhoirun-najiboi/metnum.jl | a6e35d04dc277318e32256f9b432264157e9b8f4 | [
"MIT"
] | null | null | null | notebookpraktikum/Praktikum 09.ipynb | mkhoirun-najiboi/metnum.jl | a6e35d04dc277318e32256f9b432264157e9b8f4 | [
"MIT"
] | null | null | null | notebookpraktikum/Praktikum 09.ipynb | mkhoirun-najiboi/metnum.jl | a6e35d04dc277318e32256f9b432264157e9b8f4 | [
"MIT"
] | null | null | null | 28.408273 | 414 | 0.513517 | true | 3,909 | Qwen/Qwen-72B | 1. YES
2. YES | 0.901921 | 0.855851 | 0.77191 | __label__ind_Latn | 0.820619 | 0.631738 |
# Solving systems of linear equations
Consider a general system of $m$ linear equations with $n$ unknowns (variables):
$$ a_{11}x_1 + a_{12}x_2 + \cdots + a_{1n}x_n = b_1 \\
a_{21}x_1 + a_{22}x_2 + \cdots + a_{2n}x_n = b_2 \\
\vdots \qquad \qquad \vdots \\
a_{m1}x_1 + a_{m2}x_2 + \cdots + a_{mn}x_n = b_m, $$
whe... | 943e1d728ba73d30017127013b40a49b9400eb02 | 83,951 | ipynb | Jupyter Notebook | mathematics/linear_algebra/Linear_Systems.ipynb | jrper/thebe-test | 554484b1422204a23fe47da41c6dc596a681340f | [
"MIT"
] | null | null | null | mathematics/linear_algebra/Linear_Systems.ipynb | jrper/thebe-test | 554484b1422204a23fe47da41c6dc596a681340f | [
"MIT"
] | null | null | null | mathematics/linear_algebra/Linear_Systems.ipynb | jrper/thebe-test | 554484b1422204a23fe47da41c6dc596a681340f | [
"MIT"
] | null | null | null | 121.492041 | 18,100 | 0.786923 | true | 9,488 | Qwen/Qwen-72B | 1. YES
2. YES | 0.888759 | 0.923039 | 0.820359 | __label__eng_Latn | 0.99147 | 0.744302 |
#### Nguyễn Tiến Dũng
*CTTN Toán Tin - K62*
*20170062*
***Đại học Bách khoa Hà Nội***
---
## Phân phối dừng
Cho ma trận chuyển trạng thái $P$
Giả sử tại thời điểm $t$, $X$ có thể nhận các trạng thái $1, 2, 3,...,N$ với xác suất tương ứng là $\pi_1, \pi_2,..,\pi_N$.
Khi đó $\pi = \{\pi_1, \pi_2,...,\pi_N\}$ là v... | 284772ae734c58cebf1ad0d39a0814789a479eb1 | 31,014 | ipynb | Jupyter Notebook | assignment/A7/A7.ipynb | jurgendn/processviz | 82808a92662962f04c48673c9cf159d7bc904ff7 | [
"BSD-3-Clause"
] | null | null | null | assignment/A7/A7.ipynb | jurgendn/processviz | 82808a92662962f04c48673c9cf159d7bc904ff7 | [
"BSD-3-Clause"
] | null | null | null | assignment/A7/A7.ipynb | jurgendn/processviz | 82808a92662962f04c48673c9cf159d7bc904ff7 | [
"BSD-3-Clause"
] | 2 | 2020-03-19T11:14:13.000Z | 2021-08-14T14:24:08.000Z | 149.826087 | 20,836 | 0.891017 | true | 963 | Qwen/Qwen-72B | 1. YES
2. YES | 0.847968 | 0.749087 | 0.635202 | __label__vie_Latn | 1.000007 | 0.314117 |
```python
import random
%matplotlib inline
import networkx as nx
```
# Chapter 6 Tutorial
Contents:
1. Partitions
2. Modularity
3. Zachary's Karate Club
4. Girvan-Newman clustering algorithm
## 1. Partitions
A **partition** of a graph is a separation of its nodes into disjoint groups. Consider the following graph... | 0a19adc148b7dae16d47e7bc03b5b047b397147d | 610,939 | ipynb | Jupyter Notebook | tutorials/Chapter 6 Tutorial.ipynb | arvidl/FirstCourseNetworkScience | 29516707b98555658bedbeea84c09564d20870e7 | [
"CC-BY-4.0"
] | null | null | null | tutorials/Chapter 6 Tutorial.ipynb | arvidl/FirstCourseNetworkScience | 29516707b98555658bedbeea84c09564d20870e7 | [
"CC-BY-4.0"
] | null | null | null | tutorials/Chapter 6 Tutorial.ipynb | arvidl/FirstCourseNetworkScience | 29516707b98555658bedbeea84c09564d20870e7 | [
"CC-BY-4.0"
] | null | null | null | 345.945074 | 121,788 | 0.932884 | true | 8,282 | Qwen/Qwen-72B | 1. YES
2. YES | 0.826712 | 0.870597 | 0.719733 | __label__eng_Latn | 0.987815 | 0.510513 |
# Computing mass functions, halo biases and concentrations
This notebook illustrates how to compute mass functions, halo biases and concentration-mass relations with CCL, as well as how to translate between different mass definitions.
```python
import numpy as np
import pylab as plt
import pyccl as ccl
%matplotlib in... | 81d6e5b8e0eb642a6b284a72c0ef142508f4a8b4 | 183,537 | ipynb | Jupyter Notebook | Halo-mass-function-example.ipynb | bjornvz/CCLX | dfb0fba4114dea267dec59ebca57870493f43f57 | [
"BSD-3-Clause"
] | 14 | 2019-12-08T11:05:29.000Z | 2022-02-26T19:13:52.000Z | Halo-mass-function-example.ipynb | bjornvz/CCLX | dfb0fba4114dea267dec59ebca57870493f43f57 | [
"BSD-3-Clause"
] | 19 | 2019-11-20T02:17:01.000Z | 2022-03-11T11:40:10.000Z | Halo-mass-function-example.ipynb | bjornvz/CCLX | dfb0fba4114dea267dec59ebca57870493f43f57 | [
"BSD-3-Clause"
] | 7 | 2020-02-14T10:57:19.000Z | 2022-03-28T19:21:20.000Z | 443.326087 | 53,884 | 0.94035 | true | 2,232 | Qwen/Qwen-72B | 1. YES
2. YES | 0.859664 | 0.757794 | 0.651448 | __label__eng_Latn | 0.912282 | 0.351864 |
```python
%load_ext autoreload
%autoreload 2
%matplotlib inline
```
# Naive Baye's Classifier
Naive Baye's classifier is based on Baye's theorem and is used for classification problems. For instance in NLP text classification: topic modeling, sentiment analysis, spam detection etc
## Bayes' theorem:
Partition of s... | f795cb325e7a242b156e4a59a4f5b243ebfc71aa | 33,094 | ipynb | Jupyter Notebook | content/week-12/Naive Bayes.ipynb | GiorgiBeriashvili/school-of-ai | abd033fecf32c1222da097aa8420db6c69b357e6 | [
"Apache-2.0",
"MIT"
] | null | null | null | content/week-12/Naive Bayes.ipynb | GiorgiBeriashvili/school-of-ai | abd033fecf32c1222da097aa8420db6c69b357e6 | [
"Apache-2.0",
"MIT"
] | null | null | null | content/week-12/Naive Bayes.ipynb | GiorgiBeriashvili/school-of-ai | abd033fecf32c1222da097aa8420db6c69b357e6 | [
"Apache-2.0",
"MIT"
] | null | null | null | 25.976452 | 230 | 0.453617 | true | 6,232 | Qwen/Qwen-72B | 1. YES
2. YES | 0.808067 | 0.817574 | 0.660655 | __label__eng_Latn | 0.416425 | 0.373254 |
# Spectral Analysis of Deterministic Signals
*This jupyter notebook is part of a [collection of notebooks](../index.ipynb) on various topics of Digital Signal Processing. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*
## Introduction
The analysis of the... | c81e740d6551808b9fa97c594a79b3caf62089f7 | 154,292 | ipynb | Jupyter Notebook | spectral_analysis_deterministic_signals/leakage_effect.ipynb | swchao/digitalSignalProcessingLecture | 89acae62ea710211014912d61a461ca8a3d6d713 | [
"MIT"
] | null | null | null | spectral_analysis_deterministic_signals/leakage_effect.ipynb | swchao/digitalSignalProcessingLecture | 89acae62ea710211014912d61a461ca8a3d6d713 | [
"MIT"
] | null | null | null | spectral_analysis_deterministic_signals/leakage_effect.ipynb | swchao/digitalSignalProcessingLecture | 89acae62ea710211014912d61a461ca8a3d6d713 | [
"MIT"
] | 1 | 2019-05-09T04:10:31.000Z | 2019-05-09T04:10:31.000Z | 342.871111 | 41,428 | 0.912316 | true | 3,688 | Qwen/Qwen-72B | 1. YES
2. YES | 0.7773 | 0.857768 | 0.666743 | __label__eng_Latn | 0.980012 | 0.387398 |
```python
!pip install pandas
import sympy as sym
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
sym.init_printing()
```
Requirement already satisfied: pandas in c:\users\usuario\.conda\envs\sistdin\lib\site-packages (0.23.4)
Requirement already satisfied: pytz>=2011... | 0f9633d7f5e808e9f403142ad9e3b65715203e68 | 69,645 | ipynb | Jupyter Notebook | .ipynb_checkpoints/04_Series_de_Fourier-checkpoint.ipynb | pierrediazp/Se-ales_y_Sistemas | b14bdaf814b0643589660078ddd39b5cdf86b659 | [
"MIT"
] | null | null | null | .ipynb_checkpoints/04_Series_de_Fourier-checkpoint.ipynb | pierrediazp/Se-ales_y_Sistemas | b14bdaf814b0643589660078ddd39b5cdf86b659 | [
"MIT"
] | null | null | null | .ipynb_checkpoints/04_Series_de_Fourier-checkpoint.ipynb | pierrediazp/Se-ales_y_Sistemas | b14bdaf814b0643589660078ddd39b5cdf86b659 | [
"MIT"
] | null | null | null | 111.969453 | 16,768 | 0.823505 | true | 3,267 | Qwen/Qwen-72B | 1. YES
2. YES | 0.800692 | 0.845942 | 0.677339 | __label__spa_Latn | 0.420127 | 0.412017 |
# Van der Pol oscillator
We will look at the second order differentual equation (see https://en.wikipedia.org/wiki/Van_der_Pol_oscillator):
$$
{d^2y_0 \over dx^2}-\mu(1-y_0^2){dy_0 \over dx}+y_0= 0
$$
```python
from __future__ import division, print_function
import itertools
import numpy as np
import sympy as sp
imp... | e6336ccdcfe2988e4d81ef075dbd84dc2ce922c3 | 5,108 | ipynb | Jupyter Notebook | examples/van_der_pol_interpolation.ipynb | slayoo/pyodesys | 8e1afb195dadf6c6f8e765873bc9dd0fae067c39 | [
"BSD-2-Clause"
] | 82 | 2015-09-29T16:51:03.000Z | 2022-02-02T13:26:50.000Z | examples/van_der_pol_interpolation.ipynb | slayoo/pyodesys | 8e1afb195dadf6c6f8e765873bc9dd0fae067c39 | [
"BSD-2-Clause"
] | 28 | 2015-09-29T14:40:45.000Z | 2021-09-18T19:29:50.000Z | examples/van_der_pol_interpolation.ipynb | slayoo/pyodesys | 8e1afb195dadf6c6f8e765873bc9dd0fae067c39 | [
"BSD-2-Clause"
] | 13 | 2016-03-18T14:00:39.000Z | 2021-09-17T13:54:29.000Z | 25.162562 | 204 | 0.546398 | true | 910 | Qwen/Qwen-72B | 1. YES
2. YES | 0.92944 | 0.896251 | 0.833012 | __label__eng_Latn | 0.641609 | 0.7737 |
# Chapter 3
`Original content created by Cam Davidson-Pilon`
`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) at Quantopian (@quantopian)`
____
## Opening the black box of MCMC
The previous two chapters hid the inner-mechanics of PyMC3, and more generally Markov Chain M... | e3e3185496c0a286d0960cba3329f2fb756070de | 979,447 | ipynb | Jupyter Notebook | Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb | Amirgav/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers-AG | 2ff80f127a0f361a111b9dd65a003f937d1cb67c | [
"MIT"
] | null | null | null | Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb | Amirgav/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers-AG | 2ff80f127a0f361a111b9dd65a003f937d1cb67c | [
"MIT"
] | null | null | null | Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb | Amirgav/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers-AG | 2ff80f127a0f361a111b9dd65a003f937d1cb67c | [
"MIT"
] | null | null | null | 681.59151 | 150,848 | 0.940366 | true | 12,628 | Qwen/Qwen-72B | 1. YES
2. YES | 0.705785 | 0.851953 | 0.601296 | __label__eng_Latn | 0.996529 | 0.235341 |
```python
%%html
<!--Script block to left align Markdown Tables-->
<style>
table {margin-left: 0 !important;}
</style>
```
##### Notes
Leave script block above in place to left justify the table.
This problem can also be used as laboratory exercise in `matplotlib` lesson.
Dependencies: `matplotlib` and `math`; co... | 722aac519ac612352924adafc72965fa2b4c8f33 | 23,630 | ipynb | Jupyter Notebook | 5-ExamProblems/.src/ProblemXX/ProblemXX-Dev-Solution.ipynb | dustykat/engr-1330-psuedo-course | 3e7e31a32a1896fcb1fd82b573daa5248e465a36 | [
"CC0-1.0"
] | null | null | null | 5-ExamProblems/.src/ProblemXX/ProblemXX-Dev-Solution.ipynb | dustykat/engr-1330-psuedo-course | 3e7e31a32a1896fcb1fd82b573daa5248e465a36 | [
"CC0-1.0"
] | null | null | null | 5-ExamProblems/.src/ProblemXX/ProblemXX-Dev-Solution.ipynb | dustykat/engr-1330-psuedo-course | 3e7e31a32a1896fcb1fd82b573daa5248e465a36 | [
"CC0-1.0"
] | null | null | null | 98.458333 | 17,588 | 0.854761 | true | 917 | Qwen/Qwen-72B | 1. YES
2. YES | 0.863392 | 0.831143 | 0.717602 | __label__eng_Latn | 0.960926 | 0.505561 |
# Exercise session nº 5
---
# Furrow Constriction in Animal Cell Cytokinesis
__*Sacha Ichbiah, 21/02/22, ENS Paris*__
This subject is extracted from :
> Hervé Turlier et al., *Furrow Constriction in Animal Cell Cytokinesis*, Biophysical Journal, 2014. \
> https://doi.org/10.1016/j.bpj.2013.11.014
Cytokinesis is the... | 981121f584e2e30ca2961318f5cf173510a6e0ef | 150,520 | ipynb | Jupyter Notebook | Ichbiah/TD_5-Cytokinesis/TD_5_Cytokinesis.ipynb | hturlier/M2ICFP | 1d91ff837b05a6058ee34a03fdc8062893287c6e | [
"MIT"
] | 4 | 2022-02-14T10:17:11.000Z | 2022-03-22T21:16:42.000Z | Ichbiah/TD_5-Cytokinesis/.ipynb_checkpoints/TD_5_Cytokinesis-checkpoint.ipynb | hturlier/M2ICFP | 1d91ff837b05a6058ee34a03fdc8062893287c6e | [
"MIT"
] | null | null | null | Ichbiah/TD_5-Cytokinesis/.ipynb_checkpoints/TD_5_Cytokinesis-checkpoint.ipynb | hturlier/M2ICFP | 1d91ff837b05a6058ee34a03fdc8062893287c6e | [
"MIT"
] | 2 | 2022-01-24T15:08:21.000Z | 2022-02-14T10:17:00.000Z | 235.924765 | 68,560 | 0.897575 | true | 5,457 | Qwen/Qwen-72B | 1. YES
2. YES | 0.828939 | 0.849971 | 0.704574 | __label__eng_Latn | 0.92358 | 0.475293 |
# <center>Applied Stochastic Processes HW02</center>
<center>**11510691 程远$\DeclareMathOperator*{\argmin}{argmin}
\newcommand{\using}[1]{\stackrel{\mathrm{#1}}{=}}
\newcommand{\ffrac}{\displaystyle \frac}
\newcommand{\space}{\text{ }}
\newcommand{\bspace}{\;\;\;\;}
\newcommand{\QQQ}{\boxed{?\:}}
\newcommand{\CB}[1]{\l... | 2a9e71dc9cbf74e45e386d8876986a0f8a1ff29a | 10,934 | ipynb | Jupyter Notebook | Probability and Statistics/Applied Random Process/HW/HW_02.ipynb | XavierOwen/Notes | d262a9103b29ee043aa198b475654aabd7a2818d | [
"MIT"
] | 2 | 2018-11-27T10:31:08.000Z | 2019-01-20T03:11:58.000Z | Probability and Statistics/Applied Random Process/HW/HW_02.ipynb | XavierOwen/Notes | d262a9103b29ee043aa198b475654aabd7a2818d | [
"MIT"
] | null | null | null | Probability and Statistics/Applied Random Process/HW/HW_02.ipynb | XavierOwen/Notes | d262a9103b29ee043aa198b475654aabd7a2818d | [
"MIT"
] | 1 | 2020-07-14T19:57:23.000Z | 2020-07-14T19:57:23.000Z | 45.181818 | 384 | 0.432321 | true | 3,901 | Qwen/Qwen-72B | 1. YES
2. YES | 0.731059 | 0.76908 | 0.562243 | __label__yue_Hant | 0.425233 | 0.144608 |
# Sympy
```python
from sympy import *
# init_printing()
x, y, z = symbols("x y z")
```
```python
simplify(sin(x) ** 2 + cos(x) ** 2)
```
$\displaystyle 1$
```python
expand((x + 1) ** 3)
```
$\displaystyle x^{3} + 3 x^{2} + 3 x + 1$
```python
a = 3
b = 8
c = 2
y = a * x ** 2 + b * x + c
plot(y)
``... | b1fe1ede2a968d43399a6f4d8a8466b3b7a28ba7 | 163,372 | ipynb | Jupyter Notebook | docs/Library/ThirdParty/sympy.ipynb | yoannmos/PythonGuide | b7885f7da4193801e53edc441ecc4de9ee8ea6f7 | [
"MIT"
] | 2 | 2021-09-22T02:29:09.000Z | 2021-09-27T09:44:51.000Z | docs/Library/ThirdParty/sympy.ipynb | yoannmos/PythonGuide | b7885f7da4193801e53edc441ecc4de9ee8ea6f7 | [
"MIT"
] | null | null | null | docs/Library/ThirdParty/sympy.ipynb | yoannmos/PythonGuide | b7885f7da4193801e53edc441ecc4de9ee8ea6f7 | [
"MIT"
] | null | null | null | 781.684211 | 41,542 | 0.729605 | true | 225 | Qwen/Qwen-72B | 1. YES
2. YES | 0.96378 | 0.859664 | 0.828527 | __label__yue_Hant | 0.252912 | 0.763278 |
```python
%matplotlib inline
import numpy as np
import scipy as sc
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import sympy as sp
import itertools
sns.set();
```
```python
def extract(it):
r"""
Extract the values from a iterable of iterables.
The function extracts the... | f9a26b8b2d21caaa8a6e42959e2911e0826a0b3d | 232,668 | ipynb | Jupyter Notebook | notebooks/math/runge_kutta.ipynb | kmyokoyama/machine-learning | 05c41cfa1d2c070ce4f476a20f5ad0c5bd6a1fe7 | [
"MIT"
] | null | null | null | notebooks/math/runge_kutta.ipynb | kmyokoyama/machine-learning | 05c41cfa1d2c070ce4f476a20f5ad0c5bd6a1fe7 | [
"MIT"
] | null | null | null | notebooks/math/runge_kutta.ipynb | kmyokoyama/machine-learning | 05c41cfa1d2c070ce4f476a20f5ad0c5bd6a1fe7 | [
"MIT"
] | null | null | null | 175.731118 | 69,070 | 0.860153 | true | 9,532 | Qwen/Qwen-72B | 1. YES
2. YES | 0.877477 | 0.897695 | 0.787707 | __label__eng_Latn | 0.711373 | 0.668439 |
```python
import grouptesting
from grouptesting.model import *
from grouptesting.algorithms import *
import autograd.numpy as np
from autograd import grad
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import math
from scipy.stats import bernoulli
from scipy.optimize import minimize, rosen, ro... | f971dd125cd5616aaf94fa40093a89e76d3edcf6 | 113,746 | ipynb | Jupyter Notebook | notebooks/.ipynb_checkpoints/Semester Project Plots Gabriel-checkpoint.ipynb | gabrielarpino/grouptesting | 32e48a019ccf099b8672a3e0daba3313dd3b6512 | [
"MIT"
] | null | null | null | notebooks/.ipynb_checkpoints/Semester Project Plots Gabriel-checkpoint.ipynb | gabrielarpino/grouptesting | 32e48a019ccf099b8672a3e0daba3313dd3b6512 | [
"MIT"
] | null | null | null | notebooks/.ipynb_checkpoints/Semester Project Plots Gabriel-checkpoint.ipynb | gabrielarpino/grouptesting | 32e48a019ccf099b8672a3e0daba3313dd3b6512 | [
"MIT"
] | 1 | 2020-10-15T14:11:46.000Z | 2020-10-15T14:11:46.000Z | 164.610709 | 43,728 | 0.858351 | true | 5,648 | Qwen/Qwen-72B | 1. YES
2. YES | 0.803174 | 0.752013 | 0.603997 | __label__eng_Latn | 0.187922 | 0.241617 |
Copyright 2019 Carsten Blank
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dist... | 878f2c0d99ec5d49d8750d23eed0466b2f1490af | 6,399 | ipynb | Jupyter Notebook | notebooks/experiments_paper.ipynb | carstenblank/Quantum-classifier-with-tailored-quantum-kernels---Supplemental | 7c3188f0b71e825bc8ce2b1577a93d10b34abdbc | [
"Apache-2.0"
] | 11 | 2020-02-18T14:14:40.000Z | 2021-10-10T12:19:23.000Z | notebooks/experiments_paper.ipynb | carstenblank/Quantum-classifier-with-tailored-quantum-kernels---Supplemental | 7c3188f0b71e825bc8ce2b1577a93d10b34abdbc | [
"Apache-2.0"
] | null | null | null | notebooks/experiments_paper.ipynb | carstenblank/Quantum-classifier-with-tailored-quantum-kernels---Supplemental | 7c3188f0b71e825bc8ce2b1577a93d10b34abdbc | [
"Apache-2.0"
] | 2 | 2020-07-08T23:17:01.000Z | 2021-09-27T03:13:32.000Z | 25.094118 | 160 | 0.540553 | true | 759 | Qwen/Qwen-72B | 1. YES
2. YES | 0.841826 | 0.76908 | 0.647431 | __label__eng_Latn | 0.594273 | 0.342531 |
## EEML2019: ConvNets and Computer Vision Tutorial (PART II)
### Knowledge distillation: Distilling a pre-trained teacher model into a smaller student model
* Define student model (custom Resnet-21)
* Load pre-trained teacher model (Resnet-50)
* Add KL distillation loss between teacher and student
* Observe the imp... | 9be1927f2e2d03f643518c097c54d4a7de296fd4 | 91,351 | ipynb | Jupyter Notebook | vision/Part2_start.ipynb | EvaBr/PracticalSessions | fc2d6e87a4ea4b0e4eed140f1f36fcd59274051d | [
"MIT"
] | null | null | null | vision/Part2_start.ipynb | EvaBr/PracticalSessions | fc2d6e87a4ea4b0e4eed140f1f36fcd59274051d | [
"MIT"
] | null | null | null | vision/Part2_start.ipynb | EvaBr/PracticalSessions | fc2d6e87a4ea4b0e4eed140f1f36fcd59274051d | [
"MIT"
] | null | null | null | 72.789641 | 49,320 | 0.744644 | true | 5,587 | Qwen/Qwen-72B | 1. YES
2. YES | 0.795658 | 0.73412 | 0.584108 | __label__eng_Latn | 0.860526 | 0.195409 |
### Initialization
#### Notebook stuff
```python
from IPython.display import display, Latex, HTML
display(HTML(open('01.css').read()))
```
#### Numpy and Scipy
```python
import numpy as np
from numpy import array, cos, diag, eye, linspace, pi
from numpy import poly1d, sign, sin, sqrt, where, zeros
from scipy.linal... | 3a584926035e297c67f7c80703bc5b661a72f70b | 375,635 | ipynb | Jupyter Notebook | dati_2017/hw03/01.ipynb | shishitao/boffi_dynamics | 365f16d047fb2dbfc21a2874790f8bef563e0947 | [
"MIT"
] | null | null | null | dati_2017/hw03/01.ipynb | shishitao/boffi_dynamics | 365f16d047fb2dbfc21a2874790f8bef563e0947 | [
"MIT"
] | null | null | null | dati_2017/hw03/01.ipynb | shishitao/boffi_dynamics | 365f16d047fb2dbfc21a2874790f8bef563e0947 | [
"MIT"
] | 2 | 2019-06-23T12:32:39.000Z | 2021-08-15T18:33:55.000Z | 243.12945 | 56,442 | 0.851058 | true | 41,173 | Qwen/Qwen-72B | 1. YES
2. YES | 0.870597 | 0.752013 | 0.6547 | __label__yue_Hant | 0.074375 | 0.359419 |
# ***Introduction to Radar Using Python and MATLAB***
## Andy Harrison - Copyright (C) 2019 Artech House
<br/>
# Bistatic Radar Range Equation
***
The power at the receiving radar for a bistatic configuration is given by (Equation 4.60)
\begin{equation}
P_{radar} = \frac{P_t\, G_t(\theta, \phi)\, G_r(\theta, \... | 9cbef858f25210473133460f36d59efd5b403753 | 48,051 | ipynb | Jupyter Notebook | jupyter/Chapter04/power_at_radar_bistatic.ipynb | mberkanbicer/software | 89f8004f567129216b92c156bbed658a9c03745a | [
"Apache-2.0"
] | null | null | null | jupyter/Chapter04/power_at_radar_bistatic.ipynb | mberkanbicer/software | 89f8004f567129216b92c156bbed658a9c03745a | [
"Apache-2.0"
] | null | null | null | jupyter/Chapter04/power_at_radar_bistatic.ipynb | mberkanbicer/software | 89f8004f567129216b92c156bbed658a9c03745a | [
"Apache-2.0"
] | null | null | null | 177.309963 | 42,424 | 0.916942 | true | 699 | Qwen/Qwen-72B | 1. YES
2. YES | 0.843895 | 0.782662 | 0.660485 | __label__eng_Latn | 0.86846 | 0.372859 |
```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
```
Cuda is not available!
logicqubit version 1.5.8
```python
gates = Gates()
ID = gates.ID()
X = gates.X... | 60f8b38358595bf12ac43e1cc5e4c2ca075175e0 | 24,544 | ipynb | Jupyter Notebook | vqe_4q.ipynb | clnrp/quantum_machine_learning | 5528a440d230b0613f1bd44a81a2a352441c76e5 | [
"MIT"
] | null | null | null | vqe_4q.ipynb | clnrp/quantum_machine_learning | 5528a440d230b0613f1bd44a81a2a352441c76e5 | [
"MIT"
] | null | null | null | vqe_4q.ipynb | clnrp/quantum_machine_learning | 5528a440d230b0613f1bd44a81a2a352441c76e5 | [
"MIT"
] | null | null | null | 38.171073 | 121 | 0.504889 | true | 8,157 | Qwen/Qwen-72B | 1. YES
2. YES | 0.868827 | 0.611382 | 0.531185 | __label__krc_Cyrl | 0.117852 | 0.07245 |
```python
from sympy import init_session
init_session()
```
IPython console for SymPy 1.5.1 (Python 3.6.9-64-bit) (ground types: gmpy)
These commands were executed:
>>> from __future__ import division
>>> from sympy import *
>>> x, y, z, t = symbols('x y z t')
>>> k, m, n = symbols('k m n'... | 2cef6dc1e2835a933e88d966e6d17eeb3dd2aa30 | 111,765 | ipynb | Jupyter Notebook | notebooks/RAC-53_derivatives.ipynb | jeremydavis-2/Jolanta-by-dvr | 025f7392ffc40c12ede2f07efefd1f2b0dcd8d35 | [
"Apache-2.0"
] | null | null | null | notebooks/RAC-53_derivatives.ipynb | jeremydavis-2/Jolanta-by-dvr | 025f7392ffc40c12ede2f07efefd1f2b0dcd8d35 | [
"Apache-2.0"
] | null | null | null | notebooks/RAC-53_derivatives.ipynb | jeremydavis-2/Jolanta-by-dvr | 025f7392ffc40c12ede2f07efefd1f2b0dcd8d35 | [
"Apache-2.0"
] | null | null | null | 232.359667 | 12,572 | 0.873789 | true | 609 | Qwen/Qwen-72B | 1. YES
2. YES | 0.893309 | 0.771844 | 0.689495 | __label__eng_Latn | 0.241324 | 0.440259 |
# Example 2: One-dimensional heat flow (exs2.py)
This example is from the CALFEM manual.
**Purpose:**
Analysis of one-dimensional heat flow.
**Description:**
Consider a wall built up of concrete and thermal insulation. The outdoor
temperature is −17 ◦C and the temperature inside is 20 ◦C. At the inside of
the th... | 0fc6225c93867b95da28cc22b3461c73cce8efca | 6,234 | ipynb | Jupyter Notebook | examples/.ipynb_checkpoints/exs2-checkpoint.ipynb | Karl-Eriksson/calfem-python | e9a88a85d3a73877ec99f7fbd1a296a44c3c9b22 | [
"MIT"
] | 54 | 2016-04-11T19:12:13.000Z | 2022-02-22T07:15:39.000Z | examples/.ipynb_checkpoints/exs2-checkpoint.ipynb | Karl-Eriksson/calfem-python | e9a88a85d3a73877ec99f7fbd1a296a44c3c9b22 | [
"MIT"
] | 13 | 2019-07-01T19:48:38.000Z | 2022-02-11T12:50:02.000Z | examples/.ipynb_checkpoints/exs2-checkpoint.ipynb | Karl-Eriksson/calfem-python | e9a88a85d3a73877ec99f7fbd1a296a44c3c9b22 | [
"MIT"
] | 273 | 2017-08-01T10:29:09.000Z | 2022-02-16T14:02:36.000Z | 22.751825 | 99 | 0.486846 | true | 1,144 | Qwen/Qwen-72B | 1. YES
2. YES | 0.957912 | 0.833325 | 0.798252 | __label__eng_Latn | 0.910963 | 0.692939 |
# Simplified Arm Mode
## Introduction
This notebook presents the analytical derivations of the equations of motion for
three degrees of freedom and nine muscles arm model, some of them being
bi-articular, appropriately constructed to demonstrate both kinematic and
dynamic redundancy (e.g. $d < n < m$). The model is ... | 43e8d935050132554c508013b1b671ac63223bb1 | 333,007 | ipynb | Jupyter Notebook | arm_model/model.ipynb | mitkof6/musculoskeletal-stiffness | 150a43a3d748bb0b630e77cde19ab65df5fb089c | [
"CC-BY-4.0"
] | 4 | 2019-01-24T08:10:20.000Z | 2021-04-04T18:55:02.000Z | arm_model/model.ipynb | mitkof6/musculoskeletal-stiffness | 150a43a3d748bb0b630e77cde19ab65df5fb089c | [
"CC-BY-4.0"
] | null | null | null | arm_model/model.ipynb | mitkof6/musculoskeletal-stiffness | 150a43a3d748bb0b630e77cde19ab65df5fb089c | [
"CC-BY-4.0"
] | null | null | null | 238.372942 | 63,508 | 0.796076 | true | 2,086 | Qwen/Qwen-72B | 1. YES
2. YES | 0.912436 | 0.757794 | 0.691439 | __label__eng_Latn | 0.973864 | 0.444776 |
# Surfinpy
#### Tutorial 3 - Pressure
In the previous tutorials we went through the process of generating a simple phase diagram for bulk phases and introducing temperature dependence for gaseous species. This useful however, sometimes it can be more beneficial to convert the chemical potenials (eVs) to partial pres... | 6dfae0e3aaf696d8750822411b77c7c4ccad3aaa | 362,870 | ipynb | Jupyter Notebook | examples/Notebooks/Bulk/Tutorial_3.ipynb | jstse/SurfinPy | ff3a79f9415c170885e109ab881368271f3dcc19 | [
"MIT"
] | null | null | null | examples/Notebooks/Bulk/Tutorial_3.ipynb | jstse/SurfinPy | ff3a79f9415c170885e109ab881368271f3dcc19 | [
"MIT"
] | null | null | null | examples/Notebooks/Bulk/Tutorial_3.ipynb | jstse/SurfinPy | ff3a79f9415c170885e109ab881368271f3dcc19 | [
"MIT"
] | null | null | null | 1,664.541284 | 170,226 | 0.733207 | true | 847 | Qwen/Qwen-72B | 1. YES
2. YES | 0.805632 | 0.682574 | 0.549903 | __label__eng_Latn | 0.896864 | 0.115939 |
```python
from sympy import init_session
init_session()
```
IPython console for SymPy 1.6 (Python 3.7.3-64-bit) (ground types: python)
These commands were executed:
>>> from __future__ import division
>>> from sympy import *
>>> x, y, z, t = symbols('x y z t')
>>> k, m, n = symbols('k m n'... | 063e84e995e7e206a99f714c053218ecec8f76fc | 28,765 | ipynb | Jupyter Notebook | notebooks/RAC_gradients/RAC-41_derivatives.ipynb | tsommerfeld/L2-methods_for_resonances | acba48bfede415afd99c89ff2859346e1eb4f96c | [
"MIT"
] | null | null | null | notebooks/RAC_gradients/RAC-41_derivatives.ipynb | tsommerfeld/L2-methods_for_resonances | acba48bfede415afd99c89ff2859346e1eb4f96c | [
"MIT"
] | null | null | null | notebooks/RAC_gradients/RAC-41_derivatives.ipynb | tsommerfeld/L2-methods_for_resonances | acba48bfede415afd99c89ff2859346e1eb4f96c | [
"MIT"
] | null | null | null | 112.363281 | 3,928 | 0.811333 | true | 373 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91611 | 0.863392 | 0.790961 | __label__eng_Latn | 0.319896 | 0.676001 |
# *Ab* *initio* molecular dynamics of the vibrational motion of HF
### Part 1: Generation of *ab* *initio* potential energy surfaces (PES)
We are going to construct what is often referred to as an *ab* *initio* potential energy surface of the diatomic
molecule hydrogen fluoride. That is, we are going to use various ... | 2c5be8eacf882f39bdd9f940eb00bb8898179a41 | 163,012 | ipynb | Jupyter Notebook | code/PES_VV_v1.ipynb | MolSSI-Education/ab-initio-md | a749ce15b307603ca8d14fd8927e604ceec47232 | [
"CC-BY-4.0"
] | 1 | 2020-03-09T23:42:46.000Z | 2020-03-09T23:42:46.000Z | code/PES_VV_v1.ipynb | MolSSI-Education/ab-initio-md | a749ce15b307603ca8d14fd8927e604ceec47232 | [
"CC-BY-4.0"
] | 1 | 2019-05-22T18:47:51.000Z | 2019-05-22T18:47:51.000Z | code/PES_VV_v1.ipynb | MolSSI-Education/ab-initio-md | a749ce15b307603ca8d14fd8927e604ceec47232 | [
"CC-BY-4.0"
] | 1 | 2022-02-25T18:36:41.000Z | 2022-02-25T18:36:41.000Z | 178.155191 | 30,300 | 0.888014 | true | 6,960 | Qwen/Qwen-72B | 1. YES
2. YES | 0.888759 | 0.749087 | 0.665758 | __label__eng_Latn | 0.985655 | 0.38511 |
Trusted Notebook" width="500 px" align="left">
# _*Qiskit Aqua: Generating Random Variates*_
The latest version of this notebook is available on https://github.com/Qiskit/qiskit-tutorials.
***
### Contributors
Albert Akhriev<sup>[1]</sup>, Jakub Marecek<sup>[1]</sup>
### Affliation
- <sup>[1]</sup>IBMQ
## Introd... | 10ead679ccee414c0e72f1ae64c3a20c4c9607fe | 89,921 | ipynb | Jupyter Notebook | qiskit/aqua/generating_random_variates.ipynb | sebhofer/qiskit-tutorials | 1efb5977b00345373b4c4d9889c1823859a248c1 | [
"Apache-2.0"
] | 2 | 2021-04-29T15:11:27.000Z | 2021-05-09T20:52:21.000Z | qiskit/aqua/generating_random_variates.ipynb | sebhofer/qiskit-tutorials | 1efb5977b00345373b4c4d9889c1823859a248c1 | [
"Apache-2.0"
] | 1 | 2020-05-08T20:25:11.000Z | 2020-05-08T20:25:11.000Z | qiskit/aqua/generating_random_variates.ipynb | sebhofer/qiskit-tutorials | 1efb5977b00345373b4c4d9889c1823859a248c1 | [
"Apache-2.0"
] | 1 | 2019-09-02T00:35:21.000Z | 2019-09-02T00:35:21.000Z | 189.706751 | 23,744 | 0.889336 | true | 2,943 | Qwen/Qwen-72B | 1. YES
2. YES | 0.782662 | 0.695958 | 0.5447 | __label__eng_Latn | 0.923521 | 0.103851 |
# Simulating Gate Noise
$$
\newcommand{ket}[1]{\left|{#1}\right\rangle}
\newcommand{bra}[1]{\left\langle {#1}\right|}
\newcommand{tr}{\mathrm{Tr}}
$$
## Pure states vs. mixed states
Errors in quantum computing can introduce classical uncertainty in what the underlying state is.
When this happens we sometimes need to ... | 8a797983c88a59711f3a56a7d3e8b26c51e62bdc | 192,190 | ipynb | Jupyter Notebook | notebooks/GateNoiseModels.ipynb | stjordanis/forest-tutorials | 39e99e5804891c4eb7420586fc2b691bb7935ddd | [
"Apache-2.0"
] | 20 | 2020-01-31T03:52:38.000Z | 2022-03-27T16:43:07.000Z | notebooks/GateNoiseModels.ipynb | stjordanis/forest-tutorials | 39e99e5804891c4eb7420586fc2b691bb7935ddd | [
"Apache-2.0"
] | 3 | 2020-02-05T16:23:50.000Z | 2020-11-13T15:47:38.000Z | notebooks/GateNoiseModels.ipynb | stjordanis/forest-tutorials | 39e99e5804891c4eb7420586fc2b691bb7935ddd | [
"Apache-2.0"
] | 17 | 2020-01-30T17:07:38.000Z | 2022-01-17T13:57:49.000Z | 307.504 | 88,184 | 0.915074 | true | 5,792 | Qwen/Qwen-72B | 1. YES
2. YES | 0.880797 | 0.839734 | 0.739635 | __label__eng_Latn | 0.975204 | 0.556752 |
# Aproximação e Interpolação: solução dos problemas propostos
Este notebook apresenta a solução dos problemas propostos na aula 01, Apriximação e Interpolação.
<!-- TEASER_END -->
```julia
using PyPlot
```
```julia
using Polynomials
```
```julia
using BenchmarkTools
```
```julia
struct Lagrange
x::Vector... | ff8f1e6a5a968fa7551b994001d44e72659b7626 | 24,460 | ipynb | Jupyter Notebook | 01-sol-aproximacao.ipynb | pjabardo/sci-comp | 2f590363d0b7edd87d8125494eebac1c346da78d | [
"MIT"
] | null | null | null | 01-sol-aproximacao.ipynb | pjabardo/sci-comp | 2f590363d0b7edd87d8125494eebac1c346da78d | [
"MIT"
] | null | null | null | 01-sol-aproximacao.ipynb | pjabardo/sci-comp | 2f590363d0b7edd87d8125494eebac1c346da78d | [
"MIT"
] | null | null | null | 22.075812 | 208 | 0.434832 | true | 4,965 | Qwen/Qwen-72B | 1. YES
2. YES | 0.831143 | 0.855851 | 0.711335 | __label__por_Latn | 0.302561 | 0.491 |
```python
from sympy import *
# from sympy.abc import *
from IPython.display import display
init_printing()
```
# SymPy
## Symbolic Computation
Free, Open Source, Python
- solve equations - simplify expressions
- compute derivatives, integrals, limits
- work with matrices, - plotting & printing
- code gen - physics -... | e7bce31317466b02f49f03142c3434b88f7fc4e2 | 64,892 | ipynb | Jupyter Notebook | notebooks/python-data-science/sympy/sympy.ipynb | sparkboom/my_jupyter_notes | 9255e4236b27f0419cdd2c8a2159738d8fc383be | [
"MIT"
] | null | null | null | notebooks/python-data-science/sympy/sympy.ipynb | sparkboom/my_jupyter_notes | 9255e4236b27f0419cdd2c8a2159738d8fc383be | [
"MIT"
] | null | null | null | notebooks/python-data-science/sympy/sympy.ipynb | sparkboom/my_jupyter_notes | 9255e4236b27f0419cdd2c8a2159738d8fc383be | [
"MIT"
] | null | null | null | 60.420857 | 6,284 | 0.768816 | true | 2,867 | Qwen/Qwen-72B | 1. YES
2. YES | 0.83762 | 0.882428 | 0.739139 | __label__yue_Hant | 0.487394 | 0.5556 |
# Programming Exercise 5:
# Regularized Linear Regression and Bias vs Variance
## Introduction
In this exercise, you will implement regularized linear regression and use it to study models with different bias-variance properties. Before starting on the programming exercise, we strongly recommend watching the video le... | 6c2ed2e67cb63048d8aae9e0ca89657b9e096e64 | 43,405 | ipynb | Jupyter Notebook | Exercise5/.ipynb_checkpoints/exercise5-checkpoint.ipynb | Ishasharmax/MachineLearningNotebooks | c72693ced6f104d235a40023111562b742d38f4f | [
"Apache-2.0"
] | null | null | null | Exercise5/.ipynb_checkpoints/exercise5-checkpoint.ipynb | Ishasharmax/MachineLearningNotebooks | c72693ced6f104d235a40023111562b742d38f4f | [
"Apache-2.0"
] | null | null | null | Exercise5/.ipynb_checkpoints/exercise5-checkpoint.ipynb | Ishasharmax/MachineLearningNotebooks | c72693ced6f104d235a40023111562b742d38f4f | [
"Apache-2.0"
] | null | null | null | 47.385371 | 664 | 0.602972 | true | 7,892 | Qwen/Qwen-72B | 1. YES
2. YES | 0.774583 | 0.835484 | 0.647152 | __label__eng_Latn | 0.995478 | 0.341881 |
# Thermodynamic Model to predict gene expression.
(c) 2020 Tom Röschinger. This work is licensed under a [Creative Commons Attribution License CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/). All code contained herein is licensed under an [MIT license](https://opensource.org/licenses/MIT).
```python
import ... | d322de482b7e7c22c3853bcb08d2fb36e73c8be6 | 3,892 | ipynb | Jupyter Notebook | code/experimental_design/thermodynamic_model.ipynb | tomroesch/Reg-Seq2 | bd54e7ad226ce0cde90ff80781551383d9a2511d | [
"MIT"
] | null | null | null | code/experimental_design/thermodynamic_model.ipynb | tomroesch/Reg-Seq2 | bd54e7ad226ce0cde90ff80781551383d9a2511d | [
"MIT"
] | null | null | null | code/experimental_design/thermodynamic_model.ipynb | tomroesch/Reg-Seq2 | bd54e7ad226ce0cde90ff80781551383d9a2511d | [
"MIT"
] | null | null | null | 30.40625 | 324 | 0.590185 | true | 594 | Qwen/Qwen-72B | 1. YES
2. YES | 0.877477 | 0.774583 | 0.679679 | __label__eng_Latn | 0.990482 | 0.417453 |
<a href="https://colab.research.google.com/github/jingstat/Customer-Churn-Prediction-for-Digital-Music-Service-with-PySpark/blob/main/ALEX_issue17.ipynb" target="_parent"></a>
# Extend the concentrated liquidity
From the balencor paper, we have the invariance function defined as (1), where $L$ is a constant.
\begi... | c75707de47520cb59bedf5b4b30dfc8a3ce12171 | 51,720 | ipynb | Jupyter Notebook | ALEX_issue17.ipynb | jingstat/Customer-Churn-Prediction-for-Digital-Music-Service-with-PySpark | 3508939c1aee958d36dd6911e634891e92bfdaba | [
"MIT"
] | null | null | null | ALEX_issue17.ipynb | jingstat/Customer-Churn-Prediction-for-Digital-Music-Service-with-PySpark | 3508939c1aee958d36dd6911e634891e92bfdaba | [
"MIT"
] | null | null | null | ALEX_issue17.ipynb | jingstat/Customer-Churn-Prediction-for-Digital-Music-Service-with-PySpark | 3508939c1aee958d36dd6911e634891e92bfdaba | [
"MIT"
] | null | null | null | 140.162602 | 21,578 | 0.841609 | true | 2,071 | Qwen/Qwen-72B | 1. YES
2. YES | 0.877477 | 0.785309 | 0.68909 | __label__eng_Latn | 0.664902 | 0.439318 |
# PC lab 4: Logistic regression for classification
## Introduction
In a binary classification setting, we are interested in assigning an observation $\mathbf{x}$ to one of two possible classes, denoted by $y$. For example, maybe we would like to tell if a patient has a particular disease (y = 1) or not (y = 0), given... | ea750cd7b7d3c45c9444d4422cd7d96a6a4c5cbc | 377,923 | ipynb | Jupyter Notebook | predmod/lab4/PClab04_logreg_SOLVED__.ipynb | gdewael/teaching | a78155041918422a843f31c863dd11e8afc5646a | [
"MIT"
] | null | null | null | predmod/lab4/PClab04_logreg_SOLVED__.ipynb | gdewael/teaching | a78155041918422a843f31c863dd11e8afc5646a | [
"MIT"
] | null | null | null | predmod/lab4/PClab04_logreg_SOLVED__.ipynb | gdewael/teaching | a78155041918422a843f31c863dd11e8afc5646a | [
"MIT"
] | null | null | null | 296.875884 | 129,880 | 0.908873 | true | 11,232 | Qwen/Qwen-72B | 1. YES
2. YES | 0.96378 | 0.831143 | 0.801039 | __label__eng_Latn | 0.906044 | 0.699415 |
# Homework - 1
#####Vectors and Matrices
Consider the matrix X and the vectors y and z below:
$$
\mathbf{X} =
\begin{bmatrix}
2&4 \\
1&3
\end{bmatrix}
$$
$$\mathbf{y} = \begin{bmatrix} 1 \\ 3 \end{bmatrix}$$
$$\mathbf{z} = \begin{bmatrix} 2 \\ 3 \end{bmatrix}$$
**1. What is the inner product of the vector... | e05c8b6d1cb8649cf037333ba6cfbc39747e7a4f | 11,770 | ipynb | Jupyter Notebook | coursework/CMU 10-601/Homework 1.ipynb | mathkann/ML | 65ace09c7327c2625ed176bc7d0e7ad46794218e | [
"MIT"
] | 1 | 2015-08-15T11:16:14.000Z | 2015-08-15T11:16:14.000Z | coursework/CMU 10-601/Homework 1.ipynb | mathkann/ML | 65ace09c7327c2625ed176bc7d0e7ad46794218e | [
"MIT"
] | null | null | null | coursework/CMU 10-601/Homework 1.ipynb | mathkann/ML | 65ace09c7327c2625ed176bc7d0e7ad46794218e | [
"MIT"
] | null | null | null | 31.810811 | 243 | 0.434155 | true | 2,142 | Qwen/Qwen-72B | 1. YES
2. YES | 0.92523 | 0.959154 | 0.887438 | __label__eng_Latn | 0.93346 | 0.90015 |
<a href="https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/pyro_intro.ipynb" target="_parent"></a>
[Pyro](https://pyro.ai/) is a probabilistic programming system built on top of PyTorch. It supports posterior inference based on MCMC and stochastic variational inference; discrete latent var... | 7e0b391aa07c003fcefc56a388f6960fc8c43d78 | 61,852 | ipynb | Jupyter Notebook | notebooks/misc/pyro_intro.ipynb | karm-patel/pyprobml | af8230a0bc0d01bb0f779582d87e5856d25e6211 | [
"MIT"
] | null | null | null | notebooks/misc/pyro_intro.ipynb | karm-patel/pyprobml | af8230a0bc0d01bb0f779582d87e5856d25e6211 | [
"MIT"
] | 1 | 2022-03-27T04:59:50.000Z | 2022-03-27T04:59:50.000Z | notebooks/misc/pyro_intro.ipynb | karm-patel/pyprobml | af8230a0bc0d01bb0f779582d87e5856d25e6211 | [
"MIT"
] | 2 | 2022-03-26T11:52:36.000Z | 2022-03-27T05:17:48.000Z | 69.10838 | 22,352 | 0.800297 | true | 3,706 | Qwen/Qwen-72B | 1. YES
2. YES | 0.787931 | 0.853913 | 0.672824 | __label__eng_Latn | 0.282645 | 0.401528 |
```python
import semicon
import sympy
sympy.init_printing()
```
```python
model = semicon.models.ZincBlende(
components=['foreman', 'zeeman'],
bands=['gamma_6c'],
default_databank='winkler',
)
```
```python
model.hamiltonian
```
$$\left[\begin{matrix}\frac{B_{z} g_{c}}{2} \mu_{B} + E_{0} + E_{v} + ... | 2ea43d15edb0b1b364753de77d1ffc1dff32c8f5 | 80,286 | ipynb | Jupyter Notebook | notebooks/hamiltonian_and_bulk_bands.ipynb | quantum-tinkerer/semicon | 3b4fc8c3f9a25553fc181a4cb9e5e4109c59a5e2 | [
"BSD-2-Clause"
] | null | null | null | notebooks/hamiltonian_and_bulk_bands.ipynb | quantum-tinkerer/semicon | 3b4fc8c3f9a25553fc181a4cb9e5e4109c59a5e2 | [
"BSD-2-Clause"
] | null | null | null | notebooks/hamiltonian_and_bulk_bands.ipynb | quantum-tinkerer/semicon | 3b4fc8c3f9a25553fc181a4cb9e5e4109c59a5e2 | [
"BSD-2-Clause"
] | 1 | 2019-12-30T00:29:36.000Z | 2019-12-30T00:29:36.000Z | 376.929577 | 74,028 | 0.92315 | true | 907 | Qwen/Qwen-72B | 1. YES
2. YES | 0.870597 | 0.692642 | 0.603012 | __label__yue_Hant | 0.118153 | 0.23933 |
## Breakeven Analysis: 3D Printing vs. Injection Molding ##
Detemine the breakeven point when comparing the production of the plastic enclosure for the SomniCloud
- __Given__: Enclosure volume is $2.57 in^3$
```python
part_vol = 2.57 # in^3
```
### 3D Printing Specs ###
- \$4.25 / cubic inch of ABS
- Tooling Cost... | 5318f29c8da0a7954804928595f7af7ed97554f7 | 43,279 | ipynb | Jupyter Notebook | Breakeven Analysis.ipynb | jrmcclure/MAE3501 | f36fac38184001f6b530250a960d5fdbaf9b00ec | [
"MIT"
] | null | null | null | Breakeven Analysis.ipynb | jrmcclure/MAE3501 | f36fac38184001f6b530250a960d5fdbaf9b00ec | [
"MIT"
] | null | null | null | Breakeven Analysis.ipynb | jrmcclure/MAE3501 | f36fac38184001f6b530250a960d5fdbaf9b00ec | [
"MIT"
] | null | null | null | 139.160772 | 35,722 | 0.871832 | true | 1,264 | Qwen/Qwen-72B | 1. YES
2. YES | 0.924142 | 0.808067 | 0.746769 | __label__eng_Latn | 0.227813 | 0.573326 |
<table>
<tr align=left><td>
<td>Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Kyle T. Mandli</td>
</table>
Note: The presentation below largely follows part II in "Finite Difference Methods for Ordinary and Partial Differential ... | 67dd2054501bdb1418616fd85c86eedf9c360232 | 108,080 | ipynb | Jupyter Notebook | 09_ODE_ivp_part1.ipynb | mspieg/intro-numerical-methods | d267a075c95acfed6bbcbe91951a05539be61311 | [
"CC-BY-4.0"
] | 6 | 2020-09-10T13:01:06.000Z | 2022-01-20T15:05:30.000Z | 09_ODE_ivp_part1.ipynb | AinsleyChen/intro-numerical-methods | 2eda74cccbed5c0d4c57e24c3f4c96a1aa741f08 | [
"CC-BY-4.0"
] | null | null | null | 09_ODE_ivp_part1.ipynb | AinsleyChen/intro-numerical-methods | 2eda74cccbed5c0d4c57e24c3f4c96a1aa741f08 | [
"CC-BY-4.0"
] | 35 | 2020-01-21T16:08:37.000Z | 2022-01-21T12:46:56.000Z | 31.093211 | 428 | 0.509974 | true | 24,573 | Qwen/Qwen-72B | 1. YES
2. YES | 0.782662 | 0.865224 | 0.677178 | __label__eng_Latn | 0.663918 | 0.411644 |
# 14 Linear Algebra: Singular Value Decomposition
One can always decompose a matrix $\mathsf{A}$
\begin{gather}
\mathsf{A} = \mathsf{U}\,\text{diag}(w_j)\,\mathsf{V}^{T}\\
\mathsf{U}^T \mathsf{U} = \mathsf{U} \mathsf{U}^T = 1\\
\mathsf{V}^T \mathsf{V} = \mathsf{V} \mathsf{V}^T = 1
\end{gather}
where $\mathsf{U}$ an... | eaaa04ee7940cdcc3fbcdcc367abb9f4698fac47 | 162,710 | ipynb | Jupyter Notebook | 14_linear_algebra/14_SVD.ipynb | ASU-CompMethodsPhysics-PHY494/PHY494-resources-2019 | e6114b49d28df887abe37c8144df8f4ae8cf6419 | [
"CC-BY-4.0"
] | null | null | null | 14_linear_algebra/14_SVD.ipynb | ASU-CompMethodsPhysics-PHY494/PHY494-resources-2019 | e6114b49d28df887abe37c8144df8f4ae8cf6419 | [
"CC-BY-4.0"
] | null | null | null | 14_linear_algebra/14_SVD.ipynb | ASU-CompMethodsPhysics-PHY494/PHY494-resources-2019 | e6114b49d28df887abe37c8144df8f4ae8cf6419 | [
"CC-BY-4.0"
] | null | null | null | 83.100102 | 43,968 | 0.830508 | true | 7,251 | Qwen/Qwen-72B | 1. YES
2. YES | 0.933431 | 0.835484 | 0.779866 | __label__eng_Latn | 0.912166 | 0.650223 |
# Scenario C - Peak Number Variation (results evaluation)
This file is used to evaluate the inference (numerical) results.
The model used in the inference of the parameters is formulated as follows:
\begin{equation}
\large y = f(x) = \sum\limits_{m=1}^M \big[A_m \cdot e^{-\frac{(x-\mu_m)^2}{2\cdot\sigma_m^2}}\big] ... | af2a6f7c576739be1a15ff5a00cb7117ae748654 | 341,245 | ipynb | Jupyter Notebook | code/scenarios/scenario_c/scenario_peaks_evaluation.ipynb | jnispen/PPSDA | 910261551dd08768a72ab0a3e81bd73c706a143a | [
"MIT"
] | 1 | 2021-01-07T02:22:25.000Z | 2021-01-07T02:22:25.000Z | code/scenarios/scenario_c/scenario_peaks_evaluation.ipynb | jnispen/PPSDA | 910261551dd08768a72ab0a3e81bd73c706a143a | [
"MIT"
] | null | null | null | code/scenarios/scenario_c/scenario_peaks_evaluation.ipynb | jnispen/PPSDA | 910261551dd08768a72ab0a3e81bd73c706a143a | [
"MIT"
] | null | null | null | 932.363388 | 58,920 | 0.95401 | true | 819 | Qwen/Qwen-72B | 1. YES
2. YES | 0.83762 | 0.661923 | 0.55444 | __label__eng_Latn | 0.549705 | 0.126479 |
```python
from sympy import *
x, C, D = symbols('x C D')
i, j = symbols('i j', integer=True, positive=True)
psi_i = (1-x)**(i+1)
psi_j = psi_i.subs(i, j)
integrand = diff(psi_i, x)*diff(psi_j, x)
integrand = simplify(integrand)
A_ij = integrate(integrand, (x, 0, 1))
A_ij = simplify(A_ij)
print(('A_ij:', A_ij))
f = 2
b_... | 8044911c94c9a92e2eedfcd0bb0b35a728f8cc5d | 2,544 | ipynb | Jupyter Notebook | Data Science and Machine Learning/Machine-Learning-In-Python-THOROUGH/EXAMPLES/FINITE_ELEMENTS/INTRO/SRC/38_U_XX_2_CD.ipynb | okara83/Becoming-a-Data-Scientist | f09a15f7f239b96b77a2f080c403b2f3e95c9650 | [
"MIT"
] | null | null | null | Data Science and Machine Learning/Machine-Learning-In-Python-THOROUGH/EXAMPLES/FINITE_ELEMENTS/INTRO/SRC/38_U_XX_2_CD.ipynb | okara83/Becoming-a-Data-Scientist | f09a15f7f239b96b77a2f080c403b2f3e95c9650 | [
"MIT"
] | null | null | null | Data Science and Machine Learning/Machine-Learning-In-Python-THOROUGH/EXAMPLES/FINITE_ELEMENTS/INTRO/SRC/38_U_XX_2_CD.ipynb | okara83/Becoming-a-Data-Scientist | f09a15f7f239b96b77a2f080c403b2f3e95c9650 | [
"MIT"
] | 2 | 2022-02-09T15:41:33.000Z | 2022-02-11T07:47:40.000Z | 25.69697 | 69 | 0.411557 | true | 587 | Qwen/Qwen-72B | 1. YES
2. YES | 0.938124 | 0.828939 | 0.777647 | __label__glg_Latn | 0.176586 | 0.645068 |
```python
import numpy as np
import pandas as pd
from scipy.sparse import coo_matrix, eye
import networkx as nx
import matplotlib.pyplot as plt
import graphblas
from graphblas import Matrix, Vector, Scalar
from graphblas import descriptor
from graphblas import unary, binary, monoid, semiring, op
from graphblas import i... | dcb0c8b86e24e257304cc7fe98348e73521b86ce | 164,665 | ipynb | Jupyter Notebook | notebooks/Connected Components -- FastSV.ipynb | ParticularMiner/grblas | f5cfae47f68aa9b8e7c82c364e8eb16c0051b409 | [
"Apache-2.0"
] | null | null | null | notebooks/Connected Components -- FastSV.ipynb | ParticularMiner/grblas | f5cfae47f68aa9b8e7c82c364e8eb16c0051b409 | [
"Apache-2.0"
] | null | null | null | notebooks/Connected Components -- FastSV.ipynb | ParticularMiner/grblas | f5cfae47f68aa9b8e7c82c364e8eb16c0051b409 | [
"Apache-2.0"
] | null | null | null | 49.271394 | 13,836 | 0.637349 | true | 14,715 | Qwen/Qwen-72B | 1. YES
2. YES | 0.868827 | 0.879147 | 0.763826 | __label__eng_Latn | 0.342345 | 0.612956 |
# Classification Problem (Assignment 5, TAO, Spring 2019)
### Instructor: Dr. Pawan Kumar
## Classification Problem
### Given a set of input vectors corresponding to objects (or featues) decide which of the N classes the object belogs to.
### Reference (some figures for illustration below are taken from this):
1.... | 40125c279c02455b81a704f05d8bbc5791e69b49 | 27,140 | ipynb | Jupyter Notebook | .ipynb_checkpoints/Assignment-5-Question-checkpoint.ipynb | keshavbnsl102/TAO-SVM-assignment | d451f4a9a6e942ea8c6b2b9425c6e3c200cd225d | [
"Apache-2.0"
] | null | null | null | .ipynb_checkpoints/Assignment-5-Question-checkpoint.ipynb | keshavbnsl102/TAO-SVM-assignment | d451f4a9a6e942ea8c6b2b9425c6e3c200cd225d | [
"Apache-2.0"
] | null | null | null | .ipynb_checkpoints/Assignment-5-Question-checkpoint.ipynb | keshavbnsl102/TAO-SVM-assignment | d451f4a9a6e942ea8c6b2b9425c6e3c200cd225d | [
"Apache-2.0"
] | null | null | null | 34.310999 | 467 | 0.526013 | true | 5,423 | Qwen/Qwen-72B | 1. YES
2. YES | 0.843895 | 0.880797 | 0.7433 | __label__eng_Latn | 0.959482 | 0.565268 |
jacobian, hessian
```python
% matplotlib inline
import sympy as sy
import math
sy.init_printing(use_latex='mathjax')
import matplotlib as mpl
style_name = 'bmh' #bmh
mpl.style.use(style_name)
np.set_printoptions(precision=4, linewidth =150)
style = plt.style.library[style_name]
style_colors = [ c['color'] for c ... | f4f3317a0a8aaf7f85c48af0f0b42d82820a9fc6 | 238,917 | ipynb | Jupyter Notebook | 02_optimazation/optimization (Kino).ipynb | seokyeongheo/study-math-with-python | 18266dc137e46ea299cbd89241e474d7fd610122 | [
"MIT"
] | null | null | null | 02_optimazation/optimization (Kino).ipynb | seokyeongheo/study-math-with-python | 18266dc137e46ea299cbd89241e474d7fd610122 | [
"MIT"
] | null | null | null | 02_optimazation/optimization (Kino).ipynb | seokyeongheo/study-math-with-python | 18266dc137e46ea299cbd89241e474d7fd610122 | [
"MIT"
] | 1 | 2018-06-07T05:57:02.000Z | 2018-06-07T05:57:02.000Z | 575.703614 | 117,772 | 0.93905 | true | 2,843 | Qwen/Qwen-72B | 1. YES
2. YES | 0.909907 | 0.845942 | 0.769729 | __label__kor_Hang | 0.787748 | 0.626671 |
# Lecture 10 - Priors on Function Spaces: Gaussian Processes
## Objectives:
+ Express prior knowledge/beliefs about model outputs using Gaussian process (GP)
+ Sample functions from the probability measure defined by GP
## Readings:
Please read the following before lecture:
+ [Chapter 1 from C.E. Rasmussen's tex... | 1e636a3d3c563a03153d39e8b62073ffea2ca507 | 128,467 | ipynb | Jupyter Notebook | handouts/handout_10.ipynb | FKShi/uq-course | f8b01ce87472abaed29fa87754816b3b1dd7c353 | [
"MIT"
] | 1 | 2022-02-20T16:32:35.000Z | 2022-02-20T16:32:35.000Z | handouts/handout_10.ipynb | FKShi/uq-course | f8b01ce87472abaed29fa87754816b3b1dd7c353 | [
"MIT"
] | null | null | null | handouts/handout_10.ipynb | FKShi/uq-course | f8b01ce87472abaed29fa87754816b3b1dd7c353 | [
"MIT"
] | null | null | null | 162.616456 | 21,740 | 0.868021 | true | 5,711 | Qwen/Qwen-72B | 1. YES
2. YES | 0.795658 | 0.828939 | 0.659552 | __label__eng_Latn | 0.986861 | 0.370691 |
# Using physics informed neural networks (PINNs) to solve parabolic PDEs
In this notebook, we illustrate physics informed neural networks (PINNs) to solve partial differential equations (PDEs) as proposed in
- Maziar Raissi, Paris Perdikaris, George Em Karniadakis. *Physics Informed Deep Learning (Part I): Data-driv... | b03c552032359a3d40f07b7e8a11d914c62b756e | 59,466 | ipynb | Jupyter Notebook | PINN_Solver.ipynb | hinofafa/PDESolveByNN | a0a8fc61e5d3003db344ad406e17c7c61534d6dd | [
"MIT"
] | 52 | 2021-02-24T08:29:18.000Z | 2022-03-31T07:18:39.000Z | PINN_Solver.ipynb | hinofafa/DeepPDELearner | a0a8fc61e5d3003db344ad406e17c7c61534d6dd | [
"MIT"
] | 1 | 2021-09-28T21:35:03.000Z | 2022-02-28T13:38:06.000Z | PINN_Solver.ipynb | hinofafa/DeepPDELearner | a0a8fc61e5d3003db344ad406e17c7c61534d6dd | [
"MIT"
] | 29 | 2021-02-24T15:51:30.000Z | 2022-03-12T20:42:50.000Z | 35.166174 | 355 | 0.534995 | true | 11,327 | Qwen/Qwen-72B | 1. YES
2. YES | 0.933431 | 0.754915 | 0.704661 | __label__eng_Latn | 0.880711 | 0.475495 |
# Inner problem
This notebook will use Dedalus to create a minimal working example of the solution to the inner problem:
\begin{align}
(\Gamma - \partial_{x}^2) u &= 0
\end{align}
where the penalty mask $\Gamma$ satisfies
\begin{align}
x &\to +\infty & \Gamma &\to 0\\
x &\to -\infty & \Gamma &\to 1
\end{align}
and the... | e55b61a6d872e824bffb560966fad832dbea7932 | 5,028 | ipynb | Jupyter Notebook | inner-problem.ipynb | ericwhester/volume-penalty-code | 66a1745daeae2ad71bda0bc9299c8b8271a9871f | [
"MIT"
] | 4 | 2020-03-14T19:40:40.000Z | 2022-03-18T03:02:33.000Z | inner-problem.ipynb | ericwhester/volume-penalty-code | 66a1745daeae2ad71bda0bc9299c8b8271a9871f | [
"MIT"
] | null | null | null | inner-problem.ipynb | ericwhester/volume-penalty-code | 66a1745daeae2ad71bda0bc9299c8b8271a9871f | [
"MIT"
] | null | null | null | 26.1875 | 235 | 0.551512 | true | 705 | Qwen/Qwen-72B | 1. YES
2. YES | 0.877477 | 0.819893 | 0.719437 | __label__eng_Latn | 0.731946 | 0.509826 |
```python
import numpy as np #Importa libreria numerica
import sympy as sym #simbolica
import matplotlib.pyplot as plt #importa matplotlib solo pyplot
import matplotlib.image as mpimg
from sympy.plotting import plot #para plotear 2 variables
from sympy.plotting import plot3d # para 3
from sympy.plotting import plo... | d75574b77024ca6ea2ffeca249126017230ab368 | 97,099 | ipynb | Jupyter Notebook | python/1/LAB1_EJ_2.ipynb | WayraLHD/SRA21 | 1b0447bf925678b8065c28b2767906d1daff2023 | [
"Apache-2.0"
] | 1 | 2021-09-29T16:38:53.000Z | 2021-09-29T16:38:53.000Z | python/1/LAB1_EJ_2.ipynb | WayraLHD/SRA21 | 1b0447bf925678b8065c28b2767906d1daff2023 | [
"Apache-2.0"
] | 1 | 2021-08-10T08:24:57.000Z | 2021-08-10T08:24:57.000Z | python/1/LAB1_EJ_2.ipynb | WayraLHD/SRA21 | 1b0447bf925678b8065c28b2767906d1daff2023 | [
"Apache-2.0"
] | null | null | null | 321.519868 | 89,000 | 0.92003 | true | 1,709 | Qwen/Qwen-72B | 1. YES
2. YES | 0.894789 | 0.817574 | 0.731557 | __label__kor_Hang | 0.112779 | 0.537984 |
<h1>INTERPOLATIONS</h1>
<b>Group 8</b> <br>
Gardyan Priangga Akbar (2301902296)
<h2>When do we need interpolation?</h2>
Interpolation is drawing conclusions from within a set of known information. For example, if we know that 0 is the lowest number and 10 being the maximum, we can determine that the number 5 must li... | 05e284f868b813b89b435e979697f4b6b1583279 | 108,450 | ipynb | Jupyter Notebook | Interpolation.ipynb | GiantSweetroll/Computational-Math-Interpolation | 1945649b1be4a9814deed6ab81ebd510841cd11b | [
"Apache-2.0"
] | null | null | null | Interpolation.ipynb | GiantSweetroll/Computational-Math-Interpolation | 1945649b1be4a9814deed6ab81ebd510841cd11b | [
"Apache-2.0"
] | null | null | null | Interpolation.ipynb | GiantSweetroll/Computational-Math-Interpolation | 1945649b1be4a9814deed6ab81ebd510841cd11b | [
"Apache-2.0"
] | null | null | null | 74.84472 | 14,812 | 0.808953 | true | 5,812 | Qwen/Qwen-72B | 1. YES
2. YES | 0.914901 | 0.891811 | 0.815919 | __label__eng_Latn | 0.972604 | 0.733986 |
# Introduction
This Notebook gives a quick overview of the capabilities of the visiualisation module.
- Easy drawing of plain geometric primitives (rectangles, circles)
- Easy access to interactions (useful for kinematic visualisation)
- Easy access to animation generation (time-dependent functions and configuration ... | 8a61e6ad657d0deb6958e8347b9c9d617ee99f3e | 175,728 | ipynb | Jupyter Notebook | docs/demo_notebooks/demo_visualisation.ipynb | Xabo-RB/symbtools | d7c771319bc5929ce4bfda09c74c6845749f0c3e | [
"BSD-3-Clause"
] | 5 | 2017-10-15T16:25:01.000Z | 2022-02-27T19:05:04.000Z | docs/demo_notebooks/demo_visualisation.ipynb | Xabo-RB/symbtools | d7c771319bc5929ce4bfda09c74c6845749f0c3e | [
"BSD-3-Clause"
] | 5 | 2019-07-16T13:09:17.000Z | 2021-12-21T20:10:16.000Z | docs/demo_notebooks/demo_visualisation.ipynb | Xabo-RB/symbtools | d7c771319bc5929ce4bfda09c74c6845749f0c3e | [
"BSD-3-Clause"
] | 9 | 2017-02-08T12:24:10.000Z | 2022-02-27T19:22:29.000Z | 382.849673 | 70,940 | 0.938428 | true | 1,731 | Qwen/Qwen-72B | 1. YES
2. YES | 0.853913 | 0.774583 | 0.661427 | __label__eng_Latn | 0.247219 | 0.375047 |
<a href="https://colab.research.google.com/github/charmerDark/quantum_svm_vqc_comparison/blob/main/hons_vqc.ipynb" target="_parent"></a>
```python
!pip install qiskit
```
Collecting qiskit
Downloading https://files.pythonhosted.org/packages/ab/05/b9f82e569f1d4c39cd856c2fd04c716999b0e7a7a395a7fd2b1c48d40e68... | 861767dc3350ce6a72b226f49884916c57cade2a | 42,950 | ipynb | Jupyter Notebook | hons_vqc.ipynb | charmerDark/quantum_svm_vqc_comparison | a30fc4b739b05508c7ec764da705130d56d118d6 | [
"MIT"
] | null | null | null | hons_vqc.ipynb | charmerDark/quantum_svm_vqc_comparison | a30fc4b739b05508c7ec764da705130d56d118d6 | [
"MIT"
] | null | null | null | hons_vqc.ipynb | charmerDark/quantum_svm_vqc_comparison | a30fc4b739b05508c7ec764da705130d56d118d6 | [
"MIT"
] | null | null | null | 60.492958 | 399 | 0.502957 | true | 9,907 | Qwen/Qwen-72B | 1. YES
2. YES | 0.795658 | 0.705785 | 0.561564 | __label__eng_Latn | 0.209693 | 0.14303 |
# Homework 5
## Due Date: Tuesday, October 3rd at 11:59 PM
# Problem 1
We discussed documentation and testing in lecture and also briefly touched on code coverage. You must write tests for your code for your final project (and in life). There is a nice way to automate the testing process called continuous integrati... | 266b9866d69d3b74f653b64ba8fc0a014626a898 | 55,305 | ipynb | Jupyter Notebook | homeworks/HW5/HW5-final.ipynb | xuwd11/cs207_Weidong_Xu | 00442657239c7a4040501bf7fa0f6697c731fe94 | [
"MIT"
] | null | null | null | homeworks/HW5/HW5-final.ipynb | xuwd11/cs207_Weidong_Xu | 00442657239c7a4040501bf7fa0f6697c731fe94 | [
"MIT"
] | null | null | null | homeworks/HW5/HW5-final.ipynb | xuwd11/cs207_Weidong_Xu | 00442657239c7a4040501bf7fa0f6697c731fe94 | [
"MIT"
] | null | null | null | 37.571332 | 424 | 0.511183 | true | 12,292 | Qwen/Qwen-72B | 1. YES
2. YES | 0.774583 | 0.896251 | 0.694221 | __label__eng_Latn | 0.955191 | 0.45124 |
# CBE 60553, Fall 2017, Homework 1
## Problem 1: Choose your path wisely
A particular system has the equation of state $U = \frac{5}{2} PV + C$, where $C$ is an undetermined constant.
### 1. The system starts at state $A$, in which $P={0.2}\ {MPa}$ and $V = {0.01}\ {m^{3}}$. It is taken quasistatically along the pat... | 09ba226fb7f5ab67369df9ef6d53345330cb5407 | 206,783 | ipynb | Jupyter Notebook | 02_thermo/.ipynb_checkpoints/HW1-Fa17-soln-checkpoint.ipynb | DPotoyan/Statmech4ChemBio | bc38f04545e1f64848d09c390caad7b54ba3adfd | [
"MIT"
] | 3 | 2021-04-11T18:03:17.000Z | 2022-03-22T21:32:03.000Z | 02_thermo/.ipynb_checkpoints/HW1-Fa17-soln-checkpoint.ipynb | DPotoyan/Statmech4ChemBio | bc38f04545e1f64848d09c390caad7b54ba3adfd | [
"MIT"
] | null | null | null | 02_thermo/.ipynb_checkpoints/HW1-Fa17-soln-checkpoint.ipynb | DPotoyan/Statmech4ChemBio | bc38f04545e1f64848d09c390caad7b54ba3adfd | [
"MIT"
] | 1 | 2022-01-28T18:18:49.000Z | 2022-01-28T18:18:49.000Z | 226.735746 | 20,766 | 0.891156 | true | 6,415 | Qwen/Qwen-72B | 1. YES
2. YES | 0.795658 | 0.851953 | 0.677863 | __label__eng_Latn | 0.760854 | 0.413234 |
```python
# Importing standard Qiskit libraries
from qiskit import QuantumCircuit, execute, Aer, IBMQ, QuantumRegister
from qiskit.compiler import transpile, assemble
from qiskit.tools.jupyter import *
from qiskit.visualization import *
from ibm_quantum_widgets import *
import numpy as np
import qiskit as qk
import m... | d2276f4b11f01a7727c780e1eaab37d7690bc851 | 46,901 | ipynb | Jupyter Notebook | szakkor_files/Berstein-Vazirani-mo.ipynb | thundergoth/KvantumSzakkor_2022 | afc966e11f484c90ae9804d478d1c0d1d8f3f8fd | [
"Apache-2.0",
"CC-BY-4.0"
] | 2 | 2022-03-30T04:56:20.000Z | 2022-03-30T04:56:34.000Z | szakkor_files/Berstein-Vazirani-mo.ipynb | thundergoth/KvantumSzakkor_2022 | afc966e11f484c90ae9804d478d1c0d1d8f3f8fd | [
"Apache-2.0",
"CC-BY-4.0"
] | null | null | null | szakkor_files/Berstein-Vazirani-mo.ipynb | thundergoth/KvantumSzakkor_2022 | afc966e11f484c90ae9804d478d1c0d1d8f3f8fd | [
"Apache-2.0",
"CC-BY-4.0"
] | null | null | null | 159.527211 | 38,200 | 0.883947 | true | 1,994 | Qwen/Qwen-72B | 1. YES
2. YES | 0.766294 | 0.749087 | 0.574021 | __label__hun_Latn | 0.997846 | 0.171972 |
# Optimization in Python
You might have noticed that we didn't do anything related to sparsity with scikit-learn models. A lot of the work we covered in the machine learning class is very recent research, and as such is typically not implemented by the popular libraries.
If we want to do things like sparse regression... | 53c0c5aa6501dc8ad0b566476cc1816921d3a560 | 33,877 | ipynb | Jupyter Notebook | ML3 - Optimization Modeling (Complete).ipynb | oskali/mban_softwareTools | 60b73c798a1f8447de22c46070d023de41d33a30 | [
"MIT"
] | 1 | 2021-03-06T21:16:13.000Z | 2021-03-06T21:16:13.000Z | ML3 - Optimization Modeling (Complete).ipynb | oskali/mban_softwareTools | 60b73c798a1f8447de22c46070d023de41d33a30 | [
"MIT"
] | null | null | null | ML3 - Optimization Modeling (Complete).ipynb | oskali/mban_softwareTools | 60b73c798a1f8447de22c46070d023de41d33a30 | [
"MIT"
] | 6 | 2019-12-03T22:35:28.000Z | 2021-03-04T00:28:02.000Z | 26.13966 | 305 | 0.507099 | true | 5,687 | Qwen/Qwen-72B | 1. YES
2. YES | 0.927363 | 0.931463 | 0.863804 | __label__eng_Latn | 0.853141 | 0.84524 |
# Implementation of Bayesian Neural Network Regression via Hamiltonian Monte Carlo and Black-Box Variational Inference
## Overview
This article explores regression with neural networks from a Bayesian perspective. Priors are placed on network parameters $W$ and sampling techniques such as Hamiltonian Monte Carlo (HMC... | cdba211b3a4ba2ec6c72bdb3365fe4a3223b7def | 344,891 | ipynb | Jupyter Notebook | BNNs/BNN_Regression.ipynb | alexjlim/projects | d9bdd42d1598ee94c884a855bccbc00c8c3bc57a | [
"MIT"
] | null | null | null | BNNs/BNN_Regression.ipynb | alexjlim/projects | d9bdd42d1598ee94c884a855bccbc00c8c3bc57a | [
"MIT"
] | null | null | null | BNNs/BNN_Regression.ipynb | alexjlim/projects | d9bdd42d1598ee94c884a855bccbc00c8c3bc57a | [
"MIT"
] | null | null | null | 270.290752 | 144,280 | 0.901833 | true | 22,844 | Qwen/Qwen-72B | 1. YES
2. YES | 0.857768 | 0.718594 | 0.616387 | __label__eng_Latn | 0.457101 | 0.270405 |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W1D5_DimensionalityReduction/W1D5_Tutorial1.ipynb" target="_parent"></a>
# Neuromatch Academy: Week 1, Day 5, Tutorial 1
# Dimensionality Reduction: Geometric view of data
__Content creators:__ Alex Cayco Gajic, J... | 44c0108f26920b92aa0e7f90a5a92c241cf05d60 | 835,258 | ipynb | Jupyter Notebook | tutorials/W1D5_DimensionalityReduction/W1D5_Tutorial1.ipynb | neurorishika/course-content | d7fd2feabd662c8a32afc2837f45cc7f18e1f4aa | [
"CC-BY-4.0"
] | null | null | null | tutorials/W1D5_DimensionalityReduction/W1D5_Tutorial1.ipynb | neurorishika/course-content | d7fd2feabd662c8a32afc2837f45cc7f18e1f4aa | [
"CC-BY-4.0"
] | null | null | null | tutorials/W1D5_DimensionalityReduction/W1D5_Tutorial1.ipynb | neurorishika/course-content | d7fd2feabd662c8a32afc2837f45cc7f18e1f4aa | [
"CC-BY-4.0"
] | null | null | null | 579.637752 | 194,276 | 0.945128 | true | 5,289 | Qwen/Qwen-72B | 1. YES
2. YES | 0.805632 | 0.72487 | 0.583979 | __label__eng_Latn | 0.95969 | 0.195109 |
# Linear Algebra
> ## Linearity
> ### superpostion principle (중첩의 원리)
>> a function $F(x)$ that satisfies the superposition principle is called a linear function
>> ### additivity
>>> ### $F(x_1 + x_2) = F(x_1) + F(x_2)$
>>
>> ### homogeneity
>>> ### $F(ax) = aF(x), \text{ for scalar } a.$
>> $ homogenous solution + pa... | dee3075fff5179af7523d1b7e27c656475a4bdab | 236,094 | ipynb | Jupyter Notebook | python/Vectors/algebra.ipynb | karng87/nasm_game | a97fdb09459efffc561d2122058c348c93f1dc87 | [
"MIT"
] | null | null | null | python/Vectors/algebra.ipynb | karng87/nasm_game | a97fdb09459efffc561d2122058c348c93f1dc87 | [
"MIT"
] | null | null | null | python/Vectors/algebra.ipynb | karng87/nasm_game | a97fdb09459efffc561d2122058c348c93f1dc87 | [
"MIT"
] | null | null | null | 659.480447 | 136,055 | 0.943844 | true | 1,441 | Qwen/Qwen-72B | 1. YES
2. YES | 0.872347 | 0.76908 | 0.670905 | __label__eng_Latn | 0.127156 | 0.397068 |
# TALENT Course 11
## Learning from Data: Bayesian Methods and Machine Learning
### York, UK, June 10-28, 2019
$% Some LaTeX definitions we'll use.
\newcommand{\pr}{\textrm{p}}
$
## Model selection (I)
### Bayesian evidence:
Please see the full version of the lecture notes here in [html](pub/model_selection-bs.html... | 11708f3d8a52bda04365792f09be49bdaaf54ca0 | 175,518 | ipynb | Jupyter Notebook | topics/model-selection/model-selection_I.ipynb | asemposki/Bayes2019 | bea9dbe5205fbf5939a154b1c3773e6c3baf39a4 | [
"CC0-1.0"
] | 13 | 2019-06-06T17:55:08.000Z | 2021-11-16T08:26:26.000Z | topics/model-selection/model-selection_I.ipynb | asemposki/Bayes2019 | bea9dbe5205fbf5939a154b1c3773e6c3baf39a4 | [
"CC0-1.0"
] | 1 | 2019-06-14T16:17:36.000Z | 2019-06-15T04:41:39.000Z | topics/model-selection/model-selection_I.ipynb | asemposki/Bayes2019 | bea9dbe5205fbf5939a154b1c3773e6c3baf39a4 | [
"CC0-1.0"
] | 17 | 2019-06-10T18:23:29.000Z | 2021-12-22T15:38:30.000Z | 266.339909 | 54,972 | 0.917815 | true | 3,732 | Qwen/Qwen-72B | 1. YES
2. YES | 0.795658 | 0.812867 | 0.646764 | __label__eng_Latn | 0.981308 | 0.340981 |
# Fitting Logistic Regression Models$
\newcommand{\cond}{{\mkern+2mu} \vert {\mkern+2mu}}
\newcommand{\SetDiff}{\mathrel{\backslash}}
\DeclareMathOperator{\BetaFunc}{Β}
\DeclareMathOperator{\GammaFunc}{Γ}
\DeclareMathOperator{\prob}{p}
\DeclareMathOperator{\cost}{J}
\DeclareMathOperator{\score}{V}
\DeclareMathOperator{... | 0c523af19974039c6aad3465514dc0efb6eef906 | 4,078 | ipynb | Jupyter Notebook | GLM/Fitting Logistic Regression Models.ipynb | ConradScott/IJuliaSamples | 0f5d212dcf63bc795e79ac790aa3f1c9b010c89e | [
"Apache-2.0"
] | null | null | null | GLM/Fitting Logistic Regression Models.ipynb | ConradScott/IJuliaSamples | 0f5d212dcf63bc795e79ac790aa3f1c9b010c89e | [
"Apache-2.0"
] | null | null | null | GLM/Fitting Logistic Regression Models.ipynb | ConradScott/IJuliaSamples | 0f5d212dcf63bc795e79ac790aa3f1c9b010c89e | [
"Apache-2.0"
] | null | null | null | 31.859375 | 255 | 0.500736 | true | 966 | Qwen/Qwen-72B | 1. YES
2. YES | 0.960361 | 0.879147 | 0.844298 | __label__eng_Latn | 0.442494 | 0.799921 |
# How Long Do Stars Live?
The Sun produces 400 trillion trillion watts of energy every second - that's enough to power our current energy use for 500,000 years! But where does all of that energy come from?
In the nineteenth century, this was a major question. Early astronomers assumed that the Sun's energy came from ... | b49b798851b859351cf0a9d0e3c693913f3b7e40 | 6,092 | ipynb | Jupyter Notebook | BonusProblems/Module1/BonusChallenge3.ipynb | psheehan/CIERA-HS-Program | 76f7f0ff994e74e646fa34bbb41c314bf7526e9b | [
"Naumen",
"Condor-1.1",
"MS-PL"
] | 2 | 2019-06-25T02:36:49.000Z | 2020-06-09T21:44:41.000Z | BonusProblems/Module1/BonusChallenge3.ipynb | psheehan/CIERA-HS-Program | 76f7f0ff994e74e646fa34bbb41c314bf7526e9b | [
"Naumen",
"Condor-1.1",
"MS-PL"
] | null | null | null | BonusProblems/Module1/BonusChallenge3.ipynb | psheehan/CIERA-HS-Program | 76f7f0ff994e74e646fa34bbb41c314bf7526e9b | [
"Naumen",
"Condor-1.1",
"MS-PL"
] | 7 | 2019-06-25T15:33:10.000Z | 2021-05-12T18:04:36.000Z | 35.418605 | 562 | 0.63132 | true | 910 | Qwen/Qwen-72B | 1. YES
2. YES | 0.944995 | 0.853913 | 0.806943 | __label__eng_Latn | 0.999695 | 0.713132 |
# Non Linear Regression Analysis
## Objectives
* Differentiate between linear and non-linear regression
* Use non-linear regression model in Python
If the data shows a curvy trend, then linear regression will not produce very accurate results when compared to a non-linear regression since linear regression pres... | 9d210f25b15a2babf8c093eaf3a52263471d8e20 | 155,629 | ipynb | Jupyter Notebook | MachineLearning_Basics/ML0101EN-Reg-NoneLinearRegression-py-v1.ipynb | niranjan-1/Data_Science_Projects | d6a7677b967f90a7881742cef8030a92e5148871 | [
"MIT"
] | null | null | null | MachineLearning_Basics/ML0101EN-Reg-NoneLinearRegression-py-v1.ipynb | niranjan-1/Data_Science_Projects | d6a7677b967f90a7881742cef8030a92e5148871 | [
"MIT"
] | null | null | null | MachineLearning_Basics/ML0101EN-Reg-NoneLinearRegression-py-v1.ipynb | niranjan-1/Data_Science_Projects | d6a7677b967f90a7881742cef8030a92e5148871 | [
"MIT"
] | null | null | null | 193.568408 | 18,532 | 0.905583 | true | 3,461 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91848 | 0.884039 | 0.811973 | __label__eng_Latn | 0.934099 | 0.724817 |
```python
!pip install pandas
import sympy as sym
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
sym.init_printing()
sym.__version__
```
Requirement already satisfied: pandas in c:\users\usuario\.conda\envs\sistdin\lib\site-packages (0.23.4)
Requirement already sati... | 31459132614647fb755fdfa5b6df2ccbece0f4a4 | 87,161 | ipynb | Jupyter Notebook | 04_Series_de_Fourier.ipynb | pierrediazp/Se-ales_y_Sistemas | b14bdaf814b0643589660078ddd39b5cdf86b659 | [
"MIT"
] | null | null | null | 04_Series_de_Fourier.ipynb | pierrediazp/Se-ales_y_Sistemas | b14bdaf814b0643589660078ddd39b5cdf86b659 | [
"MIT"
] | null | null | null | 04_Series_de_Fourier.ipynb | pierrediazp/Se-ales_y_Sistemas | b14bdaf814b0643589660078ddd39b5cdf86b659 | [
"MIT"
] | null | null | null | 124.338088 | 19,760 | 0.832115 | true | 3,705 | Qwen/Qwen-72B | 1. YES
2. YES | 0.658418 | 0.868827 | 0.572051 | __label__spa_Latn | 0.318346 | 0.167395 |
# An interactive introduction to polyphase filterbanks
**Author:** Danny Price, UC Berkeley
**License:** [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/)
```python
%matplotlib inline
```
```python
# Import required modules
import numpy as np
import scipy
from scipy.signal import firwin, freqz, lfilter
im... | 04231a605733441273904fac744a4c109c0b9892 | 295,109 | ipynb | Jupyter Notebook | pfb_introduction.ipynb | telegraphic/pfb_introduction | 8a3c62dcc2c1ff9e4165c0f67c0702f95514d84f | [
"CC-BY-4.0"
] | 32 | 2017-01-26T22:52:22.000Z | 2022-03-30T21:20:15.000Z | pfb_introduction.ipynb | evanmayer/pfb_introduction | 8a3c62dcc2c1ff9e4165c0f67c0702f95514d84f | [
"CC-BY-4.0"
] | 5 | 2019-11-10T11:02:42.000Z | 2020-09-22T01:04:22.000Z | pfb_introduction.ipynb | evanmayer/pfb_introduction | 8a3c62dcc2c1ff9e4165c0f67c0702f95514d84f | [
"CC-BY-4.0"
] | 16 | 2016-10-23T02:53:12.000Z | 2021-08-06T13:58:17.000Z | 492.669449 | 97,480 | 0.941381 | true | 3,610 | Qwen/Qwen-72B | 1. YES
2. YES | 0.822189 | 0.805632 | 0.662382 | __label__eng_Latn | 0.984838 | 0.377266 |
# Taylor problem 5.50
last revised: 21-Jan-2019 by Dick Furnstahl [furnstahl.1@osu.edu]
Here we are exploring the Fourier series for a waveform defined to be odd about the origin, so $f(-t) = -f(t)$, with period $\tau$. That means that the integrand for the $a_m$ coefficients is odd and so all of the correspondin... | 078e1693370fec579a427daf9fbdac22e40c0bf8 | 68,233 | ipynb | Jupyter Notebook | 2020_week_2/Taylor_problem_5.50.ipynb | CLima86/Physics_5300_CDL | d9e8ee0861d408a85b4be3adfc97e98afb4a1149 | [
"MIT"
] | null | null | null | 2020_week_2/Taylor_problem_5.50.ipynb | CLima86/Physics_5300_CDL | d9e8ee0861d408a85b4be3adfc97e98afb4a1149 | [
"MIT"
] | null | null | null | 2020_week_2/Taylor_problem_5.50.ipynb | CLima86/Physics_5300_CDL | d9e8ee0861d408a85b4be3adfc97e98afb4a1149 | [
"MIT"
] | null | null | null | 169.733831 | 54,440 | 0.876467 | true | 2,973 | Qwen/Qwen-72B | 1. YES
2. YES | 0.874077 | 0.882428 | 0.77131 | __label__eng_Latn | 0.921008 | 0.630344 |
# Exercises and Problems for Module 2
```python
import numpy as np
from pint import UnitRegistry
import matplotlib.pyplot as plt
import Utils16101
import sympy
sympy.init_printing()
%matplotlib inline
```
```python
ureg = UnitRegistry()
Q_ = ureg.Quantity
```
## Exercise 2.4.2: compute lift coefficient
First airc... | 1aa1c6750764d686a821dbc99ac254363c9cfe58 | 81,053 | ipynb | Jupyter Notebook | problems02.ipynb | Ccaccia73/Intro2Aero_Edx | 28714f3d937fe4738b0cf72f2fdc44010503ae39 | [
"Artistic-2.0"
] | null | null | null | problems02.ipynb | Ccaccia73/Intro2Aero_Edx | 28714f3d937fe4738b0cf72f2fdc44010503ae39 | [
"Artistic-2.0"
] | null | null | null | problems02.ipynb | Ccaccia73/Intro2Aero_Edx | 28714f3d937fe4738b0cf72f2fdc44010503ae39 | [
"Artistic-2.0"
] | null | null | null | 52.906658 | 35,332 | 0.75078 | true | 5,951 | Qwen/Qwen-72B | 1. YES
2. YES | 0.890294 | 0.851953 | 0.758489 | __label__eng_Latn | 0.224387 | 0.600555 |
<h1><center>Modelling Montesinho Natural Park's conflagrations</center></h1>
<h3><center>University of Cyprus - Project for MAS451</center></h3>
<h3><center>Ifigeneia Galanou, Evi Zaou, Marios Andreou</center></h3>
---
## Introduction
Modelling instances of conflagrations, in regards to various parameters and var... | faedceea7ef070ddb56fa2f209763bb706f8a27e | 570,827 | ipynb | Jupyter Notebook | Modelling Montesinho Natural Park's conflagrations.ipynb | marandmath/Modelling-Montesinho-Forest-Fires | 1eaa15b2c6863989b62f04a6c76a9032d209bbd1 | [
"Apache-2.0"
] | 1 | 2022-01-23T19:41:18.000Z | 2022-01-23T19:41:18.000Z | Modelling Montesinho Natural Park's conflagrations.ipynb | marandmath/Modelling-Montesinho-Forest-Fires | 1eaa15b2c6863989b62f04a6c76a9032d209bbd1 | [
"Apache-2.0"
] | null | null | null | Modelling Montesinho Natural Park's conflagrations.ipynb | marandmath/Modelling-Montesinho-Forest-Fires | 1eaa15b2c6863989b62f04a6c76a9032d209bbd1 | [
"Apache-2.0"
] | null | null | null | 249.924256 | 27,648 | 0.890419 | true | 17,657 | Qwen/Qwen-72B | 1. YES
2. YES | 0.867036 | 0.847968 | 0.735218 | __label__eng_Latn | 0.98354 | 0.54649 |
<figure>
<IMG SRC="gfx/Logo_norsk_pos.png" WIDTH=100 ALIGN="right">
</figure>
# Particle in a two-dimensional box
*Roberto Di Remigio*, *Luca Frediani*
After discussing and experimenting with the one-dimensional particle in a box model, we now move on to the two-dimensional case. The particle is now confined into a... | 24037dd6adfba42caa0bb9d36b27a45eb9166656 | 119,537 | ipynb | Jupyter Notebook | 06_2D-particle_in_a_box.ipynb | ilfreddy/seminars | c7e13874b41cc906a45b672e5b85c57d6880473e | [
"MIT"
] | 4 | 2017-02-04T01:34:33.000Z | 2021-06-12T12:27:37.000Z | 06_2D-particle_in_a_box.ipynb | ilfreddy/seminars | c7e13874b41cc906a45b672e5b85c57d6880473e | [
"MIT"
] | 3 | 2020-03-30T11:00:35.000Z | 2020-05-12T05:42:24.000Z | 06_2D-particle_in_a_box.ipynb | ilfreddy/seminars | c7e13874b41cc906a45b672e5b85c57d6880473e | [
"MIT"
] | 7 | 2016-04-26T20:42:43.000Z | 2022-02-06T11:12:57.000Z | 405.210169 | 108,122 | 0.918134 | true | 2,480 | Qwen/Qwen-72B | 1. YES
2. YES | 0.841826 | 0.893309 | 0.752011 | __label__eng_Latn | 0.967493 | 0.585505 |
# Physics 256
## Random Number Generators
http://www.idquantique.com/random-number-generation/
## Last Time
- Error scaling for high dimensional quadrature
- Monte Carlo Integration
## Today
- Generation and testing of pseudorandom numbers
- Tower sampling
## Setting up the Notebook
```python
import matplotl... | 3b6b7e5391b02bcf2c979bf29ba5eb09547cad88 | 470,881 | ipynb | Jupyter Notebook | 4-assets/BOOKS/Jupyter-Notebooks/Overflow/29_RandomNumbers.ipynb | impastasyndrome/Lambda-Resource-Static-Assets | 7070672038620d29844991250f2476d0f1a60b0a | [
"MIT"
] | null | null | null | 4-assets/BOOKS/Jupyter-Notebooks/Overflow/29_RandomNumbers.ipynb | impastasyndrome/Lambda-Resource-Static-Assets | 7070672038620d29844991250f2476d0f1a60b0a | [
"MIT"
] | null | null | null | 4-assets/BOOKS/Jupyter-Notebooks/Overflow/29_RandomNumbers.ipynb | impastasyndrome/Lambda-Resource-Static-Assets | 7070672038620d29844991250f2476d0f1a60b0a | [
"MIT"
] | 1 | 2021-11-05T07:48:26.000Z | 2021-11-05T07:48:26.000Z | 585.672886 | 297,076 | 0.928596 | true | 12,810 | Qwen/Qwen-72B | 1. YES
2. YES | 0.73412 | 0.73412 | 0.538931 | __label__yue_Hant | 0.251732 | 0.090448 |
## Moving average filter
The moving average(MA) filter is a Low Pass FIR used for smoothing signals. This filter sum the data of L consecutive elements of the input vector and divide by L, therefore the result is a single output point.
As the parameter L increases, the smoothness of the output is better, whereas the ... | 3f736266b1dba2a7ff02b725899f08bb7bc2bcb1 | 113,330 | ipynb | Jupyter Notebook | MA filter/Moving average filter.ipynb | frhaedo/dsp | a6941f915b602e9daf4b53c69c63be28e3e9df1d | [
"Apache-2.0"
] | null | null | null | MA filter/Moving average filter.ipynb | frhaedo/dsp | a6941f915b602e9daf4b53c69c63be28e3e9df1d | [
"Apache-2.0"
] | 1 | 2021-04-03T19:15:05.000Z | 2021-04-03T19:15:05.000Z | MA filter/Moving average filter.ipynb | frhaedo/dsp | a6941f915b602e9daf4b53c69c63be28e3e9df1d | [
"Apache-2.0"
] | null | null | null | 336.290801 | 33,584 | 0.936548 | true | 1,129 | Qwen/Qwen-72B | 1. YES
2. YES | 0.903294 | 0.913677 | 0.825319 | __label__eng_Latn | 0.688966 | 0.755825 |
# Black-Scholes European Option Pricing Script
```python
# File Contains: Python code containing closed-form solutions for the valuation of European Options,
# for backward compatability with Python 2.7
from __future__ import division
# import necessary libaries
import math
import numpy as np
from scipy.stats import... | 3bcbcf5990c14325f9cf93119860a2c65851509c | 83,120 | ipynb | Jupyter Notebook | .ipynb_checkpoints/OptionsPricingEvaluation-checkpoint.ipynb | SolitonScientific/Option_Pricing | 8e1ba226583f3f03a2d978d332696129bafa83cc | [
"MIT"
] | null | null | null | .ipynb_checkpoints/OptionsPricingEvaluation-checkpoint.ipynb | SolitonScientific/Option_Pricing | 8e1ba226583f3f03a2d978d332696129bafa83cc | [
"MIT"
] | null | null | null | .ipynb_checkpoints/OptionsPricingEvaluation-checkpoint.ipynb | SolitonScientific/Option_Pricing | 8e1ba226583f3f03a2d978d332696129bafa83cc | [
"MIT"
] | null | null | null | 165.248509 | 16,818 | 0.857507 | true | 3,801 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91848 | 0.812867 | 0.746603 | __label__eng_Latn | 0.981937 | 0.57294 |
$$
\newcommand{\dt}{\Delta t}
\newcommand{\udt}[1]{u^{({#1})}(T)}
\newcommand{\Edt}[1]{E^{({#1})}}
\newcommand{\uone}[1]{u_{1}^{({#1})}}
$$
This is the third in a series of posts on testing scientific software. For this to make sense, you'll need to have skimmed [the motivation and background](http://ianhawke.github.i... | ccd4090952b650f396b5ca971e820c8e1095587c | 23,186 | ipynb | Jupyter Notebook | content/notebooks/03-Close-Enough-Just-Euler.ipynb | IanHawke/blog | aa47807bf5a96cc97ecfbe48e41b8f795b88cba9 | [
"MIT"
] | 3 | 2015-03-10T23:49:33.000Z | 2016-06-01T23:53:24.000Z | content/notebooks/03-Close-Enough-Just-Euler.ipynb | IanHawke/blog | aa47807bf5a96cc97ecfbe48e41b8f795b88cba9 | [
"MIT"
] | null | null | null | content/notebooks/03-Close-Enough-Just-Euler.ipynb | IanHawke/blog | aa47807bf5a96cc97ecfbe48e41b8f795b88cba9 | [
"MIT"
] | null | null | null | 45.285156 | 3,229 | 0.587898 | true | 3,832 | Qwen/Qwen-72B | 1. YES
2. YES | 0.763484 | 0.72487 | 0.553427 | __label__eng_Latn | 0.975753 | 0.124125 |
# AS-AD-model with long-run growth and bubbles.
In the following, we analyze a basic AS-AD-model containing equilibria in the goods- and service markets,an inflation-targeting Taylor rule, short-run aggregate supply determined by a philips-curve with nominal wage rigidities, as well as rational expectations for inflat... | 74471302d74b777fc7b05aa309308dce1a153d1f | 399,357 | ipynb | Jupyter Notebook | AS-AD-model (IPNA assignment)/modelproject.ipynb | Holger-Harmsen/NumEcon | 20c61548c8889cbc17b9d9e83a7ce0398ef0761e | [
"MIT"
] | null | null | null | AS-AD-model (IPNA assignment)/modelproject.ipynb | Holger-Harmsen/NumEcon | 20c61548c8889cbc17b9d9e83a7ce0398ef0761e | [
"MIT"
] | null | null | null | AS-AD-model (IPNA assignment)/modelproject.ipynb | Holger-Harmsen/NumEcon | 20c61548c8889cbc17b9d9e83a7ce0398ef0761e | [
"MIT"
] | 1 | 2020-04-26T08:53:10.000Z | 2020-04-26T08:53:10.000Z | 856.98927 | 176,898 | 0.808682 | true | 3,996 | Qwen/Qwen-72B | 1. YES
2. YES | 0.826712 | 0.79053 | 0.653541 | __label__eng_Latn | 0.954449 | 0.356725 |
```python
from sympy.physics.units import *
from sympy import *
EA, l, F = var("EA, l, F")
# def k(phi):
# """ computes element stiffness matrix """
# # phi is angle between:
# # 1. vector along global x axis
# # 2. vector along 1-2-axis of truss
# # phi is counted positively about z.
# (c, s)... | abc6f379e814f5ba6d67997480951b046d1b999a | 6,390 | ipynb | Jupyter Notebook | ipynb/EMS_02A/Selbst/5.1.ipynb | kassbohm/wb-snippets | f1ac5194e9f60a9260d096ba5ed1ce40b844a3fe | [
"MIT"
] | null | null | null | ipynb/EMS_02A/Selbst/5.1.ipynb | kassbohm/wb-snippets | f1ac5194e9f60a9260d096ba5ed1ce40b844a3fe | [
"MIT"
] | null | null | null | ipynb/EMS_02A/Selbst/5.1.ipynb | kassbohm/wb-snippets | f1ac5194e9f60a9260d096ba5ed1ce40b844a3fe | [
"MIT"
] | null | null | null | 32.769231 | 121 | 0.362441 | true | 1,448 | Qwen/Qwen-72B | 1. YES
2. YES | 0.955319 | 0.824462 | 0.787624 | __label__kor_Hang | 0.110077 | 0.668248 |
# Frequentist Inference Case Study - Part A
## 1. Learning objectives
Welcome to part A of the Frequentist inference case study! The purpose of this case study is to help you apply the concepts associated with Frequentist inference in Python. Frequentist inference is the process of deriving conclusions about an unde... | 823752dbe2e6a77c817b2d6f11c62123857b9c53 | 87,819 | ipynb | Jupyter Notebook | frequentist-case-study/frequentist-case-study-part-A.ipynb | reppertj/Data-Science-Examples | ee2690f07a9f606ecdb47cf1f3538641ade24312 | [
"MIT"
] | null | null | null | frequentist-case-study/frequentist-case-study-part-A.ipynb | reppertj/Data-Science-Examples | ee2690f07a9f606ecdb47cf1f3538641ade24312 | [
"MIT"
] | null | null | null | frequentist-case-study/frequentist-case-study-part-A.ipynb | reppertj/Data-Science-Examples | ee2690f07a9f606ecdb47cf1f3538641ade24312 | [
"MIT"
] | null | null | null | 77.784765 | 20,684 | 0.812432 | true | 6,535 | Qwen/Qwen-72B | 1. YES
2. YES | 0.828939 | 0.760651 | 0.630533 | __label__eng_Latn | 0.999025 | 0.30327 |
# Classical Support Vector Machines
This notebook will serve as a summary of some of the resources below and is not meant to be used as a stand-alone reading material for Classical Support Vector Machines. We encourage you to complete reading the resources below before going forward with the notebook.
### Resource... | 242f0db00020d38625ca75e3c50e61e3db889aff | 209,825 | ipynb | Jupyter Notebook | Day 5/Classical Support Vector Machines.ipynb | AnDa-creator/Qiskit-India-Challenge | 69ed61d2b4258a217116c07dcbd13451479d11a7 | [
"MIT"
] | null | null | null | Day 5/Classical Support Vector Machines.ipynb | AnDa-creator/Qiskit-India-Challenge | 69ed61d2b4258a217116c07dcbd13451479d11a7 | [
"MIT"
] | null | null | null | Day 5/Classical Support Vector Machines.ipynb | AnDa-creator/Qiskit-India-Challenge | 69ed61d2b4258a217116c07dcbd13451479d11a7 | [
"MIT"
] | null | null | null | 338.974152 | 45,788 | 0.920377 | true | 5,008 | Qwen/Qwen-72B | 1. YES
2. YES | 0.7773 | 0.760651 | 0.591254 | __label__eng_Latn | 0.987792 | 0.21201 |
# Trajectory Optimization
## Notebook Setup
### Julia Setup
```julia
using LaTeXStrings
using Plots
using Polynomials
import Base: ctranspose
# The Poly class has an odd quirk in that is defines the conjugate transpose
# operator A' as differentiation of the polynomial. While this makes some
# sense for an isolat... | f95ea64773d7f7bc94a07bebe689b6e50fbe81ad | 238,400 | ipynb | Jupyter Notebook | doc/theory.ipynb | flying-tiger/TrajOpt.jl | 5892b3e580c26c752e1565b5e2f635d112d143cb | [
"MIT"
] | null | null | null | doc/theory.ipynb | flying-tiger/TrajOpt.jl | 5892b3e580c26c752e1565b5e2f635d112d143cb | [
"MIT"
] | null | null | null | doc/theory.ipynb | flying-tiger/TrajOpt.jl | 5892b3e580c26c752e1565b5e2f635d112d143cb | [
"MIT"
] | null | null | null | 272.768879 | 196,083 | 0.886598 | true | 10,432 | Qwen/Qwen-72B | 1. YES
2. YES | 0.76908 | 0.731059 | 0.562243 | __label__eng_Latn | 0.937662 | 0.144608 |
# Cheme 512- Method of Engineering Analysis
## Analysis of Problem 1.1.2 from Conduction Heat Solution Manual
#### Maria Politi
#### Diagram :
To determine a solution to this problem, a slab composed of two different layers was chosen. A similar procedure to the one displayed below can be use to find the solution ... | aa3edeafcea567832617a4d01c611e88646a60ff | 123,723 | ipynb | Jupyter Notebook | presentations/10_17_19_Politi.ipynb | uw-cheme512/uw-cheme512.github.io | 6dad7a9554eafb6eba347462d30c62bf9c0ec4da | [
"BSD-3-Clause"
] | null | null | null | presentations/10_17_19_Politi.ipynb | uw-cheme512/uw-cheme512.github.io | 6dad7a9554eafb6eba347462d30c62bf9c0ec4da | [
"BSD-3-Clause"
] | null | null | null | presentations/10_17_19_Politi.ipynb | uw-cheme512/uw-cheme512.github.io | 6dad7a9554eafb6eba347462d30c62bf9c0ec4da | [
"BSD-3-Clause"
] | null | null | null | 296.697842 | 75,488 | 0.919902 | true | 2,834 | Qwen/Qwen-72B | 1. YES
2. YES | 0.899121 | 0.868827 | 0.781181 | __label__eng_Latn | 0.711325 | 0.653277 |
# Nonlinear Equations and their Roots
## CH EN 2450 - Numerical Methods
**Prof. Tony Saad (<a>www.tsaad.net</a>) <br/>Department of Chemical Engineering <br/>University of Utah**
<hr/>
The purpose of root finding methods for nonlinear functions is to find the roots - or values of the independent variable, e.g. x - tha... | b8a80900fc2b1f0b9ad0aef6e48e4d31f3e32943 | 48,868 | ipynb | Jupyter Notebook | topics/nonlinear-equations/Root Finding Methods.ipynb | jomorodi/NumericalMethods | e040693001941079b2e0acc12e0c3ee5c917671c | [
"MIT"
] | 3 | 2019-03-27T05:22:34.000Z | 2021-01-27T10:49:13.000Z | topics/nonlinear-equations/Root Finding Methods.ipynb | jomorodi/NumericalMethods | e040693001941079b2e0acc12e0c3ee5c917671c | [
"MIT"
] | null | null | null | topics/nonlinear-equations/Root Finding Methods.ipynb | jomorodi/NumericalMethods | e040693001941079b2e0acc12e0c3ee5c917671c | [
"MIT"
] | 7 | 2019-12-29T23:31:56.000Z | 2021-12-28T19:04:10.000Z | 37.275362 | 382 | 0.466583 | true | 3,587 | Qwen/Qwen-72B | 1. YES
2. YES | 0.888759 | 0.855851 | 0.760645 | __label__eng_Latn | 0.845248 | 0.605566 |
(Evaluate block to execute LaTeX definitions)
$ \newcommand{\ybar}{\overline{y}} $
$ \renewcommand{\d}[2]{\frac{d #1}{d #2}} $
$ \newcommand{\dd}[2]{\frac{d^2 #1}{d #2^2}} $
$ \newcommand{\pd}[2]{\frac{\partial #1}{\partial #2}} $
$ \newcommand{\pdd}[2]{\frac{\partial^2 #1}{\partial #2^2}} $
$ \renewcommand{\b}{\beta}... | 0e5e9e76ee2ae9c62330c4b66f45409471e58477 | 77,182 | ipynb | Jupyter Notebook | Supplemental_Material.ipynb | jeffbparker/ZonalFlowWaveKinetic | 93d0de31356efc3c0d4d44a62f04a870242b8602 | [
"MIT"
] | null | null | null | Supplemental_Material.ipynb | jeffbparker/ZonalFlowWaveKinetic | 93d0de31356efc3c0d4d44a62f04a870242b8602 | [
"MIT"
] | null | null | null | Supplemental_Material.ipynb | jeffbparker/ZonalFlowWaveKinetic | 93d0de31356efc3c0d4d44a62f04a870242b8602 | [
"MIT"
] | null | null | null | 190.57284 | 29,194 | 0.852168 | true | 5,649 | Qwen/Qwen-72B | 1. YES
2. YES | 0.839734 | 0.721743 | 0.606072 | __label__eng_Latn | 0.90972 | 0.246439 |
# Running a statistical trial for a machine learning regression model
Imagine you have been given an imaging dataset and you have trained a [convolutional neural network](https://en.wikipedia.org/wiki/Convolutional_neural_network) to count the number of cells in the image for a medical-based task. On a held-out test s... | 5b1d7319cd93e94633c248266b034529ca82ab25 | 878,860 | ipynb | Jupyter Notebook | _rmd/extra_regression/power_for_regression_metrics.ipynb | erikdrysdale/erikdrysdale.github.io | ff337117e063be7f909bc2d1f3ff427781d29f31 | [
"MIT"
] | null | null | null | _rmd/extra_regression/power_for_regression_metrics.ipynb | erikdrysdale/erikdrysdale.github.io | ff337117e063be7f909bc2d1f3ff427781d29f31 | [
"MIT"
] | null | null | null | _rmd/extra_regression/power_for_regression_metrics.ipynb | erikdrysdale/erikdrysdale.github.io | ff337117e063be7f909bc2d1f3ff427781d29f31 | [
"MIT"
] | 2 | 2017-09-13T15:16:36.000Z | 2020-03-03T15:37:01.000Z | 930.010582 | 257,032 | 0.943709 | true | 11,782 | Qwen/Qwen-72B | 1. YES
2. YES | 0.787931 | 0.828939 | 0.653147 | __label__eng_Latn | 0.92387 | 0.35581 |
## <span style = "color:blue">Causal Analysis in Settings where the control group is orders of magnitude larger than the treatment group </span>
```python
%load_ext autoreload
%autoreload 2
```
```python
import numpy as np
import pandas as pd
#dowhy
import dowhy
from dowhy import CausalModel
import dowhy.datasets... | 1493a4c982bf9a5ec65ac80d160bfbe10145d4e0 | 63,104 | ipynb | Jupyter Notebook | notebooks/Causality.ipynb | jorje1908/causality | 926de35abef1b1a7e300c5399bb2dd6ec313d0c1 | [
"MIT"
] | null | null | null | notebooks/Causality.ipynb | jorje1908/causality | 926de35abef1b1a7e300c5399bb2dd6ec313d0c1 | [
"MIT"
] | null | null | null | notebooks/Causality.ipynb | jorje1908/causality | 926de35abef1b1a7e300c5399bb2dd6ec313d0c1 | [
"MIT"
] | null | null | null | 49.532182 | 19,232 | 0.620769 | true | 6,869 | Qwen/Qwen-72B | 1. YES
2. YES | 0.760651 | 0.715424 | 0.544188 | __label__eng_Latn | 0.308996 | 0.10266 |
#### _Speech Processing Labs 2020: Signals: Module 2_
```python
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import cmath
from math import floor
from matplotlib.animation import FuncAnimation
from IPython.display import HTML
plt.style.use('ggplot')
from dspMisc import *
```
# 2 Filtering t... | f2662c89666aa0c9469607c9fb88d92abc36a417 | 797,317 | ipynb | Jupyter Notebook | signals/sp-m2-2-fir-filters.ipynb | vatnid/uoe_speech_processing_course | ac479566f2d7b911cac8c94ecac92dda2b80bdb3 | [
"MIT"
] | null | null | null | signals/sp-m2-2-fir-filters.ipynb | vatnid/uoe_speech_processing_course | ac479566f2d7b911cac8c94ecac92dda2b80bdb3 | [
"MIT"
] | null | null | null | signals/sp-m2-2-fir-filters.ipynb | vatnid/uoe_speech_processing_course | ac479566f2d7b911cac8c94ecac92dda2b80bdb3 | [
"MIT"
] | null | null | null | 414.190649 | 88,564 | 0.934572 | true | 9,978 | Qwen/Qwen-72B | 1. YES
2. YES | 0.853913 | 0.815232 | 0.696137 | __label__eng_Latn | 0.987226 | 0.455692 |
# SMU Honors Physics: Adventures in Spacetime
**Authors**: Stephen Sekula
In this python notebook, we will explore numbers, especially vectors - numbers that define both a length and a direction. Vectors are a crucial class of numbers in physics. Many quantities in the natural world must be specified by more than jus... | ca076ee41a54458712790ad9db62780d87761359 | 73,945 | ipynb | Jupyter Notebook | AdventuresInSpacetime/Adventures in Spacetime.ipynb | stephensekula/smu-honors-physics | 8c98408c7693a30023e125ad29f98aa42c3002f6 | [
"MIT"
] | 1 | 2018-11-01T21:50:36.000Z | 2018-11-01T21:50:36.000Z | AdventuresInSpacetime/Adventures in Spacetime.ipynb | stephensekula/smu-honors-physics | 8c98408c7693a30023e125ad29f98aa42c3002f6 | [
"MIT"
] | null | null | null | AdventuresInSpacetime/Adventures in Spacetime.ipynb | stephensekula/smu-honors-physics | 8c98408c7693a30023e125ad29f98aa42c3002f6 | [
"MIT"
] | null | null | null | 114.289026 | 29,634 | 0.81873 | true | 5,698 | Qwen/Qwen-72B | 1. YES
2. YES | 0.859664 | 0.887205 | 0.762698 | __label__eng_Latn | 0.998192 | 0.610334 |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W1D1_ModelTypes/W1D1_Tutorial2.ipynb" target="_parent"></a>
# Neuromatch Academy: Week 1, Day 1, Tutorial 2
# Model Types: "How" models
__Content creators:__ Matt Laporte, Byron Galbraith, Konrad Kording
__Conten... | 2a892b0c92bf5a6469d64810d0e20b1b5a56cda7 | 722,016 | ipynb | Jupyter Notebook | tutorials/W1D1_ModelTypes/W1D1_Tutorial2.ipynb | Jaycob-jh/course-content | 6b2db614a7a357c16c1c108dfd4266dc0b2e9ea5 | [
"CC-BY-4.0"
] | null | null | null | tutorials/W1D1_ModelTypes/W1D1_Tutorial2.ipynb | Jaycob-jh/course-content | 6b2db614a7a357c16c1c108dfd4266dc0b2e9ea5 | [
"CC-BY-4.0"
] | null | null | null | tutorials/W1D1_ModelTypes/W1D1_Tutorial2.ipynb | Jaycob-jh/course-content | 6b2db614a7a357c16c1c108dfd4266dc0b2e9ea5 | [
"CC-BY-4.0"
] | 1 | 2021-08-06T08:05:01.000Z | 2021-08-06T08:05:01.000Z | 357.256804 | 214,488 | 0.930464 | true | 5,162 | Qwen/Qwen-72B | 1. YES
2. YES | 0.675765 | 0.793106 | 0.535953 | __label__eng_Latn | 0.977364 | 0.083528 |
# Integrating Orbits
This is a demonstration of Euler's method for integrating orbits.
```python
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
```
We consider low mass objects orbiting the Sun. We work in units of AU, yr, and solar masses. From Kepler's third law:
\begin{equation}
4 \pi^2... | 8bb1dae09216a0581509b25c73bce31e3d20c222 | 22,315 | ipynb | Jupyter Notebook | orbits_example/orbit.ipynb | zingale/ast341_examples | 0a15b9bf0b268b00021c59504eb7a2006b7e5ada | [
"BSD-3-Clause"
] | 3 | 2020-09-09T15:48:41.000Z | 2021-08-09T16:08:51.000Z | orbits_example/orbit.ipynb | zingale/ast341_examples | 0a15b9bf0b268b00021c59504eb7a2006b7e5ada | [
"BSD-3-Clause"
] | null | null | null | orbits_example/orbit.ipynb | zingale/ast341_examples | 0a15b9bf0b268b00021c59504eb7a2006b7e5ada | [
"BSD-3-Clause"
] | null | null | null | 85.826923 | 16,332 | 0.825633 | true | 1,001 | Qwen/Qwen-72B | 1. YES
2. YES | 0.939025 | 0.861538 | 0.809006 | __label__eng_Latn | 0.755735 | 0.717924 |
```python
import tensorflow as tf
#import tensorflow_quantum as tfq
import cirq
import sympy
import numpy as np
import seaborn as sns
import collections
# visualization tools
%matplotlib inline
import matplotlib.pyplot as plt
from cirq.contrib.svg import SVGCircuit
from PIL import Image
```
```python
```
```pyth... | da290a1b88ef644d7cb066fd96c53926be155660 | 7,680 | ipynb | Jupyter Notebook | Quantum_Encoding_Image_data_v2.ipynb | Rukhsan/Quantum_Encoding | ad3fecc4d9124583e5ffd546be9250e3d9fac99c | [
"Apache-2.0"
] | 1 | 2022-02-22T02:40:51.000Z | 2022-02-22T02:40:51.000Z | Quantum_Encoding_Image_data_v2.ipynb | Rukhsan/Quantum_Encoding | ad3fecc4d9124583e5ffd546be9250e3d9fac99c | [
"Apache-2.0"
] | null | null | null | Quantum_Encoding_Image_data_v2.ipynb | Rukhsan/Quantum_Encoding | ad3fecc4d9124583e5ffd546be9250e3d9fac99c | [
"Apache-2.0"
] | null | null | null | 27.725632 | 92 | 0.545703 | true | 1,260 | Qwen/Qwen-72B | 1. YES
2. YES | 0.831143 | 0.771844 | 0.641512 | __label__eng_Latn | 0.465298 | 0.328779 |
# 附录A：关于布莱克-斯科尔斯-默顿模型的一些有用的推导式
BSM价格表达式如下:
$$C(S,K,\tau,\sigma,r)=SN(d_1)-Ke^{-r\tau}N(d_2)$$
$$d_1=\frac{\ln\left(\frac{S_F}{K}\right)+\frac{\sigma^2}{2}\tau}{\sigma\sqrt{\tau}}$$
$$d_2=\frac{\ln\left(\frac{S_F}{K}\right)-\frac{\sigma^2}{2}\tau}{\sigma\sqrt{\tau}}$$
$$S_F=e^{r\tau}S$$
$$N(x)=\frac{1}{\sqrt{2\pi}}... | 1d633fbcd22af56b79f18579f766486da1b089d0 | 64,508 | ipynb | Jupyter Notebook | volatility-smile/.ipynb_checkpoints/appendix-a-checkpoint.ipynb | chenxin1-5/option-study | 722f425e4a3cad05ec8fbb3fc980fbdb9e85b655 | [
"Unlicense"
] | 3 | 2021-04-05T14:50:01.000Z | 2021-11-12T11:27:02.000Z | volatility-smile/appendix-a.ipynb | chenxin1-5/option-study | 722f425e4a3cad05ec8fbb3fc980fbdb9e85b655 | [
"Unlicense"
] | null | null | null | volatility-smile/appendix-a.ipynb | chenxin1-5/option-study | 722f425e4a3cad05ec8fbb3fc980fbdb9e85b655 | [
"Unlicense"
] | null | null | null | 93.761628 | 15,492 | 0.757673 | true | 2,482 | Qwen/Qwen-72B | 1. YES
2. YES | 0.805632 | 0.70253 | 0.565981 | __label__kor_Hang | 0.078181 | 0.153293 |
# Grain Boundary Coupled Tilt Phase Field Model
```
# ----------- Importing The Libraries ------------ #
import numpy as np
import math as mt
from sympy.vector import Del
from sympy import integrals as intp
```
```
# -------------- Defining Parameters -------------- #
# Length of the simulation box
Ly = 1
# N... | 37988ecca2e26543bfe9592aa60b2c2fbc69e0a7 | 5,379 | ipynb | Jupyter Notebook | gbCoupledTiltPFM.ipynb | tapashreepradhan/phase-field-modelling-nptel | 8da1b8271481c3032b9e486620ab86d5eff3a20c | [
"Apache-2.0"
] | null | null | null | gbCoupledTiltPFM.ipynb | tapashreepradhan/phase-field-modelling-nptel | 8da1b8271481c3032b9e486620ab86d5eff3a20c | [
"Apache-2.0"
] | null | null | null | gbCoupledTiltPFM.ipynb | tapashreepradhan/phase-field-modelling-nptel | 8da1b8271481c3032b9e486620ab86d5eff3a20c | [
"Apache-2.0"
] | null | null | null | 5,379 | 5,379 | 0.59881 | true | 1,041 | Qwen/Qwen-72B | 1. YES
2. YES | 0.909907 | 0.661923 | 0.602288 | __label__eng_Latn | 0.683684 | 0.237648 |
# Start-to-Finish Example: [TOV](https://en.wikipedia.org/wiki/Tolman%E2%80%93Oppenheimer%E2%80%93Volkoff_equation) Neutron Star Simulation: The "Hydro without Hydro" Test
## Authors: Zach Etienne & Phil Chang
### Formatting improvements courtesy Brandon Clark
## This module sets up initial data for a neutron star... | af199090461c696c87fa04af0185ac5882372401 | 246,705 | ipynb | Jupyter Notebook | Tutorial-Start_to_Finish-BSSNCurvilinear-Neutron_Star-Hydro_without_Hydro.ipynb | Steve-Hawk/nrpytutorial | 42d7450dba8bf43aa9c2d8f38f85f18803de69b7 | [
"BSD-2-Clause"
] | null | null | null | Tutorial-Start_to_Finish-BSSNCurvilinear-Neutron_Star-Hydro_without_Hydro.ipynb | Steve-Hawk/nrpytutorial | 42d7450dba8bf43aa9c2d8f38f85f18803de69b7 | [
"BSD-2-Clause"
] | null | null | null | Tutorial-Start_to_Finish-BSSNCurvilinear-Neutron_Star-Hydro_without_Hydro.ipynb | Steve-Hawk/nrpytutorial | 42d7450dba8bf43aa9c2d8f38f85f18803de69b7 | [
"BSD-2-Clause"
] | 1 | 2021-03-02T12:51:56.000Z | 2021-03-02T12:51:56.000Z | 125.103955 | 111,768 | 0.82128 | true | 22,594 | Qwen/Qwen-72B | 1. YES
2. YES | 0.841826 | 0.727975 | 0.612828 | __label__eng_Latn | 0.666567 | 0.262136 |
```python
# Erasmus+ ICCT project (2018-1-SI01-KA203-047081)
# Toggle cell visibility
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
%matplotlib n... | f0924928d885d43614315ab047f771c709fed904 | 144,416 | ipynb | Jupyter Notebook | ICCT_hr/examples/02/TD-03-Mehanicki_sustavi.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-03-Mehanicki_sustavi-checkpoint.ipynb | ICCTerasmus/ICCT | fcd56ab6b5fddc00f72521cc87accfdbec6068f6 | [
"BSD-3-Clause"
] | null | null | null | ICCT_hr/examples/02/.ipynb_checkpoints/TD-03-Mehanicki_sustavi-checkpoint.ipynb | ICCTerasmus/ICCT | fcd56ab6b5fddc00f72521cc87accfdbec6068f6 | [
"BSD-3-Clause"
] | 2 | 2021-05-24T11:40:09.000Z | 2021-08-29T16:36:18.000Z | 116.746968 | 87,099 | 0.776347 | true | 5,030 | Qwen/Qwen-72B | 1. YES
2. YES | 0.740174 | 0.679179 | 0.502711 | __label__eng_Latn | 0.053483 | 0.006294 |
# Álgebra Lineal Numérica
El álgebra lineal es el área de las matemáticas que estudia los espacios vectoriales y las transformaciones lineales entre dichos espacios. Independientemente del espacio vectorial que trabajemos, mientras sea de dimensión finita, todos elementos del espacio se pueden representar como un **ve... | 9941f8a5a2310b67c5d91c4fac1a722258c4d27e | 27,128 | ipynb | Jupyter Notebook | files/fiscomp_2020-4/material/clase11.ipynb | sayeg84/sayeg84.github.io | 18f2e36dd7252603fad8f7093dc5aa00fc721be4 | [
"MIT"
] | null | null | null | files/fiscomp_2020-4/material/clase11.ipynb | sayeg84/sayeg84.github.io | 18f2e36dd7252603fad8f7093dc5aa00fc721be4 | [
"MIT"
] | null | null | null | files/fiscomp_2020-4/material/clase11.ipynb | sayeg84/sayeg84.github.io | 18f2e36dd7252603fad8f7093dc5aa00fc721be4 | [
"MIT"
] | null | null | null | 30.310615 | 554 | 0.5383 | true | 5,649 | Qwen/Qwen-72B | 1. YES
2. YES | 0.855851 | 0.833325 | 0.713202 | __label__spa_Latn | 0.975042 | 0.495338 |
```python
#### Notebook Imports
import numpy as np
```
```python
from random import randint as rand
```
### CS229 Week 1 Algorithms
---
1. Linear Model (for regression)
2. Least Mean Squares cost function
3. Batch Gradient Descent
4. Stochastic Gradient Descent
5. Normal Equations
### Linear Model (Hypothesis Funct... | d8d3c5c805b6f637dab68a2127372f94e8e6e851 | 7,943 | ipynb | Jupyter Notebook | Week1.ipynb | m-yasir/ml-algo-scrach | f369b354ae82c5b31a469c65e5b9c99f0879d79b | [
"MIT"
] | null | null | null | Week1.ipynb | m-yasir/ml-algo-scrach | f369b354ae82c5b31a469c65e5b9c99f0879d79b | [
"MIT"
] | null | null | null | Week1.ipynb | m-yasir/ml-algo-scrach | f369b354ae82c5b31a469c65e5b9c99f0879d79b | [
"MIT"
] | null | null | null | 24.365031 | 119 | 0.463175 | true | 1,789 | Qwen/Qwen-72B | 1. YES
2. YES | 0.90053 | 0.7773 | 0.699982 | __label__yue_Hant | 0.198043 | 0.464623 |
# Clase 3b: Perfil de Yukovski
_Aunque no te lo creas, __con lo que hemos visto hasta ahora eres capaz de hacer grandes cosas__. Vale sí, un perfil de Yukovski no es gran cosa aerodinámicamente, pero si lo hacemos en Python... Echa un vistazo a la figura ¿no está mal, no? algo así intentaremos conseguir al final de ... | 591bdb2a0ab7865c03e84ecf99b4f3642fa12c39 | 41,328 | ipynb | Jupyter Notebook | notebooks_vacios/Clase3_Perfil_Yukovski.ipynb | karimkprr/Curso-AeroPython-UC3M | 50009edc50e9e626e33bd2c4fbb240647f201167 | [
"CC-BY-4.0"
] | 14 | 2015-10-05T20:21:20.000Z | 2021-02-15T03:12:53.000Z | notebooks_vacios/Clase3_Perfil_Yukovski.ipynb | karimkprr/Curso-AeroPython-UC3M | 50009edc50e9e626e33bd2c4fbb240647f201167 | [
"CC-BY-4.0"
] | 2 | 2015-10-04T17:22:10.000Z | 2015-10-08T07:46:05.000Z | notebooks_vacios/Clase3_Perfil_Yukovski.ipynb | karimkprr/Curso-AeroPython-UC3M | 50009edc50e9e626e33bd2c4fbb240647f201167 | [
"CC-BY-4.0"
] | 10 | 2016-06-16T06:00:05.000Z | 2021-10-31T01:44:40.000Z | 33.709625 | 1,306 | 0.541207 | true | 8,107 | Qwen/Qwen-72B | 1. YES
2. YES | 0.861538 | 0.731059 | 0.629835 | __label__spa_Latn | 0.809362 | 0.301648 |
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