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# Introduction to zfit
In this notebook, we will have a walk through the main components of zfit and their features. Especially the extensive model building part will be discussed separately.
zfit consists of 5 mostly independent parts. Other libraries can rely on this parts to do plotting or statistical inference, su... | b00b392978a7224fab32ff55c40d9292dc6918f0 | 170,262 | ipynb | Jupyter Notebook | tutorial2_zfit/Introduction.ipynb | zfit/python_hpc_TensorFlow_MSU | a866b63ddf59c773d89b7e499625bd1eb3d70cb0 | [
"BSD-3-Clause"
] | 1 | 2020-10-10T13:34:04.000Z | 2020-10-10T13:34:04.000Z | tutorial2_zfit/Introduction.ipynb | zfit/python_hpc_TensorFlow_MSU | a866b63ddf59c773d89b7e499625bd1eb3d70cb0 | [
"BSD-3-Clause"
] | null | null | null | tutorial2_zfit/Introduction.ipynb | zfit/python_hpc_TensorFlow_MSU | a866b63ddf59c773d89b7e499625bd1eb3d70cb0 | [
"BSD-3-Clause"
] | null | null | null | 89.049163 | 26,560 | 0.823449 | true | 9,792 | Qwen/Qwen-72B | 1. YES
2. YES | 0.72487 | 0.699254 | 0.506869 | __label__eng_Latn | 0.972054 | 0.015955 |
# Cálculo e clasificación de puntos críticos
Con todas as ferramentas que xa levamos revisado nas anteriores prácticas, o cálculo de puntos críticos e a súa clasificación mediante o criterio que involucra á matriz Hessiana de funcións de dúas variables diferenciables é moi sinxelo usando o módulo **Sympy**. No caso de... | 5ff0a30107d820b79eda1a3b90654559cfe0816f | 112,842 | ipynb | Jupyter Notebook | practicas/extremos-relativos.ipynb | maprieto/CalculoMultivariable | 6bd7839803d696c6cd0e3536c0631453eacded70 | [
"MIT"
] | 1 | 2021-01-09T18:30:54.000Z | 2021-01-09T18:30:54.000Z | practicas/extremos-relativos.ipynb | maprieto/CalculoMultivariable | 6bd7839803d696c6cd0e3536c0631453eacded70 | [
"MIT"
] | null | null | null | practicas/extremos-relativos.ipynb | maprieto/CalculoMultivariable | 6bd7839803d696c6cd0e3536c0631453eacded70 | [
"MIT"
] | null | null | null | 307.47139 | 82,555 | 0.905718 | true | 2,880 | Qwen/Qwen-72B | 1. YES
2. YES | 0.833325 | 0.737158 | 0.614292 | __label__glg_Latn | 0.970904 | 0.265537 |
# Constrained optimization
Now we will move to studying constrained optimizaton problems i.e., the full problem
$$
\begin{align} \
\min \quad &f(x)\\
\text{s.t.} \quad & g_j(x) \geq 0\text{ for all }j=1,\ldots,J\\
& h_k(x) = 0\text{ for all }k=1,\ldots,K\\
&a_i\leq x_i\leq b_i\text{ for all } i=1,\ldots,n\\
&x\in \mat... | 45256d4d304a6853e389d2698bef91a04485aac0 | 14,539 | ipynb | Jupyter Notebook | Lecture 6, Indirect methods for constrained optimization.ipynb | maeehart/TIES483 | cce5c779aeb0ade5f959a2ed5cca982be5cf2316 | [
"CC-BY-3.0"
] | 4 | 2019-04-26T12:46:14.000Z | 2021-11-23T03:38:59.000Z | Lecture 6, Indirect methods for constrained optimization.ipynb | maeehart/TIES483 | cce5c779aeb0ade5f959a2ed5cca982be5cf2316 | [
"CC-BY-3.0"
] | null | null | null | Lecture 6, Indirect methods for constrained optimization.ipynb | maeehart/TIES483 | cce5c779aeb0ade5f959a2ed5cca982be5cf2316 | [
"CC-BY-3.0"
] | 6 | 2016-01-08T16:28:11.000Z | 2021-04-10T05:18:10.000Z | 25.285217 | 321 | 0.528578 | true | 2,227 | Qwen/Qwen-72B | 1. YES
2. YES | 0.94079 | 0.897695 | 0.844543 | __label__eng_Latn | 0.981543 | 0.800488 |
# Laboratorio de física con Python
## Temario
* Simulación de ODE (de primer orden) [Proximamente, de orden superior!)
* Análisis de datos
- Transformación de datos, filtrado
- Ajuste de modelos
- Integración
- Derivación
* Adquisición de datos
* Gráficos
Importamos librerías: _numpy_ para análisis numérico, _... | 28dcb52ac12618a2eec3535582e5e3b44dd9023c | 203,947 | ipynb | Jupyter Notebook | python/Extras/Arduino/laboratorio.ipynb | LTGiardino/talleresfifabsas | a711b4425b0811478f21e6c405eeb4a52e889844 | [
"MIT"
] | 17 | 2015-10-23T17:14:34.000Z | 2021-12-31T02:18:29.000Z | python/Extras/Arduino/laboratorio.ipynb | LTGiardino/talleresfifabsas | a711b4425b0811478f21e6c405eeb4a52e889844 | [
"MIT"
] | 5 | 2016-04-03T23:39:11.000Z | 2020-04-03T02:09:02.000Z | python/Extras/Arduino/laboratorio.ipynb | LTGiardino/talleresfifabsas | a711b4425b0811478f21e6c405eeb4a52e889844 | [
"MIT"
] | 29 | 2015-10-16T04:16:01.000Z | 2021-09-18T16:55:48.000Z | 225.355801 | 26,954 | 0.890388 | true | 6,268 | Qwen/Qwen-72B | 1. YES
2. YES | 0.894789 | 0.774583 | 0.693089 | __label__spa_Latn | 0.761206 | 0.448609 |
# Binet's Formula
## Formula
Explicit formula to find the nth term of the Fibonacci sequence.
$\displaystyle F_n = \frac{1}{\sqrt{5}} \Bigg(\Bigg( \frac{1 + \sqrt{5}}{2} \Bigg)^n - \Bigg( \frac{1 - \sqrt{5}}{2} \Bigg)^n \Bigg)$
*Derived by Jacques Philippe Marie Binet, alreday known by Abraham de Moivre*
----
## ... | 6e6e90986c00390ebd9217509a085ab7b81c477e | 21,750 | ipynb | Jupyter Notebook | notebooks/math/number_theory/binets_formula.ipynb | sparkboom/my_jupyter_notes | 9255e4236b27f0419cdd2c8a2159738d8fc383be | [
"MIT"
] | null | null | null | notebooks/math/number_theory/binets_formula.ipynb | sparkboom/my_jupyter_notes | 9255e4236b27f0419cdd2c8a2159738d8fc383be | [
"MIT"
] | null | null | null | notebooks/math/number_theory/binets_formula.ipynb | sparkboom/my_jupyter_notes | 9255e4236b27f0419cdd2c8a2159738d8fc383be | [
"MIT"
] | null | null | null | 106.097561 | 14,436 | 0.849011 | true | 996 | Qwen/Qwen-72B | 1. YES
2. YES | 0.913677 | 0.855851 | 0.781971 | __label__eng_Latn | 0.594059 | 0.655113 |
# Exercise 1
## JIT the pressure poisson equation
The equation we need to unroll is given by
\begin{equation}
p_{i,j}^{n} = \frac{1}{4}\left(p_{i+1,j}^{n}+p_{i-1,j}^{n}+p_{i,j+1}^{n}+p_{i,j-1}^{n}\right) - b
\end{equation}
and recall that `b` is already computed, so no need to worry about unrolling that. We've also... | 0e5936f7cf5cec621e0e31c2a188ada3e097a3e4 | 4,223 | ipynb | Jupyter Notebook | notebooks/exercises/05.Cavity.Flow.Exercises.ipynb | gforsyth/numba_tutorial_scipy2017 | 01befd25218783f6d3fb803f55dd9e52f6072ff7 | [
"CC-BY-4.0"
] | 131 | 2017-06-23T10:18:26.000Z | 2022-03-27T21:16:56.000Z | notebooks/exercises/05.Cavity.Flow.Exercises.ipynb | gforsyth/numba_tutorial_scipy2017 | 01befd25218783f6d3fb803f55dd9e52f6072ff7 | [
"CC-BY-4.0"
] | 9 | 2017-06-11T21:20:59.000Z | 2018-10-18T13:57:30.000Z | notebooks/exercises/05.Cavity.Flow.Exercises.ipynb | gforsyth/numba_tutorial_scipy2017 | 01befd25218783f6d3fb803f55dd9e52f6072ff7 | [
"CC-BY-4.0"
] | 64 | 2017-06-26T13:04:48.000Z | 2022-01-11T20:36:31.000Z | 23.461111 | 206 | 0.460336 | true | 653 | Qwen/Qwen-72B | 1. YES
2. YES | 0.880797 | 0.888759 | 0.782816 | __label__eng_Latn | 0.89803 | 0.657077 |
```python
from sympy import *
from IPython.display import display, Latex, HTML, Markdown
init_printing()
from eqn_manip import *
from codegen_extras import *
import codegen_extras
from importlib import reload
from sympy.codegen.ast import Assignment, For, CodeBlock, real, Variable, Pointer, Declaration
from sympy.codeg... | 7b01ff500c6cfa45f4fff37c44f8da2857c39ab1 | 58,317 | ipynb | Jupyter Notebook | Wavefunctions/CubicSplineSolver.ipynb | QMCPACK/qmc_algorithms | 015fd1973e94f98662149418adc6b06dcd78946d | [
"MIT"
] | 3 | 2018-02-06T06:15:19.000Z | 2019-11-26T23:54:53.000Z | Wavefunctions/CubicSplineSolver.ipynb | chrinide/qmc_algorithms | 015fd1973e94f98662149418adc6b06dcd78946d | [
"MIT"
] | null | null | null | Wavefunctions/CubicSplineSolver.ipynb | chrinide/qmc_algorithms | 015fd1973e94f98662149418adc6b06dcd78946d | [
"MIT"
] | 4 | 2017-11-14T20:25:00.000Z | 2022-02-28T06:02:01.000Z | 31.403877 | 1,028 | 0.365434 | true | 9,653 | Qwen/Qwen-72B | 1. YES
2. YES | 0.859664 | 0.817574 | 0.702839 | __label__eng_Latn | 0.199713 | 0.471262 |
```python
from decodes.core import *
from decodes.io.jupyter_out import JupyterOut
import math
out = JupyterOut.unit_square( )
```
# Transformation Mathematics
We are familiar with a set of operations in CAD designated by verbs, such as "Move”, “Mirror”, “Rotate”, and “Scale”, and that ***act upon a geometric object... | 3c9cc74527c223690a3d4d7509cc2912e12c259c | 35,016 | ipynb | Jupyter Notebook | 107 - Transformations and Intersections/242 - Transformation Mathematics.ipynb | ksteinfe/decodes_ipynb | 2e4bb6b398472fc61ef8b88dad7babbdeb2a5754 | [
"MIT"
] | 1 | 2018-05-15T14:31:23.000Z | 2018-05-15T14:31:23.000Z | 107 - Transformations and Intersections/242 - Transformation Mathematics.ipynb | ksteinfe/decodes_ipynb | 2e4bb6b398472fc61ef8b88dad7babbdeb2a5754 | [
"MIT"
] | null | null | null | 107 - Transformations and Intersections/242 - Transformation Mathematics.ipynb | ksteinfe/decodes_ipynb | 2e4bb6b398472fc61ef8b88dad7babbdeb2a5754 | [
"MIT"
] | 2 | 2020-05-19T05:40:18.000Z | 2020-06-28T02:18:08.000Z | 41.439053 | 467 | 0.617061 | true | 5,839 | Qwen/Qwen-72B | 1. YES
2. YES | 0.695958 | 0.847968 | 0.59015 | __label__eng_Latn | 0.998084 | 0.209447 |
# Announcements
- No Problem Set this week, Problem Set 4 will be posted on 9/28.
- Stay on at the end of lecture if you want to ask questions about Problem Set 3.
<style>
@import url(https://www.numfys.net/static/css/nbstyle.css);
</style>
<a href="https://www.numfys.net"></a>
# Ordinary Differential Equations - hig... | 30c00abbaaa3111abafe96512375232710a15b33 | 28,444 | ipynb | Jupyter Notebook | Lectures/Lecture 12/Lecture12_ODE_part3.ipynb | astroarshn2000/PHYS305S20 | 18f4ebf0a51ba62fba34672cf76bd119d1db6f1e | [
"MIT"
] | 3 | 2020-09-10T06:45:46.000Z | 2020-10-20T13:50:11.000Z | Lectures/Lecture 12/Lecture12_ODE_part3.ipynb | astroarshn2000/PHYS305S20 | 18f4ebf0a51ba62fba34672cf76bd119d1db6f1e | [
"MIT"
] | null | null | null | Lectures/Lecture 12/Lecture12_ODE_part3.ipynb | astroarshn2000/PHYS305S20 | 18f4ebf0a51ba62fba34672cf76bd119d1db6f1e | [
"MIT"
] | null | null | null | 36.84456 | 598 | 0.544825 | true | 6,292 | Qwen/Qwen-72B | 1. YES
2. YES | 0.880797 | 0.91848 | 0.808995 | __label__eng_Latn | 0.987972 | 0.717899 |
# definition
数值定义. 对于 N-bit two's complement number system, 最高位 N-th bit 为符号位, 0 为正,
1 为负. 对于任意一个非负整数, 它的相反数为 its complement with respect to $2^N$.
# properties
- 一个数字的 two's complement 可以通过:
1. take its ones' complement and add one.
因为: the sum of a number and its ones' complement is -0, i.e. ‘1’ bits... | 57e58ee67b325cd5a78841b53ba007ba4ce08912 | 2,461 | ipynb | Jupyter Notebook | math/arithmetic/binary-arithmetic/two-s-complement.ipynb | Naitreey/notes-and-knowledge | 48603b2ad11c16d9430eb0293d845364ed40321c | [
"BSD-3-Clause"
] | 5 | 2018-05-16T06:06:45.000Z | 2021-05-12T08:46:18.000Z | math/arithmetic/binary-arithmetic/two-s-complement.ipynb | Naitreey/notes-and-knowledge | 48603b2ad11c16d9430eb0293d845364ed40321c | [
"BSD-3-Clause"
] | 2 | 2018-04-06T01:46:22.000Z | 2019-02-13T03:11:33.000Z | math/arithmetic/binary-arithmetic/two-s-complement.ipynb | Naitreey/notes-and-knowledge | 48603b2ad11c16d9430eb0293d845364ed40321c | [
"BSD-3-Clause"
] | 2 | 2019-04-11T11:02:32.000Z | 2020-06-27T11:59:09.000Z | 30.7625 | 102 | 0.502641 | true | 675 | Qwen/Qwen-72B | 1. YES
2. YES | 0.872347 | 0.875787 | 0.76399 | __label__eng_Latn | 0.872233 | 0.613338 |
```python
from sympy import *
from sympy.abc import m,M,l,b,c,g,t
from sympy.physics.mechanics import dynamicsymbols, init_vprinting
th = dynamicsymbols('theta')
x = dynamicsymbols('x')
dth = diff(th)
dx = diff(x)
ddth = diff(dth)
ddx = diff(dx)
init_vprinting()
```
```python
```
```python
ddth = (-(1/2)*m*l cos(t... | de761e7fe343e53c15b1cbb441c4f622da1a09df | 1,294 | ipynb | Jupyter Notebook | notebook.ipynb | dnlrbns/pendcart | 696c5d2c5fc7b787f3ab074e3ec3949a94dfc5ed | [
"MIT"
] | null | null | null | notebook.ipynb | dnlrbns/pendcart | 696c5d2c5fc7b787f3ab074e3ec3949a94dfc5ed | [
"MIT"
] | null | null | null | notebook.ipynb | dnlrbns/pendcart | 696c5d2c5fc7b787f3ab074e3ec3949a94dfc5ed | [
"MIT"
] | null | null | null | 21.213115 | 90 | 0.51391 | true | 174 | Qwen/Qwen-72B | 1. YES
2. YES | 0.90599 | 0.61878 | 0.560609 | __label__yue_Hant | 0.234876 | 0.140812 |
# The Harmonic Oscillator Strikes Back
*Note:* Much of this is adapted/copied from https://flothesof.github.io/harmonic-oscillator-three-methods-solution.html
This week we continue our adventures with the harmonic oscillator.
The harmonic oscillator is a system that, when displaced from its equilibrium position, ... | 5da4b5239749aaa9408ca4110a87007295b39726 | 87,460 | ipynb | Jupyter Notebook | harmonic_student.ipynb | sju-chem264-2019/new-10-14-10-m-jacobo | a80b342b8366f5203d08b8d572468b519067752c | [
"MIT"
] | null | null | null | harmonic_student.ipynb | sju-chem264-2019/new-10-14-10-m-jacobo | a80b342b8366f5203d08b8d572468b519067752c | [
"MIT"
] | null | null | null | harmonic_student.ipynb | sju-chem264-2019/new-10-14-10-m-jacobo | a80b342b8366f5203d08b8d572468b519067752c | [
"MIT"
] | null | null | null | 93.540107 | 11,464 | 0.804574 | true | 2,225 | Qwen/Qwen-72B | 1. YES
2. YES | 0.867036 | 0.746139 | 0.646929 | __label__eng_Latn | 0.880557 | 0.341364 |
# Sizing a mosfet using gm/Id method
This is an example you can use to calculate mosfet size in Sky130 for given design parameters. You can change the parameters below and recalculate.
```python
%pylab inline
import numpy as np
from scipy.interpolate import interp1d
import pint
ureg = pint.UnitRegistry() # convenie... | 14f0905464a6d9cee459617aaf0502b60725b1bf | 39,374 | ipynb | Jupyter Notebook | utils/gm_id_example.ipynb | tclarke/sky130radio | 4eca853b7e4fd6bc0d69998f65c04f97e73bee84 | [
"Apache-2.0"
] | 14 | 2020-09-28T19:41:26.000Z | 2021-10-05T01:40:00.000Z | utils/gm_id_example.ipynb | tclarke/sky130radio | 4eca853b7e4fd6bc0d69998f65c04f97e73bee84 | [
"Apache-2.0"
] | null | null | null | utils/gm_id_example.ipynb | tclarke/sky130radio | 4eca853b7e4fd6bc0d69998f65c04f97e73bee84 | [
"Apache-2.0"
] | 6 | 2020-07-30T21:54:19.000Z | 2021-02-07T07:58:12.000Z | 133.471186 | 16,484 | 0.893254 | true | 1,108 | Qwen/Qwen-72B | 1. YES
2. YES | 0.875787 | 0.826712 | 0.724023 | __label__eng_Latn | 0.736974 | 0.520481 |
# CHEM 1000 - Spring 2022
Prof. Geoffrey Hutchison, University of Pittsburgh
## 9 Probability
Chapter 9 in [*Mathematical Methods for Chemists*](http://sites.bu.edu/straub/mathematical-methods-for-molecular-science/)
(These lectures notes on probability and statistics will include substantial material not found in t... | 04a1b286199f64909f70542fb7ae2a8946c300a4 | 17,333 | ipynb | Jupyter Notebook | lectures/09a-probability.ipynb | ghutchis/chem1000 | 07a7eac20cc04ee9a1bdb98339fbd5653a02a38d | [
"CC-BY-4.0"
] | 12 | 2020-06-23T18:44:37.000Z | 2022-03-14T10:13:05.000Z | lectures/09a-probability.ipynb | ghutchis/chem1000 | 07a7eac20cc04ee9a1bdb98339fbd5653a02a38d | [
"CC-BY-4.0"
] | null | null | null | lectures/09a-probability.ipynb | ghutchis/chem1000 | 07a7eac20cc04ee9a1bdb98339fbd5653a02a38d | [
"CC-BY-4.0"
] | 4 | 2021-07-29T10:45:23.000Z | 2021-10-16T09:51:00.000Z | 35.373469 | 425 | 0.5868 | true | 3,084 | Qwen/Qwen-72B | 1. YES
2. YES | 0.718594 | 0.76908 | 0.552657 | __label__eng_Latn | 0.989087 | 0.122336 |
# **[HW6] DCGAN**
1. DataLoader
2. Model
3. Inception Score
4. Trainer
5. Train
이번 실습에서는 Convolution기반의 Generative Adversarial Network를 구현해서 이미지를 직접 생성해보는 실습을 진행해보겠습니다.
- dataset: CIFAR-10 (https://www.cs.toronto.edu/~kriz/cifar.html)
- model: DCGAN (https://arxiv.org/abs/1511.06434)
- evaluation: Inception Score (ht... | 43bb04f0c8dd862a6d5548827c328e58e74d3d02 | 25,331 | ipynb | Jupyter Notebook | Curriculum/03_Machine Learning/[HW6]DCGAN.ipynb | ohikendoit/Goorm-KAIST-NLP | 83b13a8599fd1588e99ef8513255a7058c482f0b | [
"MIT"
] | null | null | null | Curriculum/03_Machine Learning/[HW6]DCGAN.ipynb | ohikendoit/Goorm-KAIST-NLP | 83b13a8599fd1588e99ef8513255a7058c482f0b | [
"MIT"
] | null | null | null | Curriculum/03_Machine Learning/[HW6]DCGAN.ipynb | ohikendoit/Goorm-KAIST-NLP | 83b13a8599fd1588e99ef8513255a7058c482f0b | [
"MIT"
] | null | null | null | 25,331 | 25,331 | 0.591962 | true | 5,966 | Qwen/Qwen-72B | 1. YES
2. YES | 0.763484 | 0.654895 | 0.500001 | __label__kor_Hang | 0.955005 | 0 |
# Exercise 7) Learning and Planning
In this exercise, we will again investigate the inverted pendulum from the `gym` environment. We want to check, which benefits the implementation of planning offers.
Please note that the parameter $n$ has a different meaning in the context of planning (number of planning steps per ... | 7ee2361cd20cb9def9247e5cb233dc12da93334a | 856,697 | ipynb | Jupyter Notebook | exercises/solutions/ex07/LearningAndPlanning.ipynb | adilsheraz/reinforcement_learning_course_materials | e086ae7dcee2a0c1dbb329c2b25cf583c339c75a | [
"MIT"
] | 557 | 2020-07-20T08:38:15.000Z | 2022-03-31T19:30:35.000Z | exercises/solutions/ex07/LearningAndPlanning.ipynb | speedhunter001/reinforcement_learning_course_materials | 09a211da5707ba61cd653ab9f2a899b08357d6a3 | [
"MIT"
] | 7 | 2020-07-22T07:27:55.000Z | 2021-05-12T14:37:08.000Z | exercises/solutions/ex07/LearningAndPlanning.ipynb | speedhunter001/reinforcement_learning_course_materials | 09a211da5707ba61cd653ab9f2a899b08357d6a3 | [
"MIT"
] | 115 | 2020-09-08T17:12:25.000Z | 2022-03-31T18:13:08.000Z | 67.749862 | 53,556 | 0.696195 | true | 4,373 | Qwen/Qwen-72B | 1. YES
2. YES | 0.817574 | 0.847968 | 0.693277 | __label__eng_Latn | 0.918956 | 0.449046 |
```python
import sympy as sym
x, L, C, D, c_0, c_1, = sym.symbols('x L C D c_0 c_1')
def model1(f, L, D):
"""Solve -u'' = f(x), u(0)=0, u(L)=D."""
# Integrate twice
u_x = - sym.integrate(f, (x, 0, x)) + c_0
u = sym.integrate(u_x, (x, 0, x)) + c_1
# Set up 2 equations from the 2 boundary conditions ... | 066c129840a97eaf5f8341a7a803d54af933e636 | 3,729 | ipynb | Jupyter Notebook | Data Science and Machine Learning/Machine-Learning-In-Python-THOROUGH/EXAMPLES/FINITE_ELEMENTS/INTRO/SRC/39_U_XX_F_SYMPY.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/39_U_XX_F_SYMPY.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/39_U_XX_F_SYMPY.ipynb | okara83/Becoming-a-Data-Scientist | f09a15f7f239b96b77a2f080c403b2f3e95c9650 | [
"MIT"
] | 2 | 2022-02-09T15:41:33.000Z | 2022-02-11T07:47:40.000Z | 33 | 188 | 0.428533 | true | 961 | Qwen/Qwen-72B | 1. YES
2. YES | 0.907312 | 0.83762 | 0.759983 | __label__eng_Latn | 0.154743 | 0.604027 |
```python
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import pandas as pd
import scipy
import math
from collections import Counter
from warnings import filterwarnings
filterwarnings('ignore')
```
# Необходимые сведения из высшей математики (линейная алгебра, матем... | cafe74aa5771d986e882eb1516a7c50e78c32ace | 704,625 | ipynb | Jupyter Notebook | lessons/lec-DA-Maths.ipynb | yurichernyshov/Data-Science-Course-USURT | 6a9d87ff7dd88fc48b73f3250b8a37953811dc0e | [
"CC0-1.0"
] | 4 | 2020-10-02T10:46:12.000Z | 2022-02-14T14:11:04.000Z | lessons/lec-DA-Maths.ipynb | yurichernyshov/Data-Science-Course-USURT | 6a9d87ff7dd88fc48b73f3250b8a37953811dc0e | [
"CC0-1.0"
] | null | null | null | lessons/lec-DA-Maths.ipynb | yurichernyshov/Data-Science-Course-USURT | 6a9d87ff7dd88fc48b73f3250b8a37953811dc0e | [
"CC0-1.0"
] | 4 | 2021-01-27T08:39:25.000Z | 2022-02-14T14:11:01.000Z | 252.192198 | 127,180 | 0.921476 | true | 15,886 | Qwen/Qwen-72B | 1. YES
2. YES | 0.70253 | 0.787931 | 0.553545 | __label__rus_Cyrl | 0.554714 | 0.124401 |
We start by bringing in the Python libraries that we will be using for this code. We will be using Torch, which will enable us to run the code in the GPU for faster processing as compared to the CPU. Torch also assists us during backpropagation, using Autograd that does differentiation and stores the gradients that can... | 8f75aac42786164895b657d5cc0df8ddb09d39e9 | 24,432 | ipynb | Jupyter Notebook | Code for paper.ipynb | brianAsimba/Moon | 8bc6a745c5ada85f4636a3de54bb12bc043e923a | [
"MIT"
] | null | null | null | Code for paper.ipynb | brianAsimba/Moon | 8bc6a745c5ada85f4636a3de54bb12bc043e923a | [
"MIT"
] | null | null | null | Code for paper.ipynb | brianAsimba/Moon | 8bc6a745c5ada85f4636a3de54bb12bc043e923a | [
"MIT"
] | 1 | 2019-04-26T08:35:17.000Z | 2019-04-26T08:35:17.000Z | 37.99689 | 677 | 0.574411 | true | 5,643 | Qwen/Qwen-72B | 1. YES
2. YES | 0.92079 | 0.76908 | 0.708161 | __label__eng_Latn | 0.952628 | 0.483627 |
# **Basic Python & Jupyter**
## **Load YT Video**
```python
#load yt video
from IPython.display import YouTubeVideo
YouTubeVideo("HW29067qVWk",560,315,rel=0)
```
## **Interactive Widget**
```python
from ipywidgets import *
import ipywidgets as widgets
import numpy as np
def f(x):
return x
interact(... | 21fcf85a76fe8f1435a6ba704c5fdc375a5e92be | 154,504 | ipynb | Jupyter Notebook | sistem_kendali_pertemuan_1.ipynb | 2black0/python-control-laboratory | 005c15b6750c807c69a625b321ee04624acce8d9 | [
"MIT"
] | null | null | null | sistem_kendali_pertemuan_1.ipynb | 2black0/python-control-laboratory | 005c15b6750c807c69a625b321ee04624acce8d9 | [
"MIT"
] | null | null | null | sistem_kendali_pertemuan_1.ipynb | 2black0/python-control-laboratory | 005c15b6750c807c69a625b321ee04624acce8d9 | [
"MIT"
] | null | null | null | 79.847028 | 30,430 | 0.761715 | true | 731 | Qwen/Qwen-72B | 1. YES
2. YES | 0.757794 | 0.83762 | 0.634744 | __label__eng_Latn | 0.264048 | 0.313053 |
# Basic Image Standards
In this notebooks we are going to explore some of the different standards that are used to store photographs and similar images. Specifically, we will explore the following standards
* [TGA](https://en.wikipedia.org/wiki/Truevision_TGA)
* [PNG](https://www.w3.org/TR/2003/REC-PNG-20031110/)
* [... | a3fd3aef1da40fb4dc053b18601afe5e69936bd1 | 8,539 | ipynb | Jupyter Notebook | m4c_imgs/basic_image_standards.ipynb | chapmanbe/isys90069_2020_exploration | e73249d391acf195c4779955e3cb84f6562d42f6 | [
"MIT"
] | null | null | null | m4c_imgs/basic_image_standards.ipynb | chapmanbe/isys90069_2020_exploration | e73249d391acf195c4779955e3cb84f6562d42f6 | [
"MIT"
] | null | null | null | m4c_imgs/basic_image_standards.ipynb | chapmanbe/isys90069_2020_exploration | e73249d391acf195c4779955e3cb84f6562d42f6 | [
"MIT"
] | null | null | null | 26.684375 | 383 | 0.544092 | true | 1,324 | Qwen/Qwen-72B | 1. YES
2. YES | 0.654895 | 0.857768 | 0.561748 | __label__eng_Latn | 0.977062 | 0.143458 |
```
%matplotlib inline
from sympy import *
init_printing()
x = symbols('x')
```
```
f = 1 - sqrt((1+x)/x)
f
```
```
plot(f, (x, 0, 1))
```
```
f_strich = simplify(diff(f, x))
f_strich
```
```
krel = Abs(simplify(f_strich * x / f))
krel
```
$$\frac{1}{2} \left\lvert{\frac{\sqrt{\frac{1}{x} \left(x + 1\right)... | 5b0e51177c23a0436f5d67a9e30b8e2330e52ab1 | 47,130 | ipynb | Jupyter Notebook | Aufgabe 5b).ipynb | bschwb/Numerik | dcd178847104c382474142eae3365b6df76d8dbf | [
"MIT"
] | null | null | null | Aufgabe 5b).ipynb | bschwb/Numerik | dcd178847104c382474142eae3365b6df76d8dbf | [
"MIT"
] | null | null | null | Aufgabe 5b).ipynb | bschwb/Numerik | dcd178847104c382474142eae3365b6df76d8dbf | [
"MIT"
] | null | null | null | 160.306122 | 9,057 | 0.770592 | true | 926 | Qwen/Qwen-72B | 1. YES
2. YES | 0.879147 | 0.654895 | 0.575749 | __label__ltz_Latn | 0.102476 | 0.175987 |
## Theorem 0.0.2 (Approximate Caratheodory's Theorem)
In this worksheet we run through the proof of approximate Caratheodory, keeping an example to work with as we go. Please fill in code where indicated. Here is the theorem (slightly generalized) for reference:
**Theorem 0.0.2** (Generalized)**.** *Consider a set $T ... | 1e90e553da75dcb6bd38052b94bf7b749891dbd0 | 18,936 | ipynb | Jupyter Notebook | 2. Tuesday/Theorem0.0.2.ipynb | ArianNadjim/Tripods | f3c973251870e2e64af798f802798704d2f0249e | [
"MIT"
] | 3 | 2020-08-10T02:19:44.000Z | 2020-08-13T23:33:38.000Z | 2. Tuesday/Theorem0.0.2.ipynb | ArianNadjim/Tripods | f3c973251870e2e64af798f802798704d2f0249e | [
"MIT"
] | null | null | null | 2. Tuesday/Theorem0.0.2.ipynb | ArianNadjim/Tripods | f3c973251870e2e64af798f802798704d2f0249e | [
"MIT"
] | 3 | 2020-08-10T17:38:32.000Z | 2020-08-12T15:29:08.000Z | 59.54717 | 10,332 | 0.768166 | true | 1,433 | Qwen/Qwen-72B | 1. YES
2. YES | 0.835484 | 0.709019 | 0.592374 | __label__eng_Latn | 0.944514 | 0.214613 |
# Implementing FIR filters
In real-time filtering applications, filters are implemented by using some variation or other of their constant-coefficient difference equation (CCDE), so that one new output sample is generated for each new input sample. If all input data is available in advance, as in non-real-time (aka "o... | f70e9b9cc4638ee5b700d21c698c697a12f62d4d | 71,905 | ipynb | Jupyter Notebook | FIRimplementation/FIRImplementation.ipynb | hellgheast/COM303 | 48cfaf2ee2826662dd8f47f7aed8d7caf69ac489 | [
"MIT"
] | null | null | null | FIRimplementation/FIRImplementation.ipynb | hellgheast/COM303 | 48cfaf2ee2826662dd8f47f7aed8d7caf69ac489 | [
"MIT"
] | null | null | null | FIRimplementation/FIRImplementation.ipynb | hellgheast/COM303 | 48cfaf2ee2826662dd8f47f7aed8d7caf69ac489 | [
"MIT"
] | null | null | null | 126.149123 | 7,120 | 0.865976 | true | 3,142 | Qwen/Qwen-72B | 1. YES
2. YES | 0.839734 | 0.877477 | 0.736847 | __label__eng_Latn | 0.995584 | 0.550274 |
# Coherent dark states and polarization switching
Studying the effect of polarization switching on coherent dark states in a 9-level system. The system is made of two ground states, one excited state but all with J = 1 for a total of nine levels. Basically just 3x the 3-level system studied in "Coherent dark states in ... | 753d30d5fd0be309914b7722e6a01e94659c0144 | 177,613 | ipynb | Jupyter Notebook | examples/Coherent dark states and polarization switching.ipynb | otimgren/toy-systems | 017184e26ad19eb8497af7e7e4f3e7bb814d5807 | [
"MIT"
] | null | null | null | examples/Coherent dark states and polarization switching.ipynb | otimgren/toy-systems | 017184e26ad19eb8497af7e7e4f3e7bb814d5807 | [
"MIT"
] | null | null | null | examples/Coherent dark states and polarization switching.ipynb | otimgren/toy-systems | 017184e26ad19eb8497af7e7e4f3e7bb814d5807 | [
"MIT"
] | null | null | null | 279.265723 | 153,832 | 0.913914 | true | 5,748 | Qwen/Qwen-72B | 1. YES
2. YES | 0.785309 | 0.692642 | 0.543938 | __label__eng_Latn | 0.548561 | 0.102079 |
```python
# Make SymPy available to this program:
import sympy
from sympy import *
# Make GAlgebra available to this program:
from galgebra.ga import *
from galgebra.mv import *
from galgebra.printer import Fmt, GaPrinter, Format
# Fmt: sets the way that a multivector's basis expansion is output.
# Ga... | 99ace15d1fbbdf07263588dbb667a03986750bd8 | 3,785 | ipynb | Jupyter Notebook | python/GeometryAG/gaprimer/GAlgebraOutput.ipynb | karng87/nasm_game | a97fdb09459efffc561d2122058c348c93f1dc87 | [
"MIT"
] | null | null | null | python/GeometryAG/gaprimer/GAlgebraOutput.ipynb | karng87/nasm_game | a97fdb09459efffc561d2122058c348c93f1dc87 | [
"MIT"
] | null | null | null | python/GeometryAG/gaprimer/GAlgebraOutput.ipynb | karng87/nasm_game | a97fdb09459efffc561d2122058c348c93f1dc87 | [
"MIT"
] | null | null | null | 23.955696 | 98 | 0.520476 | true | 579 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.835484 | 0.741245 | __label__eng_Latn | 0.850503 | 0.560492 |
# Generalization: reflecting boundaries
<div id="wave:pde2:Neumann"></div>
The boundary condition $u=0$ in a wave equation reflects the wave, but
$u$ changes sign at the boundary, while the condition $u_x=0$ reflects
the wave as a mirror and preserves the sign, see a [web page](mov-wave/demo_BC_gaussian/index.html) or... | 091094a1b3a0099b1fc1fc31edaf8d7424be1ef1 | 142,673 | ipynb | Jupyter Notebook | fdm-devito-notebooks/02_wave/wave1D_fd2.ipynb | devitocodes/devito_book | 30405c3d440a1f89df69594fd0704f69650c1ded | [
"CC-BY-4.0"
] | 7 | 2020-07-17T13:19:15.000Z | 2021-03-27T05:21:09.000Z | fdm-devito-notebooks/02_wave/wave1D_fd2.ipynb | devitocodes/devito_book | 30405c3d440a1f89df69594fd0704f69650c1ded | [
"CC-BY-4.0"
] | 73 | 2020-07-14T15:38:52.000Z | 2020-09-25T11:54:59.000Z | fdm-devito-notebooks/02_wave/wave1D_fd2.ipynb | devitocodes/devito_book | 30405c3d440a1f89df69594fd0704f69650c1ded | [
"CC-BY-4.0"
] | 1 | 2021-03-27T05:21:14.000Z | 2021-03-27T05:21:14.000Z | 33.156635 | 200 | 0.522201 | true | 31,166 | Qwen/Qwen-72B | 1. YES
2. YES | 0.815232 | 0.822189 | 0.670275 | __label__eng_Latn | 0.97874 | 0.395605 |
```julia
using DifferentialEquations
using Plots
```
Consider the simple reaction:
\begin{align}
A &\longleftrightarrow B\\
B &\longleftrightarrow C\\
\end{align}
Both are elementary steps that occur in the liquid phase, and we will consider it in a few different solvent environments.
```julia
gammaA(XA, X... | d0e383c262941cb3bae976cf83a378ee1a70408c | 694,451 | ipynb | Jupyter Notebook | 2021_JCAT_DeDonder_Solvents/Case Study 2.ipynb | jqbond/Research_Public | a6eb581e4e3e72f40fd6c7e900b6f4b30311076f | [
"MIT"
] | null | null | null | 2021_JCAT_DeDonder_Solvents/Case Study 2.ipynb | jqbond/Research_Public | a6eb581e4e3e72f40fd6c7e900b6f4b30311076f | [
"MIT"
] | null | null | null | 2021_JCAT_DeDonder_Solvents/Case Study 2.ipynb | jqbond/Research_Public | a6eb581e4e3e72f40fd6c7e900b6f4b30311076f | [
"MIT"
] | null | null | null | 177.472783 | 16,447 | 0.684044 | true | 2,386 | Qwen/Qwen-72B | 1. YES
2. YES | 0.865224 | 0.651355 | 0.563568 | __label__yue_Hant | 0.206957 | 0.147687 |
# Monte Carlo - Doble Pozo
\begin{equation}
V(x)=E_{0}\left[ \left(\frac{x}{a}\right)^4 -2\left(\frac{x}{a}\right)^2 \right]-\frac{b}{a}x
\end{equation}
```python
import openmm as mm
from openmm import app
from openmm import unit
from openmmtools.constants import kB
import numpy as np
from tqdm import tqdm
import m... | 0f7586853f6527893dc68e9e83421e754fd02559 | 34,815 | ipynb | Jupyter Notebook | Tarea5/Example.ipynb | dprada/DIY_MD | 6fb4f880616a558a03d67f1cbb8426ccda6cd4e2 | [
"MIT"
] | null | null | null | Tarea5/Example.ipynb | dprada/DIY_MD | 6fb4f880616a558a03d67f1cbb8426ccda6cd4e2 | [
"MIT"
] | null | null | null | Tarea5/Example.ipynb | dprada/DIY_MD | 6fb4f880616a558a03d67f1cbb8426ccda6cd4e2 | [
"MIT"
] | 1 | 2022-02-15T21:10:09.000Z | 2022-02-15T21:10:09.000Z | 101.501458 | 26,664 | 0.859687 | true | 1,070 | Qwen/Qwen-72B | 1. YES
2. YES | 0.831143 | 0.672332 | 0.558804 | __label__kor_Hang | 0.154386 | 0.136618 |
# Sample Coding Exercise : Interpolation
- https://www.hackerrank.com/contests/intro-to-statistics/challenges/temperature-predictions/problem
- Take care with 2-D: you may need to use the correlation in the variables to improve the fit!\
```python
%matplotlib inline
from IPython.core.display import display, HTML
i... | 6d6fd1595ecd5456da6df23693002ffd5880f1ac | 288,799 | ipynb | Jupyter Notebook | Tutorials/Interpolation/.ipynb_checkpoints/Practice_interp-checkpoint.ipynb | rlbellaire/MyPythonTools | 3816e735aa24b2f317b083f010e5c138dcc7b56c | [
"MIT"
] | null | null | null | Tutorials/Interpolation/.ipynb_checkpoints/Practice_interp-checkpoint.ipynb | rlbellaire/MyPythonTools | 3816e735aa24b2f317b083f010e5c138dcc7b56c | [
"MIT"
] | null | null | null | Tutorials/Interpolation/.ipynb_checkpoints/Practice_interp-checkpoint.ipynb | rlbellaire/MyPythonTools | 3816e735aa24b2f317b083f010e5c138dcc7b56c | [
"MIT"
] | 1 | 2021-04-27T02:31:27.000Z | 2021-04-27T02:31:27.000Z | 118.408774 | 55,128 | 0.81645 | true | 16,546 | Qwen/Qwen-72B | 1. YES
2. YES | 0.828939 | 0.705785 | 0.585053 | __label__eng_Latn | 0.169704 | 0.197603 |
Contenido bajo licencia Creative Commons BY 4.0 y código bajo licencia MIT. © Juan Gómez y Nicolás Guarín-Zapata 2020. Este material es parte del curso Modelación Computacional en el programa de Ingeniería Civil de la Universidad EAFIT.
# Interpolación en 2D
## Introducción
Acá extenderemos el esquema de interpolaci... | e3bd36d1a0664a2e19a60fb4a19f3d66de8f40c0 | 98,449 | ipynb | Jupyter Notebook | notebooks/02c_interpolacion_2d.ipynb | AppliedMechanics-EAFIT/Mod_Temporal | 6a0506d906ed42b143b773777e8dc0da5af763eb | [
"MIT"
] | 5 | 2019-02-20T18:14:01.000Z | 2020-07-19T22:44:44.000Z | notebooks/02c_interpolacion_2d.ipynb | AppliedMechanics-EAFIT/Mod_Temporal | 6a0506d906ed42b143b773777e8dc0da5af763eb | [
"MIT"
] | 3 | 2020-04-15T00:22:58.000Z | 2020-07-04T17:03:54.000Z | notebooks/02c_interpolacion_2d.ipynb | AppliedMechanics-EAFIT/Mod_Temporal | 6a0506d906ed42b143b773777e8dc0da5af763eb | [
"MIT"
] | 3 | 2020-05-14T18:17:09.000Z | 2020-10-27T06:37:05.000Z | 71.288197 | 36,147 | 0.703664 | true | 4,039 | Qwen/Qwen-72B | 1. YES
2. YES | 0.826712 | 0.754915 | 0.624097 | __label__spa_Latn | 0.979959 | 0.288317 |
```python
import tensorflow as tf
```
```python
from pycalphad import Database, Model, variables as v
from pycalphad.codegen.sympydiff_utils import build_functions
from sympy import lambdify
import numpy as np
dbf = Database('Al-Cu-Zr_Zhou.tdb')
mod = Model(dbf, ['AL', 'CU', 'ZR'], 'LIQUID')
```
```python
mod.vari... | 322e9c803078b5573f58fa53b0aaac104dd8ad17 | 5,437 | ipynb | Jupyter Notebook | Tensorflow-XLA.ipynb | richardotis/pycalphad-sandbox | 43d8786eee8f279266497e9c5f4630d19c893092 | [
"MIT"
] | 1 | 2017-03-08T18:21:30.000Z | 2017-03-08T18:21:30.000Z | Tensorflow-XLA.ipynb | richardotis/pycalphad-sandbox | 43d8786eee8f279266497e9c5f4630d19c893092 | [
"MIT"
] | null | null | null | Tensorflow-XLA.ipynb | richardotis/pycalphad-sandbox | 43d8786eee8f279266497e9c5f4630d19c893092 | [
"MIT"
] | 1 | 2018-11-03T01:31:57.000Z | 2018-11-03T01:31:57.000Z | 21.073643 | 103 | 0.510576 | true | 813 | Qwen/Qwen-72B | 1. YES
2. YES | 0.774583 | 0.705785 | 0.546689 | __label__eng_Latn | 0.353857 | 0.108472 |
# Clustering Techniques Writeup
### September 26, 2016
### K-Means Clustering
K-Means clustering is a regression technique which involves 'fitting' a number $n$ of given values from a dataset around a pre-defined number of $k$ clusters. The K-means clustering process seeks to minimize the Euclidean distance from eac... | 0f4b8f9436902b82fe1850c289ac1deadbba56bc | 12,987 | ipynb | Jupyter Notebook | examples/Jupyter/.ipynb_checkpoints/Clustering Techniques-checkpoint.ipynb | jonl1096/seelvizorg | ae4e3567ce89eb62edcd742060619fdf1883b991 | [
"Apache-2.0"
] | null | null | null | examples/Jupyter/.ipynb_checkpoints/Clustering Techniques-checkpoint.ipynb | jonl1096/seelvizorg | ae4e3567ce89eb62edcd742060619fdf1883b991 | [
"Apache-2.0"
] | 2 | 2017-04-18T02:50:14.000Z | 2017-04-18T18:04:20.000Z | Jupyter/.ipynb_checkpoints/Clustering Techniques-checkpoint.ipynb | NeuroDataDesign/seelviz-archive | cb9bcf7c0f32f0256f71be59dd7d7a9086d0f3b3 | [
"Apache-2.0"
] | null | null | null | 61.549763 | 641 | 0.653654 | true | 2,682 | Qwen/Qwen-72B | 1. YES
2. YES | 0.877477 | 0.901921 | 0.791414 | __label__eng_Latn | 0.998578 | 0.677053 |
```python
# 그래프, 수학 기능 추가
# Add graph and math features
import pylab as py
import numpy as np
import numpy.linalg as nl
# 기호 연산 기능 추가
# Add symbolic operation capability
import sympy as sy
```
# 임의하중하 단순지지보의 반력<br>Reaction forces of a simple supported beam under a general load
다음과 같은 보의 반력을 구해 보자.<br>
Let's try to... | 0f89961e0780439f33e0dab2bfa9bdf3bb74bacd | 7,045 | ipynb | Jupyter Notebook | 45_sympy/20_Beam_Reaction_Force_General.ipynb | kangwonlee/2009eca-nmisp-template | 46a09c988c5e0c4efd493afa965d4a17d32985e8 | [
"BSD-3-Clause"
] | null | null | null | 45_sympy/20_Beam_Reaction_Force_General.ipynb | kangwonlee/2009eca-nmisp-template | 46a09c988c5e0c4efd493afa965d4a17d32985e8 | [
"BSD-3-Clause"
] | null | null | null | 45_sympy/20_Beam_Reaction_Force_General.ipynb | kangwonlee/2009eca-nmisp-template | 46a09c988c5e0c4efd493afa965d4a17d32985e8 | [
"BSD-3-Clause"
] | null | null | null | 17.971939 | 137 | 0.474663 | true | 944 | Qwen/Qwen-72B | 1. YES
2. YES | 0.891811 | 0.73412 | 0.654696 | __label__kor_Hang | 0.925317 | 0.359409 |
# Free-Body Diagram for particles
> Renato Naville Watanabe
> [Laboratory of Biomechanics and Motor Control](http://pesquisa.ufabc.edu.br/bmclab)
> Federal University of ABC, Brazil
```python
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set_context('notebook', fon... | 03e2fc952088163e07b0e5c3699b37f371fd60a4 | 201,578 | ipynb | Jupyter Notebook | notebooks/FBDParticles.ipynb | e-moncao-lima/BMC | 98c3abbf89e630d64b695b535b0be4ddc8b2724b | [
"CC-BY-4.0"
] | null | null | null | notebooks/FBDParticles.ipynb | e-moncao-lima/BMC | 98c3abbf89e630d64b695b535b0be4ddc8b2724b | [
"CC-BY-4.0"
] | null | null | null | notebooks/FBDParticles.ipynb | e-moncao-lima/BMC | 98c3abbf89e630d64b695b535b0be4ddc8b2724b | [
"CC-BY-4.0"
] | 1 | 2018-10-13T17:35:16.000Z | 2018-10-13T17:35:16.000Z | 149.760773 | 33,965 | 0.845559 | true | 13,561 | Qwen/Qwen-72B | 1. YES
2. YES | 0.72487 | 0.743168 | 0.5387 | __label__eng_Latn | 0.739534 | 0.089911 |
```python
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 16 20:23:07 2021
@author: gansa001
"""
from sympy import *
from sympy.plotting import plot
import matplotlib.pyplot as plt
import numpy as np
import math
```
### 1. Write a computer program to calculate the Lagrange interpolation polynomial Pn(x) to f(x) such t... | 0cc319a6271dbe222c3b607104b7c90822e65a1b | 285,396 | ipynb | Jupyter Notebook | Lagrange-Chebyshev Interpolation Error.ipynb | GJAnsah/Lagrangian | 8619f905fff0943242e3069404f49d55d8ca3f5a | [
"MIT"
] | null | null | null | Lagrange-Chebyshev Interpolation Error.ipynb | GJAnsah/Lagrangian | 8619f905fff0943242e3069404f49d55d8ca3f5a | [
"MIT"
] | null | null | null | Lagrange-Chebyshev Interpolation Error.ipynb | GJAnsah/Lagrangian | 8619f905fff0943242e3069404f49d55d8ca3f5a | [
"MIT"
] | null | null | null | 448.735849 | 33,720 | 0.935644 | true | 1,479 | Qwen/Qwen-72B | 1. YES
2. YES | 0.899121 | 0.875787 | 0.787439 | __label__eng_Latn | 0.675676 | 0.667817 |
# 05 The Closed-Shell CCSD energy
The coupled cluster model provides a higher level of accuracy beyond the MP2 approach. The purpose of this project is to understand the fundamental aspects of the calculation of the CCSD (coupled cluster singles and doubles) energy. Reference to this project is [Hirata, ..., Bartlett,... | 21bb0dca8e97b353b9bccc2684e218745351b2bc | 69,019 | ipynb | Jupyter Notebook | source/Project_05/Project_05.ipynb | ajz34/PyCrawfordProgProj | d2ba51223a4e6e56deefc5c0d68aa4e663fbcd80 | [
"Apache-2.0"
] | 13 | 2020-08-13T06:59:08.000Z | 2022-03-21T15:48:09.000Z | source/Project_05/Project_05.ipynb | ajz34/PyCrawfordProgProj | d2ba51223a4e6e56deefc5c0d68aa4e663fbcd80 | [
"Apache-2.0"
] | null | null | null | source/Project_05/Project_05.ipynb | ajz34/PyCrawfordProgProj | d2ba51223a4e6e56deefc5c0d68aa4e663fbcd80 | [
"Apache-2.0"
] | 3 | 2021-04-26T03:28:48.000Z | 2021-09-06T21:04:07.000Z | 31.343778 | 670 | 0.511062 | true | 15,577 | Qwen/Qwen-72B | 1. YES
2. YES | 0.808067 | 0.737158 | 0.595673 | __label__eng_Latn | 0.634057 | 0.222279 |
# Spectral Estimation of Random 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).*
## The Periodogram
The [periodogram](htt... | c987c26c97cdc0db096046443b7d5a4412e23e0b | 120,071 | ipynb | Jupyter Notebook | spectral_estimation_random_signals/periodogram.ipynb | Fun-pee/signal-processing | 205d5e55e3168a1ec9da76b569af92c0056619aa | [
"MIT"
] | 3 | 2020-09-21T10:15:40.000Z | 2020-09-21T13:36:40.000Z | spectral_estimation_random_signals/periodogram.ipynb | jools76/digital-signal-processing-lecture | 4bdfe13fa4a7502412f3f0d54deb8f034aef1ce2 | [
"MIT"
] | null | null | null | spectral_estimation_random_signals/periodogram.ipynb | jools76/digital-signal-processing-lecture | 4bdfe13fa4a7502412f3f0d54deb8f034aef1ce2 | [
"MIT"
] | null | null | null | 67.191382 | 24,554 | 0.616985 | true | 1,706 | Qwen/Qwen-72B | 1. YES
2. YES | 0.90053 | 0.861538 | 0.775841 | __label__eng_Latn | 0.970788 | 0.640871 |
# Systems of Equations
Imagine you are at a casino, and you have a mixture of £10 and £25 chips. You know that you have a total of 16 chips, and you also know that the total value of chips you have is £250. Is this enough information to determine how many of each denomination of chip you have?
Well, we can express eac... | 964b5bce027d0e3f21a1161453352a5454a478ad | 23,218 | ipynb | Jupyter Notebook | MathsToML/Module01-Equations, Graphs, and Functions/01-03-Systems of Equations.ipynb | hpaucar/data-mining-repo | d0e48520bc6c01d7cb72e882154cde08020e1d33 | [
"MIT"
] | null | null | null | MathsToML/Module01-Equations, Graphs, and Functions/01-03-Systems of Equations.ipynb | hpaucar/data-mining-repo | d0e48520bc6c01d7cb72e882154cde08020e1d33 | [
"MIT"
] | null | null | null | MathsToML/Module01-Equations, Graphs, and Functions/01-03-Systems of Equations.ipynb | hpaucar/data-mining-repo | d0e48520bc6c01d7cb72e882154cde08020e1d33 | [
"MIT"
] | null | null | null | 331.685714 | 17,318 | 0.889913 | true | 1,222 | Qwen/Qwen-72B | 1. YES
2. YES | 0.893309 | 0.941654 | 0.841189 | __label__eng_Latn | 0.999421 | 0.792696 |
$\newcommand{\rads}{~rad.s$^{-1}$}$
$\newcommand{\bnabla}{\boldsymbol{\nabla}}$
$\newcommand{\eexp}[1]{\textrm{e}^{#1}}$
$\newcommand{\glm}[1]{\overline{#1}^L}$
$\newcommand{\di}[0]{\textrm{d}}$
$\newcommand{\bs}[1]{\boldsymbol{#1}}$
$\newcommand{\ode}[2]{\frac{\di {#1}}{\di {#2}}}$
$\newcommand{\oden}[3]{\frac{\di^{#1... | b9f420395baf23a90fc6910c19076bcb7caed411 | 115,500 | ipynb | Jupyter Notebook | PHY293/C04-Coupling.ipynb | ngrisouard/TenureApplicationCode | 68f60dcfea11cdbbad17cf0b231e55cc37c32f38 | [
"MIT"
] | 1 | 2021-12-12T11:26:43.000Z | 2021-12-12T11:26:43.000Z | PHY293/C04-Coupling.ipynb | ngrisouard/TenureApplicationCode | 68f60dcfea11cdbbad17cf0b231e55cc37c32f38 | [
"MIT"
] | null | null | null | PHY293/C04-Coupling.ipynb | ngrisouard/TenureApplicationCode | 68f60dcfea11cdbbad17cf0b231e55cc37c32f38 | [
"MIT"
] | 1 | 2021-12-12T11:26:44.000Z | 2021-12-12T11:26:44.000Z | 34.601558 | 708 | 0.563411 | true | 21,817 | Qwen/Qwen-72B | 1. YES
2. YES | 0.712232 | 0.749087 | 0.533524 | __label__eng_Latn | 0.994598 | 0.077884 |
# Lab03: Machine learning
- MSSV:
- Họ và tên:
## Yêu cầu bài tập
**Cách làm bài**
Bạn sẽ làm trực tiếp trên file notebook này; trong file, từ `TODO` để cho biết những phần mà bạn cần phải làm.
Bạn có thể thảo luận ý tưởng cũng như tham khảo các tài liệu, nhưng *code và bài làm phải là của bạn*.
Nếu vi phạm th... | 5a84660357b97018a450e5dbc7428995e1bbb24d | 37,247 | ipynb | Jupyter Notebook | lab03/.ipynb_checkpoints/Lab03-MachineLearning-checkpoint.ipynb | nhutnamhcmus/decision-tree-bayes | 5d8548bb84d3bbe4a8b3d53f193d4cec23b4177c | [
"MIT"
] | null | null | null | lab03/.ipynb_checkpoints/Lab03-MachineLearning-checkpoint.ipynb | nhutnamhcmus/decision-tree-bayes | 5d8548bb84d3bbe4a8b3d53f193d4cec23b4177c | [
"MIT"
] | null | null | null | lab03/.ipynb_checkpoints/Lab03-MachineLearning-checkpoint.ipynb | nhutnamhcmus/decision-tree-bayes | 5d8548bb84d3bbe4a8b3d53f193d4cec23b4177c | [
"MIT"
] | null | null | null | 44.500597 | 7,752 | 0.663194 | true | 3,614 | Qwen/Qwen-72B | 1. YES
2. YES | 0.855851 | 0.785309 | 0.672107 | __label__vie_Latn | 0.916382 | 0.399861 |
### Introduction to simplicial homology
```python
# uncomment to install the panel library
#pip install panel
import numpy as np
import matplotlib.pyplot as plt
from bokeh import palettes
%config InlineBackend.figure_format="retina"
import panel as pn
pn.extension()
```
### Affinely independent points
We say ... | feda3cd605cd88ccad2ad5e6bbb0c0166c90b697 | 173,841 | ipynb | Jupyter Notebook | notebooks/simplicial_homology.ipynb | manuflores/sandbox | 27b44dfb6bea20d56ece640c5db9d842cbfc424b | [
"MIT"
] | null | null | null | notebooks/simplicial_homology.ipynb | manuflores/sandbox | 27b44dfb6bea20d56ece640c5db9d842cbfc424b | [
"MIT"
] | null | null | null | notebooks/simplicial_homology.ipynb | manuflores/sandbox | 27b44dfb6bea20d56ece640c5db9d842cbfc424b | [
"MIT"
] | null | null | null | 162.468224 | 67,323 | 0.828982 | true | 2,018 | Qwen/Qwen-72B | 1. YES
2. YES | 0.874077 | 0.79053 | 0.690985 | __label__eng_Latn | 0.377041 | 0.44372 |
Overview of "A Quantum Approximate Optimization Algorithm" written by Edward Farhi, Jeffrey Goldstone and Sam Gutmann.
# Introduction:
Combinatorial optimization problems attempt to optimize an objective function over *n* bits with respect to *m* clauses. The bits are grouped into a string $z = z_1z_2...z_n$, whil... | f00178369ba99ab381e10b4b9392eeab62b7b7c3 | 216,828 | ipynb | Jupyter Notebook | notebooks/unused/qaoa_cirq.ipynb | bernalde/QuIPML | a4593210b2dffa01561e6aafb01136471a0628cb | [
"MIT"
] | 1 | 2021-11-08T21:42:27.000Z | 2021-11-08T21:42:27.000Z | notebooks/unused/qaoa_cirq.ipynb | bernalde/QuIPML | a4593210b2dffa01561e6aafb01136471a0628cb | [
"MIT"
] | null | null | null | notebooks/unused/qaoa_cirq.ipynb | bernalde/QuIPML | a4593210b2dffa01561e6aafb01136471a0628cb | [
"MIT"
] | 1 | 2021-09-10T06:08:44.000Z | 2021-09-10T06:08:44.000Z | 263.781022 | 96,456 | 0.851145 | true | 9,805 | Qwen/Qwen-72B | 1. YES
2. YES | 0.942507 | 0.824462 | 0.777061 | __label__eng_Latn | 0.97386 | 0.643705 |
### Action of boundary and co-boundary maps on a chain
__*Definition.*__ An abstract simplicial complex $K$ is a collection of finite sets that is closed
under set inclusion, i.e. if $\sigma \in K$ and $\tau \subseteq \sigma$, then $\tau \in K$.
__*Definition.*__ The boundary operator $\partial_d : C_d(K) \rightarrow... | 425c6516b9a46b0a30befc2e9a439e0657d2b320 | 3,425 | ipynb | Jupyter Notebook | examples/theory_simplicial_diffusion.ipynb | tsitsvero/hodgelaplacians | e03f96bf81de05fb93911b21f4f95443dcb3cec6 | [
"MIT"
] | 13 | 2019-06-17T13:07:04.000Z | 2022-01-24T09:13:03.000Z | examples/theory_simplicial_diffusion.ipynb | tsitsvero/hodgelaplacians | e03f96bf81de05fb93911b21f4f95443dcb3cec6 | [
"MIT"
] | 1 | 2021-10-01T16:47:29.000Z | 2021-12-09T07:26:54.000Z | examples/theory_simplicial_diffusion.ipynb | tsitsvero/hodgelaplacians | e03f96bf81de05fb93911b21f4f95443dcb3cec6 | [
"MIT"
] | 3 | 2021-08-31T00:48:04.000Z | 2021-12-21T16:18:51.000Z | 43.35443 | 203 | 0.581898 | true | 810 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.83762 | 0.74314 | __label__eng_Latn | 0.720956 | 0.564896 |
# Model project: Cournot competition
In the following project, we searh to model how two competing firms determine the optimal amount of a homogenous good to produce in Cournot competition.
We are assuming the following points throughout the assignment:
- There are two firms (1 and 2), who produce the same good (hom... | f7811e5d41caa6910f93e7659dec2d9d0953b67b | 60,577 | ipynb | Jupyter Notebook | modelproject/Modelproject-1.ipynb | NumEconCopenhagen/projects-2020-amalie-asima-marina-1 | a8d6ff30b018d063094ce69a0bc5fd1b302fa75f | [
"MIT"
] | null | null | null | modelproject/Modelproject-1.ipynb | NumEconCopenhagen/projects-2020-amalie-asima-marina-1 | a8d6ff30b018d063094ce69a0bc5fd1b302fa75f | [
"MIT"
] | 12 | 2020-04-13T10:30:30.000Z | 2020-05-11T19:18:26.000Z | modelproject/Modelproject-1.ipynb | NumEconCopenhagen/projects-2020-amalie-asima-marina-1 | a8d6ff30b018d063094ce69a0bc5fd1b302fa75f | [
"MIT"
] | 3 | 2020-03-12T08:34:51.000Z | 2021-05-12T15:52:01.000Z | 111.765683 | 24,124 | 0.865543 | true | 2,553 | Qwen/Qwen-72B | 1. YES
2. YES | 0.882428 | 0.737158 | 0.650489 | __label__eng_Latn | 0.97103 | 0.349635 |
Problem 1 (20 points)
Show that the stationary point (zero gradient) of the function$$
\begin{aligned}
f=2x_{1}^{2} - 4x_1 x_2+ 1.5x^{2}_{2}+ x_2
\end{aligned}
$$is a saddle (with indefinite Hessian). Find the directions of downslopes away from the saddle. Hint: Use Taylor's expansion at the saddle point. Find dire... | be4f3179814a85fb033775235e3b5f496402857c | 3,006 | ipynb | Jupyter Notebook | Imcomplete_HW2.ipynb | MrNobodyInCamelCase/Trail_repo | 2508eef78e9793945d46c2394a61633e693387b7 | [
"Apache-2.0"
] | null | null | null | Imcomplete_HW2.ipynb | MrNobodyInCamelCase/Trail_repo | 2508eef78e9793945d46c2394a61633e693387b7 | [
"Apache-2.0"
] | null | null | null | Imcomplete_HW2.ipynb | MrNobodyInCamelCase/Trail_repo | 2508eef78e9793945d46c2394a61633e693387b7 | [
"Apache-2.0"
] | null | null | null | 29.184466 | 184 | 0.541916 | true | 567 | Qwen/Qwen-72B | 1. YES
2. YES | 0.942507 | 0.894789 | 0.843345 | __label__eng_Latn | 0.799061 | 0.797706 |
```python
import names
from syft.core.common import UID
from sympy import symbols
from scipy import optimize
import sympy as sym
import numpy as np
import random
from sympy.solvers import solve
from functools import lru_cache
# ordered_symbols = list()
# for i in range(100):
# ordered_symbols.append(symbols("s"+s... | 8664411e6b1afbb23775436cd9b3742bd1fb09ad | 43,214 | ipynb | Jupyter Notebook | packages/syft/examples/experimental/adversarial_accountant/Untitled1.ipynb | callezenwaka/PySyft | 2545c302441cfe727ec095c4f9aa136bff02be32 | [
"Apache-1.1"
] | 2 | 2022-02-18T03:48:27.000Z | 2022-03-05T06:13:57.000Z | packages/syft/examples/experimental/adversarial_accountant/Untitled1.ipynb | callezenwaka/PySyft | 2545c302441cfe727ec095c4f9aa136bff02be32 | [
"Apache-1.1"
] | 3 | 2021-11-17T15:34:03.000Z | 2021-12-08T14:39:10.000Z | packages/syft/examples/experimental/adversarial_accountant/Untitled1.ipynb | callezenwaka/PySyft | 2545c302441cfe727ec095c4f9aa136bff02be32 | [
"Apache-1.1"
] | 1 | 2021-08-19T12:23:01.000Z | 2021-08-19T12:23:01.000Z | 39.357013 | 864 | 0.528648 | true | 8,363 | Qwen/Qwen-72B | 1. YES
2. YES | 0.890294 | 0.757794 | 0.67466 | __label__eng_Latn | 0.661755 | 0.405792 |
```python
import ambulance_game as abg
import numpy as np
import sympy as sym
from sympy.abc import a,b,c,d,e,f,g,h,i,j
```
# Classic Markov Chain
```python
def get_P0(lambda_2, lambda_1, mu, num_of_servers, threshold):
ro = (lambda_2 + lambda_1) / (mu * num_of_servers)
summation_1 = np.sum(
[
... | 50ecbf11c2bf66e6b7adb1ed5af20901a4790874 | 998,281 | ipynb | Jupyter Notebook | nbs/src/Markov/closed_form_formula_of_pi/investigate-close-form-pi.ipynb | 11michalis11/AmbulanceDecisionGame | 45164ba51da0417297f715e41716cb91facc120f | [
"MIT"
] | null | null | null | nbs/src/Markov/closed_form_formula_of_pi/investigate-close-form-pi.ipynb | 11michalis11/AmbulanceDecisionGame | 45164ba51da0417297f715e41716cb91facc120f | [
"MIT"
] | 20 | 2020-04-20T09:08:31.000Z | 2021-09-23T11:09:25.000Z | nbs/src/Markov/closed_form_formula_of_pi/investigate-close-form-pi.ipynb | 11michalis11/AmbulanceDecisionGame | 45164ba51da0417297f715e41716cb91facc120f | [
"MIT"
] | null | null | null | 96.043968 | 23,541 | 0.583995 | true | 74,637 | Qwen/Qwen-72B | 1. YES
2. YES | 0.924142 | 0.857768 | 0.792699 | __label__ast_Latn | 0.054806 | 0.680039 |
# Chapter 2 Exercises
In this notebook we will go through the exercises of chapter 2 of Introduction to Stochastic Processes with R by Robert Dobrow.
```python
import numpy as np
```
## 2.1
A Markov chain has transition Matrix
$$
p=\left(\begin{array}{cc}
0.1 & 0.3&0.6\\
0 & 0.4& 0.6 \\
0.3 & 0.2 &0.5
\end{array}\r... | ce9e388822f72a9455c50b39e459bb0cf61cc436 | 56,321 | ipynb | Jupyter Notebook | Chapter02_py.ipynb | larispardo/StochasticProcessR | a2f8b6c41f2fe451629209317fc32f2c28e0e4ee | [
"MIT"
] | null | null | null | Chapter02_py.ipynb | larispardo/StochasticProcessR | a2f8b6c41f2fe451629209317fc32f2c28e0e4ee | [
"MIT"
] | null | null | null | Chapter02_py.ipynb | larispardo/StochasticProcessR | a2f8b6c41f2fe451629209317fc32f2c28e0e4ee | [
"MIT"
] | null | null | null | 31.04796 | 584 | 0.47144 | true | 13,771 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91848 | 0.908618 | 0.834548 | __label__eng_Latn | 0.916103 | 0.777267 |
# Open Science Prize: Supplementary Material
This notebook is meant to provide a little more information about the Open Science Prize, but mostly, this notebook is a launching point from which the motivated learner can find open access sources with even more detailed information.
## 1 The Heisenberg Spin Model
In the... | 9bb8c60a6458ba79c9db78e4fe8583ed7dca62ad | 161,610 | ipynb | Jupyter Notebook | ibmq-qsim-sup-mat.ipynb | qfizik/open-science-prize-2021 | 9cc4dcd3fe8aaf9e352abf283ddaaaf16afbdef1 | [
"Apache-2.0"
] | null | null | null | ibmq-qsim-sup-mat.ipynb | qfizik/open-science-prize-2021 | 9cc4dcd3fe8aaf9e352abf283ddaaaf16afbdef1 | [
"Apache-2.0"
] | null | null | null | ibmq-qsim-sup-mat.ipynb | qfizik/open-science-prize-2021 | 9cc4dcd3fe8aaf9e352abf283ddaaaf16afbdef1 | [
"Apache-2.0"
] | null | null | null | 173.961249 | 58,300 | 0.871877 | true | 8,865 | Qwen/Qwen-72B | 1. YES
2. YES | 0.851953 | 0.891811 | 0.759781 | __label__eng_Latn | 0.9016 | 0.603558 |
# Linear Programming: Introduction
## Definition
Formally, a linear program is an optimzation problem of the form:
\begin{equation}
\min \vec{c}^\mathsf{T}\vec x\\
\textrm{subject to} \begin{cases}
\mathbf{A}\vec x=\vec b\\
\vec x\ge\vec 0
\end{cases}
\end{equation}
where $\vec c\in\mathbb R^n$, $\vec b\in\mathbb R... | 162d7466e9ef9f19b55cae7e80719ed031028d97 | 10,683 | ipynb | Jupyter Notebook | Lectures_old/Lecture 5.ipynb | BenLauwens/ES313.jl | 5a7553e53c288834f768d26e0d5aa22f9062b6af | [
"MIT"
] | 3 | 2018-12-17T16:00:26.000Z | 2020-01-18T04:09:25.000Z | Lectures_old/Lecture 5.ipynb | BenLauwens/ES313 | 5a7553e53c288834f768d26e0d5aa22f9062b6af | [
"MIT"
] | null | null | null | Lectures_old/Lecture 5.ipynb | BenLauwens/ES313 | 5a7553e53c288834f768d26e0d5aa22f9062b6af | [
"MIT"
] | 2 | 2018-08-27T13:41:05.000Z | 2020-02-08T11:00:53.000Z | 32.769939 | 516 | 0.536366 | true | 2,543 | Qwen/Qwen-72B | 1. YES
2. YES | 0.936285 | 0.91118 | 0.853124 | __label__eng_Latn | 0.935652 | 0.820426 |
Marec 2015, J.Slavič in L.Knez
Vprašanje 1: Za sistem enačb:
$$
\mathbf{A}=
\begin{bmatrix}
1 & -4 & 1\\
1 & 6 & -1\\
2 & -1 & 2
\end{bmatrix}
\qquad
\mathbf{b}=
\begin{bmatrix}
7\\
13\\
5
\end{bmatrix}
$$
najdite rešitev s pomočjo ``SymPy``.
```python
fr... | 4d6c2471bdd8f0fb702ede28ce73c737129fbb5b | 18,204 | ipynb | Jupyter Notebook | pypinm-master/vprasanja za razmislek/Vaja 5 - polovica.ipynb | CrtomirJuren/python-delavnica | db96470d2cb1870390545cfbe511552a9ef08720 | [
"MIT"
] | null | null | null | pypinm-master/vprasanja za razmislek/Vaja 5 - polovica.ipynb | CrtomirJuren/python-delavnica | db96470d2cb1870390545cfbe511552a9ef08720 | [
"MIT"
] | null | null | null | pypinm-master/vprasanja za razmislek/Vaja 5 - polovica.ipynb | CrtomirJuren/python-delavnica | db96470d2cb1870390545cfbe511552a9ef08720 | [
"MIT"
] | null | null | null | 35.142857 | 2,498 | 0.642936 | true | 1,368 | Qwen/Qwen-72B | 1. YES
2. YES | 0.896251 | 0.817574 | 0.732752 | __label__slv_Latn | 0.902994 | 0.540761 |
Linear Algebra Examples
====
This just shows the machanics of linear algebra calculations with python. See Lecture 5 for motivation and understanding.
```python
import numpy as np
import scipy.linalg as la
import matplotlib.pyplot as plt
%matplotlib inline
```
```python
plt.style.use('ggplot')
```
Resources
----
... | 2a1d4d98a850642594e1987d5c8c14440dae0af8 | 19,673 | ipynb | Jupyter Notebook | notebooks/copies/lectures/T06_Linear_Algebra_Examples.ipynb | robkravec/sta-663-2021 | 4dc8018f7b172eaf81da9edc33174768ff157939 | [
"MIT"
] | null | null | null | notebooks/copies/lectures/T06_Linear_Algebra_Examples.ipynb | robkravec/sta-663-2021 | 4dc8018f7b172eaf81da9edc33174768ff157939 | [
"MIT"
] | null | null | null | notebooks/copies/lectures/T06_Linear_Algebra_Examples.ipynb | robkravec/sta-663-2021 | 4dc8018f7b172eaf81da9edc33174768ff157939 | [
"MIT"
] | null | null | null | 17.612355 | 442 | 0.451533 | true | 1,936 | Qwen/Qwen-72B | 1. YES
2. YES | 0.928409 | 0.930458 | 0.863846 | __label__eng_Latn | 0.616989 | 0.845336 |
# Import
```python
#region
import matplotlib.pyplot as plt
import math
from sympy import *
import matplotlib.pyplot as plt
from numpy import linspace
import numpy as np
#endregion
t = symbols('t')
f = symbols('f', cls=Function)
```
# Input
```python
#read input
#region
def ReadArray(f):
line = f.readline()
... | c7a0c1915dff07fa7f2d162fcbd338d44c7eddae | 52,856 | ipynb | Jupyter Notebook | Topic 5 - Solving Differential Equations/26.2.PowerSeries/PowerSeries/.ipynb_checkpoints/PowerSeries-checkpoint.ipynb | dthanhqhtt/MI3040-Numerical-Analysis | cf38ea7e6dc834b19e7cffef8b867a02ba472eae | [
"MIT"
] | 7 | 2020-11-23T17:00:20.000Z | 2022-01-31T06:28:40.000Z | Topic 5 - Solving Differential Equations/26.2.PowerSeries/PowerSeries/.ipynb_checkpoints/PowerSeries-checkpoint.ipynb | dthanhqhtt/MI3040-Numerical-Analysis | cf38ea7e6dc834b19e7cffef8b867a02ba472eae | [
"MIT"
] | 2 | 2020-09-22T17:08:05.000Z | 2020-12-20T12:00:59.000Z | Topic 5 - Solving Differential Equations/26.2.PowerSeries/PowerSeries/.ipynb_checkpoints/PowerSeries-checkpoint.ipynb | dthanhqhtt/MI3040-Numerical-Analysis | cf38ea7e6dc834b19e7cffef8b867a02ba472eae | [
"MIT"
] | 5 | 2020-12-03T05:11:49.000Z | 2021-09-28T03:33:35.000Z | 161.639144 | 16,344 | 0.892803 | true | 958 | Qwen/Qwen-72B | 1. YES
2. YES | 0.855851 | 0.771844 | 0.660583 | __label__eng_Latn | 0.309343 | 0.373087 |
```python
import numpy
%matplotlib notebook
import matplotlib.pyplot
import sympy
```
# Elliptical Turns
## Overview
At an intersection, a car has to make a smooth turn between two road endpoints. To approximate this effect, I will attempt to fit an ellipse between the two roads. The ellipse must intersect at the en... | a9a9973b711d6b982d99a83541e930e79439cc72 | 309,601 | ipynb | Jupyter Notebook | Environment/EllipticalTurns.ipynb | RobertDurfee/RLTrafficIntersections | 88272512e068fe41d7389faf476c2b8e63e36002 | [
"MIT"
] | 1 | 2021-02-03T03:24:57.000Z | 2021-02-03T03:24:57.000Z | Environment/EllipticalTurns.ipynb | RobertDurfee/RLTrafficIntersections | 88272512e068fe41d7389faf476c2b8e63e36002 | [
"MIT"
] | 1 | 2021-02-03T09:12:19.000Z | 2021-02-03T15:00:03.000Z | Environment/EllipticalTurns.ipynb | RobertDurfee/RLTrafficIntersections | 88272512e068fe41d7389faf476c2b8e63e36002 | [
"MIT"
] | null | null | null | 82.253188 | 47,091 | 0.706884 | true | 4,851 | Qwen/Qwen-72B | 1. YES
2. YES | 0.896251 | 0.853913 | 0.76532 | __label__eng_Latn | 0.859441 | 0.616428 |
<a href="https://colab.research.google.com/github/ebatty/MathToolsforNeuroscience/blob/master/Week2/Week2Tutorial1.ipynb" target="_parent"></a>
# Week 2: Linear Algebra II
# Tutorial 1
# [insert your name]
**Important reminders**: Before starting, click "File -> Save a copy in Drive". Produce a pdf for submission b... | fed8cf8c0cbf9658732adf84e00a08fd62a1f61b | 60,485 | ipynb | Jupyter Notebook | Week2/Week2Tutorial1.ipynb | hugoladret/MathToolsforNeuroscience | fad301909da9274bb6c40cac96e2c62ed85b3956 | [
"MIT"
] | null | null | null | Week2/Week2Tutorial1.ipynb | hugoladret/MathToolsforNeuroscience | fad301909da9274bb6c40cac96e2c62ed85b3956 | [
"MIT"
] | null | null | null | Week2/Week2Tutorial1.ipynb | hugoladret/MathToolsforNeuroscience | fad301909da9274bb6c40cac96e2c62ed85b3956 | [
"MIT"
] | null | null | null | 49.496727 | 19,496 | 0.683905 | true | 7,419 | Qwen/Qwen-72B | 1. YES
2. YES | 0.66888 | 0.766294 | 0.512559 | __label__eng_Latn | 0.989833 | 0.029175 |
# Taylor Series for Approximations
Taylor series are commonly used in physics to approximate functions making them easier to handle specially when solving equations. In this notebook we give a visual example on how it works and the biases that it introduces.
## Theoretical Formula
Consider a function $f$ that is $n$... | d1e7f77a0398c1454062aa878fc5723994dd3e99 | 7,425 | ipynb | Jupyter Notebook | taylor_series.ipynb | fadinammour/taylor_series | 4deb11d51dcf23432c035486d997cfebd3ea7418 | [
"MIT"
] | null | null | null | taylor_series.ipynb | fadinammour/taylor_series | 4deb11d51dcf23432c035486d997cfebd3ea7418 | [
"MIT"
] | null | null | null | taylor_series.ipynb | fadinammour/taylor_series | 4deb11d51dcf23432c035486d997cfebd3ea7418 | [
"MIT"
] | null | null | null | 33.75 | 232 | 0.566734 | true | 1,353 | Qwen/Qwen-72B | 1. YES
2. YES | 0.914901 | 0.904651 | 0.827666 | __label__eng_Latn | 0.838701 | 0.761278 |
# Algebraic differentiators: A detailed introduction
This notebook includes a detailed introduction into the theoretical background of algebraic differentiators and shows how to use the proposed implementation.
## Content of this notebook
\textbf{Theoretical background}: Time-domain and frequency-domain analysis
\tex... | 0b53b35953463cd491cf5cbb981b0cc3d6075b81 | 703,530 | ipynb | Jupyter Notebook | examples/DetailedExamples.ipynb | AmineCybernetics/Algebraic-differentiators | e6dfd9db66d755e54779dd634f77c5ccd8888c5b | [
"BSD-3-Clause"
] | 6 | 2021-08-06T07:05:09.000Z | 2021-09-17T12:28:11.000Z | examples/DetailedExamples.ipynb | AmineCybernetics/Algebraic-differentiators | e6dfd9db66d755e54779dd634f77c5ccd8888c5b | [
"BSD-3-Clause"
] | null | null | null | examples/DetailedExamples.ipynb | AmineCybernetics/Algebraic-differentiators | e6dfd9db66d755e54779dd634f77c5ccd8888c5b | [
"BSD-3-Clause"
] | 1 | 2021-08-10T10:25:12.000Z | 2021-08-10T10:25:12.000Z | 131.648578 | 148,481 | 0.793591 | true | 4,571 | Qwen/Qwen-72B | 1. YES
2. YES | 0.880797 | 0.800692 | 0.705247 | __label__eng_Latn | 0.698765 | 0.476857 |
# Customer Assignment Problem
## Objective and Prerequisites
Sharpen your mathematical optimization modeling skills with this example, in which you will learn how to select the location of facilities based on their proximity to customers. We’ll demonstrate how you can construct a mixed-integer programming (MIP) model... | 4f609534dfa3bdd1fc43c0ba38c5823220f4fa3b | 431,371 | ipynb | Jupyter Notebook | customer_assignment/customer_assignment_gcl.ipynb | anupamsharmaberkeley/Gurobi_Optimization | 701200b5bfd9bf46036675f5b157b3d8e3728ff9 | [
"Apache-2.0"
] | 153 | 2019-07-11T15:08:37.000Z | 2022-03-25T10:12:54.000Z | customer_assignment/customer_assignment_gcl.ipynb | anupamsharmaberkeley/Gurobi_Optimization | 701200b5bfd9bf46036675f5b157b3d8e3728ff9 | [
"Apache-2.0"
] | 7 | 2020-10-29T12:34:13.000Z | 2022-02-28T14:16:43.000Z | customer_assignment/customer_assignment_gcl.ipynb | anupamsharmaberkeley/Gurobi_Optimization | 701200b5bfd9bf46036675f5b157b3d8e3728ff9 | [
"Apache-2.0"
] | 91 | 2019-11-11T17:04:54.000Z | 2022-03-30T21:34:20.000Z | 1,054.696822 | 415,216 | 0.952892 | true | 2,967 | Qwen/Qwen-72B | 1. YES
2. YES
| 0.966914 | 0.812867 | 0.785973 | __label__eng_Latn | 0.975027 | 0.664411 |
## 5. Linear ensemble filtering Lorenz-96 problem with localization
In this notebook, we apply the stochastic ensemble Kalman filter to the Lorenz-96 problem.
To regularize the inference problem, we use a localization radius `L` to cut-off long-range correlations and improve the conditioning of the covariance matrix.... | 559ea242492cb3d4ac71ce5ba66e80ed2e947c02 | 199,004 | ipynb | Jupyter Notebook | notebooks/Linear ensemble filtering Lorenz 96 with localization.ipynb | mleprovost/TransportBasedInference.jl | bdcedf72e9ea23c24678fe6af7a00202c5f9d5d7 | [
"MIT"
] | 1 | 2022-03-23T03:16:56.000Z | 2022-03-23T03:16:56.000Z | notebooks/Linear ensemble filtering Lorenz 96 with localization.ipynb | mleprovost/TransportBasedInference.jl | bdcedf72e9ea23c24678fe6af7a00202c5f9d5d7 | [
"MIT"
] | null | null | null | notebooks/Linear ensemble filtering Lorenz 96 with localization.ipynb | mleprovost/TransportBasedInference.jl | bdcedf72e9ea23c24678fe6af7a00202c5f9d5d7 | [
"MIT"
] | null | null | null | 340.760274 | 118,541 | 0.935549 | true | 3,075 | Qwen/Qwen-72B | 1. YES
2. YES | 0.787931 | 0.754915 | 0.594821 | __label__eng_Latn | 0.881394 | 0.220299 |
## Rosenbrock
The definition ca be found in <cite data-cite="rosenbrock"></cite>. It is a non-convex function, introduced by Howard H. Rosenbrock in 1960 and also known as Rosenbrock's valley or Rosenbrock's banana function.
**Definition**
\begin{align}
\begin{split}
f(x) &=& \sum_{i=1}^{n-1} \bigg[100 (x_{i+1}-x_i... | 5eb4814f88f56a5564d62b620579e2f4efada290 | 332,031 | ipynb | Jupyter Notebook | source/problems/single/rosenbrock.ipynb | SunTzunami/pymoo-doc | f82d8908fe60792d49a7684c4bfba4a6c1339daf | [
"Apache-2.0"
] | 2 | 2021-09-11T06:43:49.000Z | 2021-11-10T13:36:09.000Z | source/problems/single/rosenbrock.ipynb | SunTzunami/pymoo-doc | f82d8908fe60792d49a7684c4bfba4a6c1339daf | [
"Apache-2.0"
] | 3 | 2021-09-21T14:04:47.000Z | 2022-03-07T13:46:09.000Z | source/problems/single/rosenbrock.ipynb | SunTzunami/pymoo-doc | f82d8908fe60792d49a7684c4bfba4a6c1339daf | [
"Apache-2.0"
] | 3 | 2021-10-09T02:47:26.000Z | 2022-02-10T07:02:37.000Z | 2,496.473684 | 329,172 | 0.962961 | true | 262 | Qwen/Qwen-72B | 1. YES
2. YES | 0.939025 | 0.872347 | 0.819156 | __label__eng_Latn | 0.72157 | 0.741506 |
Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Lorena A. Barba, Gilbert F. Forsyth 2015. Thanks to NSF for support via CAREER award #1149784.
[@LorenaABarba](https://twitter.com/LorenaABarba)
12 steps to Navier-Stokes
=====
***
W... | 201e1f23ff9d62775d1bda6fbd9192903b83b1bc | 68,332 | ipynb | Jupyter Notebook | lessons/05_Step_4.ipynb | iafleischer/CFDPython | 02e1959e483b4503e85ccfe1f4fdb39e9b1601f8 | [
"CC-BY-3.0"
] | null | null | null | lessons/05_Step_4.ipynb | iafleischer/CFDPython | 02e1959e483b4503e85ccfe1f4fdb39e9b1601f8 | [
"CC-BY-3.0"
] | null | null | null | lessons/05_Step_4.ipynb | iafleischer/CFDPython | 02e1959e483b4503e85ccfe1f4fdb39e9b1601f8 | [
"CC-BY-3.0"
] | 1 | 2021-05-01T13:45:12.000Z | 2021-05-01T13:45:12.000Z | 109.50641 | 24,900 | 0.812152 | true | 3,989 | Qwen/Qwen-72B | 1. YES
2. YES | 0.83762 | 0.819893 | 0.686759 | __label__eng_Latn | 0.900684 | 0.433903 |
# Multiple Features
```python
import pandas as pd
import numpy as np
```
```python
size =[2104,1416,1534,852]
nbr_bedrooms = [5,3,3,2]
nbr_floors = [1,2,2,1]
age = [45,40,30,36]
price = [460,232,315,178]
```
```python
d = {'size':size,'nbr_bedrooms':nbr_bedrooms,'nbr_floors':nbr_floors,'age':age,'price':price}
``... | 69dd45969f4f1b15e1b1693ab130ee5779b4db5b | 9,416 | ipynb | Jupyter Notebook | Andrew Ng - Coursera/Week 2/Multiple Features Regression.ipynb | chikoungoun/Machine-Learning | 18625cacec264b612c4ec69cdc82cf7db46e6785 | [
"MIT"
] | null | null | null | Andrew Ng - Coursera/Week 2/Multiple Features Regression.ipynb | chikoungoun/Machine-Learning | 18625cacec264b612c4ec69cdc82cf7db46e6785 | [
"MIT"
] | null | null | null | Andrew Ng - Coursera/Week 2/Multiple Features Regression.ipynb | chikoungoun/Machine-Learning | 18625cacec264b612c4ec69cdc82cf7db46e6785 | [
"MIT"
] | null | null | null | 21.948718 | 224 | 0.421623 | true | 1,388 | Qwen/Qwen-72B | 1. YES
2. YES | 0.882428 | 0.826712 | 0.729514 | __label__eng_Latn | 0.394924 | 0.533236 |
```python
# picture from Jeremy Blum's book "Exploring Arduino" 2nd edition Page 50
from IPython.display import Image
from IPython.core.display import HTML
Image(url= "https://i.imgur.com/K6pJCwd.png")
```
```python
# through trial and error we find that 5*sin(0.5*x)**2 is very close to Blum's graph
# the pro... | 9178035be86d2f09703d477bd99f48b335045776 | 69,951 | ipynb | Jupyter Notebook | Personal_Projects/Reverse_Engineering/Reverse_Engineering_Analog-to-Digital_Graphs_v0.3.ipynb | NSC9/Sample_of_Work | 8f8160fbf0aa4fd514d4a5046668a194997aade6 | [
"MIT"
] | null | null | null | Personal_Projects/Reverse_Engineering/Reverse_Engineering_Analog-to-Digital_Graphs_v0.3.ipynb | NSC9/Sample_of_Work | 8f8160fbf0aa4fd514d4a5046668a194997aade6 | [
"MIT"
] | null | null | null | Personal_Projects/Reverse_Engineering/Reverse_Engineering_Analog-to-Digital_Graphs_v0.3.ipynb | NSC9/Sample_of_Work | 8f8160fbf0aa4fd514d4a5046668a194997aade6 | [
"MIT"
] | null | null | null | 279.804 | 22,728 | 0.919629 | true | 1,180 | Qwen/Qwen-72B | 1. YES
2. YES | 0.868827 | 0.899121 | 0.781181 | __label__eng_Latn | 0.969848 | 0.653277 |
# Adaptive Finite Element Method for a Nonlinear Poisson Equation
In this tutorial we solve the nonlinear Poisson equation from tutorial 01 using
adaptive grid refinement. The finite element solution on a given mesh is used to
compute local error indicators that can be used to iteratively reduce the
discretization err... | 592090db81a4a832322c3315ae694d721d23c15a | 69,048 | ipynb | Jupyter Notebook | notebooks/tutorial05/pdelab-tutorial05.ipynb | dokempf/dune-jupyter-course | 1da9c0c2a056952a738e8c7f5aa5aa00fb59442c | [
"BSD-3-Clause"
] | 1 | 2022-01-21T03:16:12.000Z | 2022-01-21T03:16:12.000Z | notebooks/tutorial05/pdelab-tutorial05.ipynb | dokempf/dune-jupyter-course | 1da9c0c2a056952a738e8c7f5aa5aa00fb59442c | [
"BSD-3-Clause"
] | 21 | 2021-04-22T13:52:59.000Z | 2021-10-04T13:31:59.000Z | notebooks/tutorial05/pdelab-tutorial05.ipynb | dokempf/dune-jupyter-course | 1da9c0c2a056952a738e8c7f5aa5aa00fb59442c | [
"BSD-3-Clause"
] | 1 | 2021-04-21T08:20:02.000Z | 2021-04-21T08:20:02.000Z | 41.898058 | 660 | 0.602842 | true | 13,811 | Qwen/Qwen-72B | 1. YES
2. YES | 0.763484 | 0.851953 | 0.650452 | __label__eng_Latn | 0.984536 | 0.349549 |
```python
def downloadDriveFile(file_id,file_name,file_extension):
'''
Allows charge of public files into colab's workspace
'''
!wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate '... | 6a12c82530f7ddbf93ae7aec1ad286db6f617438 | 347,256 | ipynb | Jupyter Notebook | numeric_analysis_exercises/steepest_descent_examples.ipynb | lufgarciaar/num_analysis_exercises | d145908494c5a7453830ec32dcac91df6fb028a4 | [
"BSD-2-Clause"
] | null | null | null | numeric_analysis_exercises/steepest_descent_examples.ipynb | lufgarciaar/num_analysis_exercises | d145908494c5a7453830ec32dcac91df6fb028a4 | [
"BSD-2-Clause"
] | null | null | null | numeric_analysis_exercises/steepest_descent_examples.ipynb | lufgarciaar/num_analysis_exercises | d145908494c5a7453830ec32dcac91df6fb028a4 | [
"BSD-2-Clause"
] | null | null | null | 347,256 | 347,256 | 0.869828 | true | 1,152 | Qwen/Qwen-72B | 1. YES
2. YES | 0.828939 | 0.815232 | 0.675778 | __label__eng_Latn | 0.232175 | 0.408389 |
# Robust Registration of Catalogs
**Fan Tian, 12/01/2019** - ftian4@jhu.edu <br/>
## Description
In this notebook, we demonstrate using the robust registration algorithm [1] to cross-match small catalogs (particularly to those of the HST images) with rotation and shift. This is the latest version of the algorithm tha... | 6a25f94a79e2315021f3a0f8975313221eac03d4 | 28,727 | ipynb | Jupyter Notebook | demo_robust_registration.ipynb | rlwastro/robust-registration | 4289c9c725ad29561bcb7ec374bc98e5c02df5f4 | [
"BSD-3-Clause"
] | 2 | 2020-02-18T17:43:24.000Z | 2021-02-02T12:55:18.000Z | demo_robust_registration.ipynb | rlwastro/robust-registration | 4289c9c725ad29561bcb7ec374bc98e5c02df5f4 | [
"BSD-3-Clause"
] | null | null | null | demo_robust_registration.ipynb | rlwastro/robust-registration | 4289c9c725ad29561bcb7ec374bc98e5c02df5f4 | [
"BSD-3-Clause"
] | null | null | null | 35.641439 | 383 | 0.564103 | true | 6,440 | Qwen/Qwen-72B | 1. YES
2. YES | 0.828939 | 0.715424 | 0.593043 | __label__eng_Latn | 0.782949 | 0.216167 |
# Task 3: Expectation values
There are two main quantities that we wish to compute for the ground state in this project.
They are the full many-body ground state energy $E$ from the general Hartree-Fock method, and the particle density (also known as the one-body density and the electron density) $\rho(x)$.
## The ge... | 8ef50441ea13b5f399ebd58670731db0fc49c2a4 | 2,377 | ipynb | Jupyter Notebook | docs/task-3-expectation-values.ipynb | Schoyen/tdhf-project-fys4411 | b0231c0d759382c14257cc4572698aa80c1c94d0 | [
"MIT"
] | 1 | 2021-06-03T00:34:57.000Z | 2021-06-03T00:34:57.000Z | docs/task-3-expectation-values.ipynb | Schoyen/tdhf-project-fys4411 | b0231c0d759382c14257cc4572698aa80c1c94d0 | [
"MIT"
] | null | null | null | docs/task-3-expectation-values.ipynb | Schoyen/tdhf-project-fys4411 | b0231c0d759382c14257cc4572698aa80c1c94d0 | [
"MIT"
] | null | null | null | 33.478873 | 194 | 0.574253 | true | 418 | Qwen/Qwen-72B | 1. YES
2. YES | 0.917303 | 0.757794 | 0.695127 | __label__eng_Latn | 0.994519 | 0.453344 |
# Lecture 9: Expectation, Indicator Random Variables, Linearity
## More on Cumulative Distribution Functions
A CDF: $F(x) = P(X \le x)$, as a function of real $x$ has to be
* non-negative
* add up to 1
In the following discrete case, it is easy to see how the probability mass function (PMF) relates to the CDF:
T... | 3d9580db1f9988d418cc878a6d7d37994f2f2eb9 | 11,121 | ipynb | Jupyter Notebook | Lecture_09.ipynb | dirtScrapper/Stats-110-master | a123692d039193a048ff92f5a7389e97e479eb7e | [
"BSD-3-Clause"
] | null | null | null | Lecture_09.ipynb | dirtScrapper/Stats-110-master | a123692d039193a048ff92f5a7389e97e479eb7e | [
"BSD-3-Clause"
] | null | null | null | Lecture_09.ipynb | dirtScrapper/Stats-110-master | a123692d039193a048ff92f5a7389e97e479eb7e | [
"BSD-3-Clause"
] | null | null | null | 35.082019 | 368 | 0.491682 | true | 2,660 | Qwen/Qwen-72B | 1. YES
2. YES | 0.658418 | 0.894789 | 0.589145 | __label__eng_Latn | 0.939055 | 0.207111 |
# The Harmonic Oscillator Strikes Back
*Note:* Much of this is adapted/copied from https://flothesof.github.io/harmonic-oscillator-three-methods-solution.html
This week we continue our adventures with the harmonic oscillator.
The harmonic oscillator is a system that, when displaced from its equilibrium position, ... | 0e85ae5f39c866fd984aaaf849e1b43f11bb7cfb | 302,325 | ipynb | Jupyter Notebook | harmonic_student.ipynb | sju-chem264-2019/new-10-14-10-Yekaterina25 | d4c92231de3198e78affaa2e6bb1165d2cea20f1 | [
"MIT"
] | null | null | null | harmonic_student.ipynb | sju-chem264-2019/new-10-14-10-Yekaterina25 | d4c92231de3198e78affaa2e6bb1165d2cea20f1 | [
"MIT"
] | null | null | null | harmonic_student.ipynb | sju-chem264-2019/new-10-14-10-Yekaterina25 | d4c92231de3198e78affaa2e6bb1165d2cea20f1 | [
"MIT"
] | null | null | null | 225.784167 | 25,312 | 0.906591 | true | 3,357 | Qwen/Qwen-72B | 1. YES
2. YES | 0.76908 | 0.743168 | 0.571556 | __label__eng_Latn | 0.697896 | 0.166246 |
```python
import modern_robotics as mr
import numpy as np
import sympy as sp
from sympy.physics.mechanics import dynamicsymbols, mechanics_printing
mechanics_printing()
from Utilities.symbolicFunctions import *
from Utilities.kukaKinematics import Slist, Mlist
```
# Task 3
## 3.2
### Develop and implement a solution ... | 9ee28400b01c5d641c06abcde49fe4b286109314 | 10,009 | ipynb | Jupyter Notebook | Task_3.ipynb | BirkHveding/RobotTek | 37f4ab0de6de9131239ff5d97e4b68a7091f291b | [
"Apache-2.0"
] | null | null | null | Task_3.ipynb | BirkHveding/RobotTek | 37f4ab0de6de9131239ff5d97e4b68a7091f291b | [
"Apache-2.0"
] | null | null | null | Task_3.ipynb | BirkHveding/RobotTek | 37f4ab0de6de9131239ff5d97e4b68a7091f291b | [
"Apache-2.0"
] | null | null | null | 38.644788 | 1,209 | 0.569188 | true | 1,658 | Qwen/Qwen-72B | 1. YES
2. YES | 0.800692 | 0.875787 | 0.701236 | __label__eng_Latn | 0.69428 | 0.467537 |
```python
from matplotlib import pyplot as plt
import numpy as np
from nodepy import runge_kutta_method as rk
from nodepy import stability_function
from sympy import symbols, expand
from scipy.special import laguerre
from ipywidgets import interact, FloatSlider
```
```python
def restricted_pade(k,gamma=0):
coeffs... | 6740dea5f630c98d53cf625186724da5abd7225f | 9,307 | ipynb | Jupyter Notebook | examples/Stability functions and order stars.ipynb | logichen/nodepy | 3d994caff078f142be1157162132a8788c6e8bb4 | [
"BSD-2-Clause"
] | null | null | null | examples/Stability functions and order stars.ipynb | logichen/nodepy | 3d994caff078f142be1157162132a8788c6e8bb4 | [
"BSD-2-Clause"
] | null | null | null | examples/Stability functions and order stars.ipynb | logichen/nodepy | 3d994caff078f142be1157162132a8788c6e8bb4 | [
"BSD-2-Clause"
] | null | null | null | 58.16875 | 5,606 | 0.790265 | true | 420 | Qwen/Qwen-72B | 1. YES
2. YES | 0.899121 | 0.828939 | 0.745317 | __label__eng_Latn | 0.520182 | 0.569952 |
# Announcements
- No Problem Set this week, Problem Set 4 will be posted on 9/28.
- Solutions to Problem Set 3 posted on D2L.
<style>
@import url(https://www.numfys.net/static/css/nbstyle.css);
</style>
<a href="https://www.numfys.net"></a>
# Ordinary Differential Equations - higher order methods in practice
<sectio... | 60615d454c2923296220c5f9baa336ef950a4e60 | 806,824 | ipynb | Jupyter Notebook | Lectures/Lecture 13/Lecture13_ODE_part4.ipynb | astroarshn2000/PHYS305S20 | 18f4ebf0a51ba62fba34672cf76bd119d1db6f1e | [
"MIT"
] | 3 | 2020-09-10T06:45:46.000Z | 2020-10-20T13:50:11.000Z | Lectures/Lecture 13/Lecture13_ODE_part4.ipynb | astroarshn2000/PHYS305S20 | 18f4ebf0a51ba62fba34672cf76bd119d1db6f1e | [
"MIT"
] | null | null | null | Lectures/Lecture 13/Lecture13_ODE_part4.ipynb | astroarshn2000/PHYS305S20 | 18f4ebf0a51ba62fba34672cf76bd119d1db6f1e | [
"MIT"
] | null | null | null | 833.495868 | 475,280 | 0.945323 | true | 8,344 | Qwen/Qwen-72B | 1. YES
2. YES | 0.885631 | 0.888759 | 0.787113 | __label__eng_Latn | 0.992193 | 0.667059 |
# Tutorial
## Preliminaries
Before following this tutorial we need to set up the tools and load the data. We need to import several packages, so before running this notebook you should create an environment (conda or virtualenv) with matplotlib, numpy, and scikit-image, and jupyter.
You can use the Anaconda Navigator... | f9fbdcda281125b29e8faafd44fc3fd8c589a584 | 11,621 | ipynb | Jupyter Notebook | Tutorial.ipynb | SimonCastillo/bio231c | 0cff86d41f88a57abfa69c18c5f43f0ceaf4ccd7 | [
"MIT"
] | null | null | null | Tutorial.ipynb | SimonCastillo/bio231c | 0cff86d41f88a57abfa69c18c5f43f0ceaf4ccd7 | [
"MIT"
] | null | null | null | Tutorial.ipynb | SimonCastillo/bio231c | 0cff86d41f88a57abfa69c18c5f43f0ceaf4ccd7 | [
"MIT"
] | null | null | null | 22.608949 | 277 | 0.551071 | true | 1,306 | Qwen/Qwen-72B | 1. YES
2. YES | 0.679179 | 0.839734 | 0.570329 | __label__eng_Latn | 0.994219 | 0.163396 |
# The 24 Game
The Python program below finds solutions to the 24 game: use four numbers and any of the four basic arithmetic operations (multiplication, division, addition and subtraction) to produce the number 24 (or any number you choose).
Execute the program, choose four numbers (separated by commas) and the targe... | d9f898f5abf528b45cb643eb4a6df90023210fdf | 4,598 | ipynb | Jupyter Notebook | 24i.ipynb | tiggerntatie/24 | 49ad4d06230ee001518ff2bb41b24f2772b744c3 | [
"MIT"
] | null | null | null | 24i.ipynb | tiggerntatie/24 | 49ad4d06230ee001518ff2bb41b24f2772b744c3 | [
"MIT"
] | null | null | null | 24i.ipynb | tiggerntatie/24 | 49ad4d06230ee001518ff2bb41b24f2772b744c3 | [
"MIT"
] | null | null | null | 33.562044 | 236 | 0.50087 | true | 883 | Qwen/Qwen-72B | 1. YES
2. YES | 0.948155 | 0.865224 | 0.820366 | __label__eng_Latn | 0.959808 | 0.744318 |
<div style='background-image: url("../../share/images/header.svg") ; padding: 0px ; background-size: cover ; border-radius: 5px ; height: 250px'>
<div style="float: right ; margin: 50px ; padding: 20px ; background: rgba(255 , 255 , 255 , 0.7) ; width: 50% ; height: 150px">
<div style="position: relative ; ... | 15f606174ad92e8fa3ad17328fff38cac8708ae8 | 6,075 | ipynb | Jupyter Notebook | notebooks/Computational Seismology/The Finite-Difference Method/fd_first_derivative.ipynb | krischer/seismo_live_build | e4e8e59d9bf1b020e13ac91c0707eb907b05b34f | [
"CC-BY-3.0"
] | 3 | 2020-07-11T10:01:39.000Z | 2020-12-16T14:26:03.000Z | notebooks/Computational Seismology/The Finite-Difference Method/fd_first_derivative.ipynb | krischer/seismo_live_build | e4e8e59d9bf1b020e13ac91c0707eb907b05b34f | [
"CC-BY-3.0"
] | null | null | null | notebooks/Computational Seismology/The Finite-Difference Method/fd_first_derivative.ipynb | krischer/seismo_live_build | e4e8e59d9bf1b020e13ac91c0707eb907b05b34f | [
"CC-BY-3.0"
] | 3 | 2020-11-11T05:05:41.000Z | 2022-03-12T09:36:24.000Z | 6,075 | 6,075 | 0.594897 | true | 1,216 | Qwen/Qwen-72B | 1. YES
2. YES | 0.880797 | 0.812867 | 0.715971 | __label__eng_Latn | 0.734934 | 0.501772 |
```python
# from matplotlib import pyplot as plt
from sympy import Symbol, Eq, Function, solve, Rational, lambdify, latex
from IPython.display import display
from typing import List
#initialize some symbols here:
rho1 = Symbol("rho_1")
t = Symbol("t")
R = Function("R")(t)
R_d1 = R.diff()
R_d2 = R.diff().diff()
P0 = S... | 37696aeee49c594dd3b489b298013c5b0830c850 | 9,035 | ipynb | Jupyter Notebook | Projects/Project_1/Q2/Question2New.ipynb | UWaterloo-Mech-3A/Calculus | 2e3f3bb606fda4bfe1c7f793a11b07b336f356ed | [
"MIT"
] | null | null | null | Projects/Project_1/Q2/Question2New.ipynb | UWaterloo-Mech-3A/Calculus | 2e3f3bb606fda4bfe1c7f793a11b07b336f356ed | [
"MIT"
] | null | null | null | Projects/Project_1/Q2/Question2New.ipynb | UWaterloo-Mech-3A/Calculus | 2e3f3bb606fda4bfe1c7f793a11b07b336f356ed | [
"MIT"
] | 1 | 2021-07-16T06:01:32.000Z | 2021-07-16T06:01:32.000Z | 38.943966 | 2,041 | 0.555285 | true | 966 | Qwen/Qwen-72B | 1. YES
2. YES | 0.896251 | 0.615088 | 0.551273 | __label__eng_Latn | 0.623377 | 0.119122 |
```python
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
```
The first exercise is about using Newton's method to find the cube roots of unity - find $z$ such that $z^3 = 1$. From the fundamental theorem of algebra, we know there must be exactly 3 complex roots since this is a degree 3 polynomia... | 03e8e772fb398d451609c9302452220e6a4e3397 | 26,448 | ipynb | Jupyter Notebook | homework/08_Optimization.ipynb | cliburn/sta-663-2017 | 89e059dfff25a4aa427cdec5ded755ab456fbc16 | [
"MIT"
] | 52 | 2017-01-11T03:16:00.000Z | 2021-01-15T05:28:48.000Z | homework/08_Optimization.ipynb | slimdt/Duke_Stat633_2017 | 89e059dfff25a4aa427cdec5ded755ab456fbc16 | [
"MIT"
] | 1 | 2017-04-16T17:10:49.000Z | 2017-04-16T19:13:03.000Z | homework/08_Optimization.ipynb | slimdt/Duke_Stat633_2017 | 89e059dfff25a4aa427cdec5ded755ab456fbc16 | [
"MIT"
] | 47 | 2017-01-13T04:50:54.000Z | 2021-06-23T11:48:33.000Z | 88.454849 | 14,202 | 0.805014 | true | 1,922 | Qwen/Qwen-72B | 1. YES
2. YES | 0.817574 | 0.90053 | 0.73625 | __label__eng_Latn | 0.997384 | 0.548888 |
```
from sympy import *
```
```
n_u, n_b, m_p, m_r = symbols("n_u n_b m_p m_r")
F = MatrixSymbol("F", n_u, n_u)
M = MatrixSymbol("M", n_b, n_b)
C = MatrixSymbol("C", n_b, n_u)
B = MatrixSymbol("B", m_p, n_u)
D = MatrixSymbol("D", m_r, n_b)
```
```
B.T.shape
```
(n_u, m_p)
```
A = BlockMatrix([[F,B.T,C.T... | e466a3c648b87cc017f163676b8bc7a84d7d3ba4 | 4,437 | ipynb | Jupyter Notebook | MHD/BLKdecomps/.ipynb_checkpoints/Untitled0-checkpoint.ipynb | wathen/PhD | 35524f40028541a4d611d8c78574e4cf9ddc3278 | [
"MIT"
] | 3 | 2020-10-25T13:30:20.000Z | 2021-08-10T21:27:30.000Z | MHD/BLKdecomps/.ipynb_checkpoints/Untitled0-checkpoint.ipynb | wathen/PhD | 35524f40028541a4d611d8c78574e4cf9ddc3278 | [
"MIT"
] | null | null | null | MHD/BLKdecomps/.ipynb_checkpoints/Untitled0-checkpoint.ipynb | wathen/PhD | 35524f40028541a4d611d8c78574e4cf9ddc3278 | [
"MIT"
] | 3 | 2019-10-28T16:12:13.000Z | 2020-01-13T13:59:44.000Z | 34.395349 | 1,319 | 0.508902 | true | 388 | Qwen/Qwen-72B | 1. YES
2. YES | 0.960361 | 0.779993 | 0.749075 | __label__kor_Hang | 0.105857 | 0.578684 |
# Asset replacement model
**Randall Romero Aguilar, PhD**
This demo is based on the original Matlab demo accompanying the <a href="https://mitpress.mit.edu/books/applied-computational-economics-and-finance">Computational Economics and Finance</a> 2001 textbook by Mario Miranda and Paul Fackler.
Original (Matlab) Co... | 2cd2f19f2a48c061344d1aa1b01da50707d9d53a | 128,368 | ipynb | Jupyter Notebook | _build/jupyter_execute/notebooks/ddp/02 Asset replacement model.ipynb | randall-romero/CompEcon-python | c7a75f57f8472c972fddcace8ff7b86fee049d29 | [
"MIT"
] | 23 | 2016-12-14T13:21:27.000Z | 2020-08-23T21:04:34.000Z | _build/jupyter_execute/notebooks/ddp/02 Asset replacement model.ipynb | randall-romero/CompEcon | c7a75f57f8472c972fddcace8ff7b86fee049d29 | [
"MIT"
] | 1 | 2017-09-10T04:48:54.000Z | 2018-03-31T01:36:46.000Z | _build/jupyter_execute/notebooks/ddp/02 Asset replacement model.ipynb | randall-romero/CompEcon-python | c7a75f57f8472c972fddcace8ff7b86fee049d29 | [
"MIT"
] | 13 | 2017-02-25T08:10:38.000Z | 2020-05-15T09:49:16.000Z | 236.841328 | 53,420 | 0.912992 | true | 2,061 | Qwen/Qwen-72B | 1. YES
2. YES | 0.83762 | 0.815232 | 0.682855 | __label__eng_Latn | 0.961577 | 0.424832 |
# Tutorial on motion energy model implementation
This notebook demonstrates the components underlying a spatiotemporal energy model of motion perception. The model was originally introduced in 1985 by EH Adelson and JR Bergen. The basic idea is to think of motion velocity as an orientation in space-time. The model wo... | a5fab4d340ec25d2e4c1d64ec0dfd77da74c77d6 | 212,506 | ipynb | Jupyter Notebook | motionenergy_tutorial.ipynb | aernesto/Waskom_JVision_2018 | 7b9b74976bdfa45582256fcd8fed0b072bc5c2f1 | [
"BSD-3-Clause"
] | null | null | null | motionenergy_tutorial.ipynb | aernesto/Waskom_JVision_2018 | 7b9b74976bdfa45582256fcd8fed0b072bc5c2f1 | [
"BSD-3-Clause"
] | 2 | 2019-02-25T19:15:43.000Z | 2019-02-25T19:31:03.000Z | motionenergy_tutorial.ipynb | aernesto/Waskom_JVision_2018 | 7b9b74976bdfa45582256fcd8fed0b072bc5c2f1 | [
"BSD-3-Clause"
] | null | null | null | 206.919182 | 102,000 | 0.906412 | true | 3,669 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.7773 | 0.689624 | __label__eng_Latn | 0.96774 | 0.440559 |
(sec:KAM)=
# An Informal Introduction to Ideas Related to KAM (Kolmogorov-Arnold-Moser) Theory
KAM theory has developed into a recognized branch of dynamical systems theory that is concerned with the study of the persistence of quasiperiodic trajectories in Hamiltonian system subjected to perturbation (generally, a ... | 0986b45b7d2b705d0cc94d070bc36e1bdb33ee82 | 25,069 | ipynb | Jupyter Notebook | book/_build/jupyter_execute/content/chapter2_2.ipynb | champsproject/lagrangian_descriptors | b3a88a2243bd5b0dce7cc945f9504bfadc9a4b19 | [
"CC-BY-4.0"
] | 12 | 2020-07-24T17:35:42.000Z | 2021-08-12T17:31:53.000Z | book/_build/jupyter_execute/content/chapter2_2.ipynb | champsproject/lagrangian_descriptors | b3a88a2243bd5b0dce7cc945f9504bfadc9a4b19 | [
"CC-BY-4.0"
] | 12 | 2020-05-26T17:28:38.000Z | 2020-07-27T10:40:54.000Z | book/_build/jupyter_execute/content/chapter2_2.ipynb | champsproject/lagrangian_descriptors | b3a88a2243bd5b0dce7cc945f9504bfadc9a4b19 | [
"CC-BY-4.0"
] | null | null | null | 68.122283 | 1,715 | 0.680841 | true | 5,437 | Qwen/Qwen-72B | 1. YES
2. YES | 0.793106 | 0.805632 | 0.638952 | __label__eng_Latn | 0.997596 | 0.32283 |
# 微分方程式の計算について N0.5 －1 直接積分、変数分離形
### 学籍番号[＿＿＿＿＿＿＿＿＿]クラス[＿＿＿＿＿] クラス番号[＿＿＿＿＿] 名前[＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿]
########### 1階常微分方程式 直接積分形
$$ \frac{dy}{dx}=f(x) $$
$$ y = \int f(x)dx = F(X)+C $$ f(x) の原始関数を F(x) とする ,C は積分定数
########## 1階微分方程式 変数分離形
$$ \frac{dy}{dx}=f(x)g(y) $$
左をｙのみ、右をｘのみに変形して分離する 両辺をそれぞれ不定積分する
$$ ... | 795e41a0da10f544e57befd05810160824997752 | 49,306 | ipynb | Jupyter Notebook | 09_20181126-bibunhoteisiki-7-1-Ex&ans-1.ipynb | kt-pro-git-1/Calculus_Differential_Equation-public | d5deaf117e6841c4f6ceb53bc80b020220fd4814 | [
"MIT"
] | 1 | 2019-07-10T11:33:18.000Z | 2019-07-10T11:33:18.000Z | 09_20181126-bibunhoteisiki-7-1-Ex&ans-1.ipynb | kt-pro-git-1/Calculus_Differential_Equation-public | d5deaf117e6841c4f6ceb53bc80b020220fd4814 | [
"MIT"
] | null | null | null | 09_20181126-bibunhoteisiki-7-1-Ex&ans-1.ipynb | kt-pro-git-1/Calculus_Differential_Equation-public | d5deaf117e6841c4f6ceb53bc80b020220fd4814 | [
"MIT"
] | null | null | null | 70.841954 | 2,208 | 0.803695 | true | 1,007 | Qwen/Qwen-72B | 1. YES
2. YES | 0.92944 | 0.859664 | 0.799006 | __label__yue_Hant | 0.277964 | 0.694692 |
# Tutorial: Small Angle Neutron Scattering
Small Angle Neutron Scattering (SANS) is a powerful reciprocal space technique that can be used to investigate magnetic structures on mesoscopic length scales. In SANS the atomic structure generally has a minimal impact hence the sample can be approximated by a continuous mag... | 2cab58ff8c92a89d30b52f15083dd43a5e166bd8 | 891,037 | ipynb | Jupyter Notebook | docs/SANS.ipynb | ubermag/exsim | 35e7a88716a9ed2c9a34f4c93c628560a597b57f | [
"BSD-3-Clause"
] | null | null | null | docs/SANS.ipynb | ubermag/exsim | 35e7a88716a9ed2c9a34f4c93c628560a597b57f | [
"BSD-3-Clause"
] | 6 | 2021-06-10T13:42:08.000Z | 2021-07-21T08:57:50.000Z | docs/SANS.ipynb | ubermag/exsim | 35e7a88716a9ed2c9a34f4c93c628560a597b57f | [
"BSD-3-Clause"
] | null | null | null | 1,528.365352 | 256,564 | 0.959388 | true | 2,751 | Qwen/Qwen-72B | 1. YES
2. YES | 0.795658 | 0.727975 | 0.57922 | __label__eng_Latn | 0.921949 | 0.184051 |
```python
from preamble import *
%matplotlib notebook
import matplotlib as mpl
mpl.rcParams['legend.numpoints'] = 1
```
## Evaluation Metrics and scoring
### Metrics for binary classification
```python
from sklearn.model_selection import train_test_split
data = pd.read_csv("data/bank-campaign.csv")
X = data.drop("... | 0797b84bc61b6348a706cda72ae5c6c9b70241e4 | 20,552 | ipynb | Jupyter Notebook | notebooks/03 Evaluation Metrics.ipynb | lampsonnguyen/ml-training-advance | 992c8304683879ade23410cfa4478622980ef420 | [
"MIT"
] | null | null | null | notebooks/03 Evaluation Metrics.ipynb | lampsonnguyen/ml-training-advance | 992c8304683879ade23410cfa4478622980ef420 | [
"MIT"
] | null | null | null | notebooks/03 Evaluation Metrics.ipynb | lampsonnguyen/ml-training-advance | 992c8304683879ade23410cfa4478622980ef420 | [
"MIT"
] | 2 | 2018-04-20T03:09:43.000Z | 2021-07-23T05:48:42.000Z | 28.192044 | 140 | 0.566174 | true | 3,213 | Qwen/Qwen-72B | 1. YES
2. YES | 0.803174 | 0.83762 | 0.672754 | __label__eng_Latn | 0.317725 | 0.401365 |
```python
from scipy.stats import uniform, expon, norm
from scipy import integrate
uniform.pdf(x=5, loc=1, scale=9)
```
0.1111111111111111
```python
1 - norm.cdf(x=0.3, loc=100, scale=15)
```
0.9999999999850098
```python
uniform.pdf(x=-1, loc=1, scale=15)
```
0.0
```python
```
... | ca7a8ed6fb868419e6e9363a3b37d49627a07190 | 4,288 | ipynb | Jupyter Notebook | Semester/SW03/SW03.ipynb | florianbaer/STAT | 7cb86406ed99b88055c92c1913b46e8995835cbb | [
"MIT"
] | null | null | null | Semester/SW03/SW03.ipynb | florianbaer/STAT | 7cb86406ed99b88055c92c1913b46e8995835cbb | [
"MIT"
] | null | null | null | Semester/SW03/SW03.ipynb | florianbaer/STAT | 7cb86406ed99b88055c92c1913b46e8995835cbb | [
"MIT"
] | null | null | null | 23.822222 | 1,026 | 0.522621 | true | 196 | Qwen/Qwen-72B | 1. YES
2. YES | 0.851953 | 0.626124 | 0.533428 | __label__yue_Hant | 0.412017 | 0.077662 |
###### Content under Creative Commons Attribution license CC-BY 4.0, code under MIT license (c)2014 L.A. Barba, C.D. Cooper, G.F. Forsyth.
# Riding the wave
## Numerical schemes for hyperbolic PDEs
Welcome back! This is the second notebook of *Riding the wave: Convection problems*, the third module of ["Practical N... | 5c5e7447e35d466e292f6722e50527eed60d081e | 231,915 | ipynb | Jupyter Notebook | lessons/03_wave/03_02_convectionSchemes.ipynb | mcarpe/numerical-mooc | 62b3c14c2c56d85d65c6075f2d7eb44266b49c17 | [
"CC-BY-3.0"
] | 748 | 2015-01-04T22:50:56.000Z | 2022-03-30T20:42:16.000Z | lessons/03_wave/03_02_convectionSchemes.ipynb | mcarpe/numerical-mooc | 62b3c14c2c56d85d65c6075f2d7eb44266b49c17 | [
"CC-BY-3.0"
] | 62 | 2015-02-02T01:06:07.000Z | 2020-11-09T12:27:41.000Z | lessons/03_wave/03_02_convectionSchemes.ipynb | mcarpe/numerical-mooc | 62b3c14c2c56d85d65c6075f2d7eb44266b49c17 | [
"CC-BY-3.0"
] | 1,270 | 2015-01-02T19:19:52.000Z | 2022-02-27T01:02:44.000Z | 69.644144 | 6,488 | 0.7791 | true | 8,569 | Qwen/Qwen-72B | 1. YES
2. YES | 0.851953 | 0.73412 | 0.625435 | __label__eng_Latn | 0.965553 | 0.291426 |
# "Spin Glass Models 3: Ising Model - Theory"
> "In this blog post we will introduce another model of spin glasses: the Ising model. We relax some of the simplifications of previous models to create something that more accurately captures the structure of real spin-glasses. We will look at this model mathematically to ... | c9e23738e6e0d812aa412c18d90b79cccdb06842 | 170,993 | ipynb | Jupyter Notebook | _notebooks/2020-03-17-Spin-Glass-Models-3.ipynb | lewiscoleblog/blog | 50183d63491abbf9e56676a784f53dfbb3952af1 | [
"Apache-2.0"
] | 2 | 2020-03-31T18:53:59.000Z | 2021-03-25T01:02:14.000Z | _notebooks/2020-03-17-Spin-Glass-Models-3.ipynb | lewiscoleblog/blog | 50183d63491abbf9e56676a784f53dfbb3952af1 | [
"Apache-2.0"
] | 3 | 2020-04-07T15:31:16.000Z | 2021-09-28T01:25:25.000Z | _notebooks/2020-03-17-Spin-Glass-Models-3.ipynb | lewiscoleblog/blog | 50183d63491abbf9e56676a784f53dfbb3952af1 | [
"Apache-2.0"
] | 1 | 2020-05-09T18:03:39.000Z | 2020-05-09T18:03:39.000Z | 284.514143 | 50,092 | 0.904236 | true | 7,156 | Qwen/Qwen-72B | 1. YES
2. YES | 0.749087 | 0.841826 | 0.630601 | __label__eng_Latn | 0.991698 | 0.303428 |
```python
%matplotlib inline
```
对抗样本生成
==============================
**Author:** `Nathan Inkawhich <https://github.com/inkawhich>`__
**翻译者**: `Antares博士 <http://www.studyai.com/antares>`__
如果你正在阅读这篇文章，希望你能体会到一些机器学习模型是多么的有效。研究不断推动ML模型变得更快、更准确和更高效。
然而，设计和训练模型的一个经常被忽视的方面是安全性和健壮性，特别是在面对希望欺骗模型的对手时。
本教程将提高您对ML模型的安全漏洞... | 64093f8befd3aecabc36e5aee3df2b86f1b7106f | 26,386 | ipynb | Jupyter Notebook | build/_downloads/2b3dcb9883348c9350c194d591814364/fgsm_tutorial.ipynb | ScorpioDoctor/antares02 | 631b817d2e98f351d1173b620d15c4a5efed11da | [
"BSD-3-Clause"
] | null | null | null | build/_downloads/2b3dcb9883348c9350c194d591814364/fgsm_tutorial.ipynb | ScorpioDoctor/antares02 | 631b817d2e98f351d1173b620d15c4a5efed11da | [
"BSD-3-Clause"
] | null | null | null | build/_downloads/2b3dcb9883348c9350c194d591814364/fgsm_tutorial.ipynb | ScorpioDoctor/antares02 | 631b817d2e98f351d1173b620d15c4a5efed11da | [
"BSD-3-Clause"
] | null | null | null | 136.010309 | 5,182 | 0.712575 | true | 4,934 | Qwen/Qwen-72B | 1. YES
2. YES | 0.766294 | 0.685949 | 0.525639 | __label__yue_Hant | 0.23068 | 0.059564 |
<a href="https://colab.research.google.com/github/john-s-butler-dit/Numerical-Analysis-Python/blob/master/Chapter%2006%20-%20Boundary%20Value%20Problems/604_Boundary%20Value%20Problem%20Example%202.ipynb" target="_parent"></a>
# Finite Difference Method
#### John S Butler john.s.butler@tudublin.ie [Course Notes](htt... | 2cc378795b62ea742c469674720d827519c366dd | 66,063 | ipynb | Jupyter Notebook | Chapter 06 - Boundary Value Problems/604_Boundary Value Problem Example 2.ipynb | jjcrofts77/Numerical-Analysis-Python | 97e4b9274397f969810581ff95f4026f361a56a2 | [
"MIT"
] | 69 | 2019-09-05T21:39:12.000Z | 2022-03-26T14:00:25.000Z | Chapter 06 - Boundary Value Problems/604_Boundary Value Problem Example 2.ipynb | jjcrofts77/Numerical-Analysis-Python | 97e4b9274397f969810581ff95f4026f361a56a2 | [
"MIT"
] | null | null | null | Chapter 06 - Boundary Value Problems/604_Boundary Value Problem Example 2.ipynb | jjcrofts77/Numerical-Analysis-Python | 97e4b9274397f969810581ff95f4026f361a56a2 | [
"MIT"
] | 13 | 2021-06-17T15:34:04.000Z | 2022-01-14T14:53:43.000Z | 161.129268 | 17,294 | 0.862858 | true | 2,742 | Qwen/Qwen-72B | 1. YES
2. YES | 0.894789 | 0.808067 | 0.72305 | __label__eng_Latn | 0.475997 | 0.518219 |
# 13 Root Finding
An important tool in the computational tool box is to find roots of equations for which no closed form solutions exist:
We want to find the roots $x_0$ of
$$
f(x_0) = 0
$$
## Problem: Projectile range
The equations of motion for the projectile with linear air resistance (see *12 ODE applications*... | 04a0da4f184a8eee1a77774014be3435a54f0407 | 146,300 | ipynb | Jupyter Notebook | 13_root_finding/13-Root-finding.ipynb | ASU-CompMethodsPhysics-PHY494/PHY494-resources-2020 | 20e08c20995eab567063b1845487e84c0e690e96 | [
"CC-BY-4.0"
] | null | null | null | 13_root_finding/13-Root-finding.ipynb | ASU-CompMethodsPhysics-PHY494/PHY494-resources-2020 | 20e08c20995eab567063b1845487e84c0e690e96 | [
"CC-BY-4.0"
] | null | null | null | 13_root_finding/13-Root-finding.ipynb | ASU-CompMethodsPhysics-PHY494/PHY494-resources-2020 | 20e08c20995eab567063b1845487e84c0e690e96 | [
"CC-BY-4.0"
] | null | null | null | 148.678862 | 38,592 | 0.880752 | true | 4,407 | Qwen/Qwen-72B | 1. YES
2. YES | 0.833325 | 0.810479 | 0.675392 | __label__eng_Latn | 0.89026 | 0.407493 |
# Generating Functions
Generating functions are functions that encode sequences of numbers as the coefficients of power series.
Consider a set $S$ with $n$ elements.
Pretend there is a picture function' $P(s)\ \forall s\in S$.
The picture function enables one, for example, to write the multiset $\{1,1,2\}$ as an... | 72b31a8f79ce5452c0c8f85cda868faae37e325e | 235,977 | ipynb | Jupyter Notebook | Combinatorics - Generating Functions.ipynb | jpbm/probabilism | a2f5c1595aed616236b2b889195604f365175899 | [
"MIT"
] | null | null | null | Combinatorics - Generating Functions.ipynb | jpbm/probabilism | a2f5c1595aed616236b2b889195604f365175899 | [
"MIT"
] | null | null | null | Combinatorics - Generating Functions.ipynb | jpbm/probabilism | a2f5c1595aed616236b2b889195604f365175899 | [
"MIT"
] | null | null | null | 655.491667 | 127,972 | 0.939816 | true | 3,084 | Qwen/Qwen-72B | 1. YES
2. YES | 0.942507 | 0.901921 | 0.850066 | __label__eng_Latn | 0.944737 | 0.813322 |
Trusted Notebook" width="250 px" align="left">
## _*Quantum Counterfeit Coin Problem*_
The latest version of this notebook is available on https://github.com/QISKit/qiskit-tutorial.
***
### Contributors
Rudy Raymond, Takashi Imamichi
## Introduction
The counterfeit coin problem is a classic puzzle first proposed... | 10f1d06505442809305d443d412c1ccbf490d498 | 28,716 | ipynb | Jupyter Notebook | reference/games/quantum_counterfeit_coin_problem.ipynb | marisvs/qiskit-tutorial | 9efa21357a6f01e0312ad2e9786b1a0f9f67fc74 | [
"Apache-2.0"
] | null | null | null | reference/games/quantum_counterfeit_coin_problem.ipynb | marisvs/qiskit-tutorial | 9efa21357a6f01e0312ad2e9786b1a0f9f67fc74 | [
"Apache-2.0"
] | null | null | null | reference/games/quantum_counterfeit_coin_problem.ipynb | marisvs/qiskit-tutorial | 9efa21357a6f01e0312ad2e9786b1a0f9f67fc74 | [
"Apache-2.0"
] | null | null | null | 75.172775 | 9,058 | 0.736036 | true | 4,239 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.757794 | 0.672319 | __label__eng_Latn | 0.991576 | 0.400353 |
```python
from games_setup import *
from SBMLLint.common import constants as cn
from SBMLLint.common.molecule import Molecule, MoleculeStoichiometry
from SBMLLint.common.reaction import Reaction
from SBMLLint.games.som import SOM
from SBMLLint.common.simple_sbml import SimpleSBML
import collections
import itertools
im... | 1407d54ad9d7563a0d06341fdc2aee7c79f44195 | 22,656 | ipynb | Jupyter Notebook | notebook/april26_2019_biocontrol.ipynb | BeckResearchLab/SBMLLint | a5f2b1ad691c192e456e2c0b5d208d921a933a4f | [
"MIT"
] | null | null | null | notebook/april26_2019_biocontrol.ipynb | BeckResearchLab/SBMLLint | a5f2b1ad691c192e456e2c0b5d208d921a933a4f | [
"MIT"
] | null | null | null | notebook/april26_2019_biocontrol.ipynb | BeckResearchLab/SBMLLint | a5f2b1ad691c192e456e2c0b5d208d921a933a4f | [
"MIT"
] | null | null | null | 25.399103 | 102 | 0.353681 | true | 4,150 | Qwen/Qwen-72B | 1. YES
2. YES | 0.83762 | 0.661923 | 0.55444 | __label__yue_Hant | 0.37259 | 0.126479 |
# What is inversion?
For those with little or no background or experience, the concept of inversion can be intimidating. Many available resources assume a certain amount of background already, and for the uninitiated, this can make the topic difficult to grasp. Likewise, if material is too general and simplistic, it i... | 54a9b69787af03f0e296075586ab4da32f0422af | 14,325 | ipynb | Jupyter Notebook | Final Drafts/.ipynb_checkpoints/Module 0, a quick overview-checkpoint.ipynb | lheagy/inversion-tutorial | 78673e800d4662992e5fe9595b4422bc2d5c50e9 | [
"MIT"
] | 2 | 2017-10-08T02:10:35.000Z | 2017-10-18T17:49:21.000Z | Final Drafts/.ipynb_checkpoints/Module 0, a quick overview-checkpoint.ipynb | lheagy/inversion-tutorial | 78673e800d4662992e5fe9595b4422bc2d5c50e9 | [
"MIT"
] | null | null | null | Final Drafts/.ipynb_checkpoints/Module 0, a quick overview-checkpoint.ipynb | lheagy/inversion-tutorial | 78673e800d4662992e5fe9595b4422bc2d5c50e9 | [
"MIT"
] | 3 | 2016-09-01T20:38:20.000Z | 2020-05-13T22:19:16.000Z | 60.699153 | 1,083 | 0.644887 | true | 2,667 | Qwen/Qwen-72B | 1. YES
2. YES | 0.824462 | 0.798187 | 0.658075 | __label__eng_Latn | 0.999764 | 0.367259 |
# Ce notebook est *en cours de rédaction*
- Je vais implémenter une fonction, en [Python 3](https://docs.python.org/3/), qui permettra de résoudre rapidement un problème mathématique.
---
## Exposé du problème :
- Soit $n \geq 1$ un nombre de faces pour des dés bien équilibrés. On prendre $n = 6$ pour commencer, mais ... | e64d5f28d3a889a116dc70a3089ea7fff2c712b0 | 105,824 | ipynb | Jupyter Notebook | simus/Calcul_d_une_paire_de_des_un_peu_particuliers.ipynb | dutc/notebooks | e3ac65c22f6ad2ce863c0b80a999f029fff1ca2c | [
"MIT"
] | 4 | 2017-05-15T19:41:09.000Z | 2019-04-09T11:34:07.000Z | simus/Calcul_d_une_paire_de_des_un_peu_particuliers.ipynb | dutc/notebooks | e3ac65c22f6ad2ce863c0b80a999f029fff1ca2c | [
"MIT"
] | null | null | null | simus/Calcul_d_une_paire_de_des_un_peu_particuliers.ipynb | dutc/notebooks | e3ac65c22f6ad2ce863c0b80a999f029fff1ca2c | [
"MIT"
] | 5 | 2017-07-25T05:05:55.000Z | 2021-09-27T07:47:43.000Z | 40.530065 | 21,760 | 0.581191 | true | 18,888 | Qwen/Qwen-72B | 1. YES
2. YES | 0.877477 | 0.851953 | 0.747569 | __label__fra_Latn | 0.428865 | 0.575185 |
# Neuro-Fuzzy Classification of MNIST
In this notebook a neuro-fuzzy classifier will be trained and evaluated on the MNIST dataset. No feature reductions techniques will be used, the intent is to provide a baseline performance to compare to other techniques.
```python
import gzip
import numpy as np
def load_data(fil... | 5585dd4858a0f0d315f32677b39b4d77b2f423bc | 206,105 | ipynb | Jupyter Notebook | mnist/classifier-mnist.ipynb | rkluzinski/research-2019 | 105a295b40c0f21349dbbc6ab992eced4c36bc67 | [
"MIT"
] | 2 | 2020-10-10T06:46:08.000Z | 2022-03-29T03:08:45.000Z | mnist/classifier-mnist.ipynb | christianfv5/Deep_Fuzzy_1 | 105a295b40c0f21349dbbc6ab992eced4c36bc67 | [
"MIT"
] | null | null | null | mnist/classifier-mnist.ipynb | christianfv5/Deep_Fuzzy_1 | 105a295b40c0f21349dbbc6ab992eced4c36bc67 | [
"MIT"
] | null | null | null | 241.05848 | 132,576 | 0.897494 | true | 6,625 | Qwen/Qwen-72B | 1. YES
2. YES | 0.891811 | 0.849971 | 0.758014 | __label__eng_Latn | 0.346758 | 0.599452 |
```python
import loader
from sympy import *
init_printing()
from root.solver import *
```
#### Find the general solution of $y^{(4)} - 4y''' + 4y'' = 0$
```python
yc, p = nth_order_const_coeff(1, -4, 4, 0, 0)
p.display()
```
$\displaystyle \text{Characteristic equation: }$
$\displaystyle r^{4} - 4 r^{3} + 4 r^... | 3d6157fb16a89d8aa0e6f90946945568562dd7f4 | 4,519 | ipynb | Jupyter Notebook | notebooks/higher-order-homogeneous-ode-constant-coefficients.ipynb | kaiyingshan/ode-solver | 30c6798efe9c35a088b2c6043493470701641042 | [
"MIT"
] | 2 | 2019-02-17T23:15:20.000Z | 2019-02-17T23:15:27.000Z | notebooks/higher-order-homogeneous-ode-constant-coefficients.ipynb | kaiyingshan/ode-solver | 30c6798efe9c35a088b2c6043493470701641042 | [
"MIT"
] | null | null | null | notebooks/higher-order-homogeneous-ode-constant-coefficients.ipynb | kaiyingshan/ode-solver | 30c6798efe9c35a088b2c6043493470701641042 | [
"MIT"
] | null | null | null | 23.293814 | 381 | 0.451427 | true | 606 | Qwen/Qwen-72B | 1. YES
2. YES | 0.937211 | 0.868827 | 0.814274 | __label__yue_Hant | 0.3151 | 0.730164 |
# 2 Potential Outcomes
## 2.1 Potential Outcomes and Individual Treatment Effects
#### Potential Outcome
- 表示如果采用treatment T，输出将会是什么，用$Y(t)$表示
- 不是实际观察到的输出$Y$
#### Individual Treatment Effect (ITE)
个体$i$的ITE：
$$\tau_i \triangleq Y_i(1)-Y_i(0)$$
## 2.2 The Fundamental Problem of Causal Inference
#### Fundamental P... | 91a8ac3603f0873ca2f80aee7b0049a54f7bc89c | 14,673 | ipynb | Jupyter Notebook | docs/causal_inference/introduction_to_causal_inference/ch2.ipynb | cancermqiao/CancerMBook | bd26c0e3e1f76f66b75aacf75b3cb8602715e803 | [
"Apache-2.0"
] | null | null | null | docs/causal_inference/introduction_to_causal_inference/ch2.ipynb | cancermqiao/CancerMBook | bd26c0e3e1f76f66b75aacf75b3cb8602715e803 | [
"Apache-2.0"
] | null | null | null | docs/causal_inference/introduction_to_causal_inference/ch2.ipynb | cancermqiao/CancerMBook | bd26c0e3e1f76f66b75aacf75b3cb8602715e803 | [
"Apache-2.0"
] | null | null | null | 34.606132 | 325 | 0.555442 | true | 3,498 | Qwen/Qwen-72B | 1. YES
2. YES | 0.699254 | 0.779993 | 0.545413 | __label__eng_Latn | 0.298027 | 0.105508 |
### Nomenclature:
$d$ means a derivative ALONG the saturation line,
$\partial$ means a partial derivative AT the saturation line (or anywhere in the single phase region).
### References:
Krafcik and Velasco, DOI 10.1119/1.4858403
Thorade and Saadat, DOI 10.1007/s12665-013-2394-z
https://en.wikipedia.org/wiki... | adf8d67f6f7f8facd4b956b44ffad18d3ceb250a | 288,465 | ipynb | Jupyter Notebook | doc/notebooks/Saturation.ipynb | pauliacomi/CoolProp | 80eb4601c67ecd04353067663db50937fd7ccdae | [
"MIT"
] | 520 | 2015-01-14T16:49:41.000Z | 2022-03-29T07:48:50.000Z | doc/notebooks/Saturation.ipynb | pauliacomi/CoolProp | 80eb4601c67ecd04353067663db50937fd7ccdae | [
"MIT"
] | 1,647 | 2015-01-01T07:42:45.000Z | 2022-03-31T23:48:56.000Z | doc/notebooks/Saturation.ipynb | pauliacomi/CoolProp | 80eb4601c67ecd04353067663db50937fd7ccdae | [
"MIT"
] | 320 | 2015-01-02T17:24:27.000Z | 2022-03-19T07:01:00.000Z | 302.057592 | 87,411 | 0.891093 | true | 12,695 | Qwen/Qwen-72B | 1. YES
2. YES | 0.909907 | 0.760651 | 0.692121 | __label__eng_Latn | 0.154741 | 0.446361 |
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