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## Divide y vencerás
Este es un método de diseño de algoritmos que se basa en *subdividir* el problema en sub-problemas, resolverlos *recursivamente*, y luego *combinar* las soluciones de los sub-problemas para construir la solución del problema original. Es necesario que los subproblemas tengan la misma estructura qu... | c580d718bf76158a572568de2acf9a892e6899af | 9,538 | ipynb | Jupyter Notebook | Dividir_para_reinar.ipynb | femunoz/AED | 7d156a385ff4e4948f53003eb161ff2df0088bb2 | [
"CC0-1.0"
] | 1 | 2022-01-16T16:15:34.000Z | 2022-01-16T16:15:34.000Z | Dividir_para_reinar.ipynb | femunoz/AED | 7d156a385ff4e4948f53003eb161ff2df0088bb2 | [
"CC0-1.0"
] | null | null | null | Dividir_para_reinar.ipynb | femunoz/AED | 7d156a385ff4e4948f53003eb161ff2df0088bb2 | [
"CC0-1.0"
] | null | null | null | 40.935622 | 833 | 0.52705 | true | 1,904 | Qwen/Qwen-72B | 1. YES
2. YES | 0.795658 | 0.91118 | 0.724988 | __label__spa_Latn | 0.988202 | 0.522721 |
# SLU11 - Tree-based models: Learning notebook
## Imports
```python
from math import log2
import pandas as pd
import numpy as np
from sklearn.datasets import load_boston
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.metrics import mean_squared_error
from sklearn.tree ... | 6bc88c3d1295c5d3a5407c8ef30965529b8e4621 | 314,141 | ipynb | Jupyter Notebook | S01 - Bootcamp and Binary Classification/SLU11 - Tree-Based Models/Learning notebook.ipynb | FarhadManiCodes/batch5-students | 3a147145dc4f4ac65a851542987cf687b9915d5b | [
"MIT"
] | 2 | 2022-02-04T17:40:04.000Z | 2022-03-26T18:03:12.000Z | S01 - Bootcamp and Binary Classification/SLU11 - Tree-Based Models/Learning notebook.ipynb | FarhadManiCodes/batch5-students | 3a147145dc4f4ac65a851542987cf687b9915d5b | [
"MIT"
] | null | null | null | S01 - Bootcamp and Binary Classification/SLU11 - Tree-Based Models/Learning notebook.ipynb | FarhadManiCodes/batch5-students | 3a147145dc4f4ac65a851542987cf687b9915d5b | [
"MIT"
] | 2 | 2021-10-30T16:20:13.000Z | 2021-11-25T12:09:31.000Z | 152.569694 | 106,160 | 0.866108 | true | 12,011 | Qwen/Qwen-72B | 1. YES
2. YES | 0.882428 | 0.839734 | 0.741005 | __label__eng_Latn | 0.988157 | 0.559934 |
```python
import numpy as np
import matplotlib.pyplot as plt
from sympy import *
f, d, D, z, kx = symbols('f,d,D,Z, K_x')
z = symbols('Z')
Delta = Symbol("Delta")
eq_right = Delta * z
eq_left = Abs(1/d-1/(d+Delta*d))*f*D*kx
Eq(eq_right, eq_left)
```
$\displaystyle \Delta Z = D K_{x} f \left|{\frac{1}{\Delta d + d}... | db7d93839ef39f15c445d821d7063fb3ad55a6db | 206,605 | ipynb | Jupyter Notebook | measurement_model.ipynb | nanaimi/aux_tools | 397fd959d48b616c604ac490bc007aacc51cd6ed | [
"BSD-2-Clause"
] | null | null | null | measurement_model.ipynb | nanaimi/aux_tools | 397fd959d48b616c604ac490bc007aacc51cd6ed | [
"BSD-2-Clause"
] | null | null | null | measurement_model.ipynb | nanaimi/aux_tools | 397fd959d48b616c604ac490bc007aacc51cd6ed | [
"BSD-2-Clause"
] | null | null | null | 1,395.97973 | 202,268 | 0.956986 | true | 756 | Qwen/Qwen-72B | 1. YES
2. YES | 0.917303 | 0.718594 | 0.659169 | __label__eng_Latn | 0.508754 | 0.3698 |
# Poisson Distribution
> ***GitHub***: https://github.com/czs108
## Definition
\begin{equation}
P(X = r) = \frac{e^{-\lambda} \cdot {\lambda}^{r}}{r!}
\end{equation}
\begin{equation}
\lambda = \text{The mean number of occurrences in the interval or the rate of occurrence.}
\end{equation}
If a variable $X$ follows ... | 28bd062f42e394a77e59fa24b9aceb6a9aecc546 | 2,222 | ipynb | Jupyter Notebook | exercises/Poisson Distribution.ipynb | czs108/Probability-Theory-Exercises | 60c6546db1e7f075b311d1e59b0afc3a13d93229 | [
"MIT"
] | null | null | null | exercises/Poisson Distribution.ipynb | czs108/Probability-Theory-Exercises | 60c6546db1e7f075b311d1e59b0afc3a13d93229 | [
"MIT"
] | null | null | null | exercises/Poisson Distribution.ipynb | czs108/Probability-Theory-Exercises | 60c6546db1e7f075b311d1e59b0afc3a13d93229 | [
"MIT"
] | 1 | 2022-03-21T05:04:07.000Z | 2022-03-21T05:04:07.000Z | 23.638298 | 178 | 0.49865 | true | 366 | Qwen/Qwen-72B | 1. YES
2. YES | 0.909907 | 0.91848 | 0.835732 | __label__eng_Latn | 0.973664 | 0.780018 |
<h1>SymPy: Open Source Symbolic Mathematics</h1>
```
%load_ext sympyprinting
```
```
from __future__ import division
from sympy import *
x, y, z = symbols("x y z")
k, m, n = symbols("k m n", integer=True)
f, g, h = map(Function, 'fgh')
```
<h2>Elementary operations</h2>
```
Rational(3,2)*pi + exp(I*x) / (x**2 + ... | 650b9291537b166ff3d4655fe5673e07652a4d9f | 37,033 | ipynb | Jupyter Notebook | docs/examples/notebooks/sympy.ipynb | tinyclues/ipython | 71e32606b0242772b81c9be0d40751ba47d95f2c | [
"BSD-3-Clause-Clear"
] | 1 | 2016-05-26T10:57:18.000Z | 2016-05-26T10:57:18.000Z | docs/examples/notebooks/sympy.ipynb | adgaudio/ipython | a924f50c0f7b84127391f1c396326258c2b303e2 | [
"BSD-3-Clause-Clear"
] | null | null | null | docs/examples/notebooks/sympy.ipynb | adgaudio/ipython | a924f50c0f7b84127391f1c396326258c2b303e2 | [
"BSD-3-Clause-Clear"
] | null | null | null | 138.182836 | 3,463 | 0.734021 | true | 453 | Qwen/Qwen-72B | 1. YES
2. YES | 0.960361 | 0.847968 | 0.814355 | __label__kor_Hang | 0.127141 | 0.730353 |
## Multiple Linear Regression
We will see how multiple input variables together influence the output variable, while also learning how the calculations differ from that of Simple LR model. We will also build a regression model using Python.
At last, we will go deeper into Linear Regression and will learn things like Mu... | 6da3c734da62ed5271f59e32d2cf68280e7323f2 | 229,583 | ipynb | Jupyter Notebook | 05_Multi Linear Regression/03_Advertisment_Solution.ipynb | kunalk3/eR_task | 4d717680576bc62793c42840e772a28fb3f6c223 | [
"MIT"
] | 1 | 2021-02-25T13:58:57.000Z | 2021-02-25T13:58:57.000Z | 05_Multi Linear Regression/03_Advertisment_Solution.ipynb | kunalk3/eR_task | 4d717680576bc62793c42840e772a28fb3f6c223 | [
"MIT"
] | null | null | null | 05_Multi Linear Regression/03_Advertisment_Solution.ipynb | kunalk3/eR_task | 4d717680576bc62793c42840e772a28fb3f6c223 | [
"MIT"
] | null | null | null | 186.047812 | 108,106 | 0.83937 | true | 7,080 | Qwen/Qwen-72B | 1. YES
2. YES | 0.851953 | 0.880797 | 0.750398 | __label__eng_Latn | 0.850391 | 0.581757 |
# hw 6: estimators
learning objectives:
* solidify "what is an estimator"
* evaluate the effectiveness of an estimator computationally (through simulation)
* understand the notion of unbiasedness, consistency, and efficiency of an estimator and evaluate these qualities computationally (through simulation)
```julia
us... | 0387d7762847a676d2151bb5692542b8488188a2 | 160,272 | ipynb | Jupyter Notebook | Homework/Homework 6/Homework 6.ipynb | cartemic/CHE-599-intro-to-data-science | a2afe72b51a3b9e844de94d59961bedc3534a405 | [
"MIT"
] | null | null | null | Homework/Homework 6/Homework 6.ipynb | cartemic/CHE-599-intro-to-data-science | a2afe72b51a3b9e844de94d59961bedc3534a405 | [
"MIT"
] | null | null | null | Homework/Homework 6/Homework 6.ipynb | cartemic/CHE-599-intro-to-data-science | a2afe72b51a3b9e844de94d59961bedc3534a405 | [
"MIT"
] | 2 | 2019-10-02T16:11:36.000Z | 2019-10-15T20:10:40.000Z | 232.278261 | 74,214 | 0.908524 | true | 3,713 | Qwen/Qwen-72B | 1. YES
2. YES | 0.824462 | 0.828939 | 0.683428 | __label__eng_Latn | 0.986421 | 0.426165 |
# Simple Linear Regression with NumPy
In school, students are taught to draw lines like the following.
$$ y = 2 x + 1$$
They're taught to pick two values for $x$ and calculate the corresponding values for $y$ using the equation.
Then they draw a set of axes, plot the points, and then draw a line extending through th... | c6d6c8de8171f8aedd804b06ae6ed3ad2723e18e | 288,104 | ipynb | Jupyter Notebook | simple-linear-regression.ipynb | mikequaid/numpy | 350e0dc3d2bc482c69be60b41619fc710a778eb3 | [
"Apache-2.0"
] | null | null | null | simple-linear-regression.ipynb | mikequaid/numpy | 350e0dc3d2bc482c69be60b41619fc710a778eb3 | [
"Apache-2.0"
] | null | null | null | simple-linear-regression.ipynb | mikequaid/numpy | 350e0dc3d2bc482c69be60b41619fc710a778eb3 | [
"Apache-2.0"
] | null | null | null | 274.908397 | 117,404 | 0.916822 | true | 6,637 | Qwen/Qwen-72B | 1. YES
2. YES | 0.944177 | 0.849971 | 0.802523 | __label__eng_Latn | 0.99514 | 0.702863 |
# ADM Quantities in terms of BSSN Quantities
## Author: Zach Etienne
### Formatting improvements courtesy Brandon Clark
[comment]: <> (Abstract: TODO)
**Module Status:** <font color='orange'><b> Self-Validated </b></font>
**Validation Notes:** This tutorial module has been confirmed to be self-consistent with it... | 538ba5f82d979edff77ab41d18c5a071e6f451c7 | 24,195 | ipynb | Jupyter Notebook | Tutorial-ADM_in_terms_of_BSSN.ipynb | dinatraykova/nrpytutorial | 74d1bab0c45380727975568ba956b69c082e2293 | [
"BSD-2-Clause"
] | null | null | null | Tutorial-ADM_in_terms_of_BSSN.ipynb | dinatraykova/nrpytutorial | 74d1bab0c45380727975568ba956b69c082e2293 | [
"BSD-2-Clause"
] | null | null | null | Tutorial-ADM_in_terms_of_BSSN.ipynb | dinatraykova/nrpytutorial | 74d1bab0c45380727975568ba956b69c082e2293 | [
"BSD-2-Clause"
] | 2 | 2019-11-14T03:31:18.000Z | 2019-12-12T13:42:52.000Z | 43.75226 | 559 | 0.553296 | true | 6,040 | Qwen/Qwen-72B | 1. YES
2. YES | 0.805632 | 0.709019 | 0.571209 | __label__eng_Latn | 0.550014 | 0.165439 |
# The positive predictive value
## Let's see this notebook in a better format :
### [HERE](http://www.reproducibleimaging.org/module-stats/05-PPV/)
## Some Definitions
* $H_0$ : null hypothesis: The hypotheis that the effect we are testing for is null
* $H_A$ : alternative hypothesis : Not $H_0$, so there is some ... | c88ee41bd33e81eddda97893e5047d54154ae8f9 | 77,097 | ipynb | Jupyter Notebook | notebooks/Positive-Predictive-Value.ipynb | ReproNim/stat-repronim-module | ccef0fa1d23d0023db4cbbfc1e3091037df77f3b | [
"CC-BY-4.0"
] | 3 | 2020-02-27T19:04:46.000Z | 2020-02-27T19:13:30.000Z | notebooks/Positive-Predictive-Value.ipynb | ReproNim/stat-repronim-module | ccef0fa1d23d0023db4cbbfc1e3091037df77f3b | [
"CC-BY-4.0"
] | 6 | 2016-11-12T02:07:16.000Z | 2020-06-11T10:47:46.000Z | notebooks/Positive-Predictive-Value.ipynb | ReproNim/stat-repronim-module | ccef0fa1d23d0023db4cbbfc1e3091037df77f3b | [
"CC-BY-4.0"
] | 12 | 2016-11-03T18:03:01.000Z | 2021-06-04T06:53:07.000Z | 105.32377 | 19,614 | 0.835558 | true | 3,274 | Qwen/Qwen-72B | 1. YES
2. YES | 0.853913 | 0.857768 | 0.732459 | __label__eng_Latn | 0.87698 | 0.54008 |
```python
import numpy as np
import dapy.filters as filters
from dapy.models import NettoGimenoMendesModel
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('seaborn-white')
plt.rcParams['figure.dpi'] = 100
```
## Model
One-dimensional stochastic dynamical system due to Netto et al. [1] with state dyna... | 13ef3d9f741f183a0f37eacdc1ff4688d954795b | 988,058 | ipynb | Jupyter Notebook | notebooks/Netto_Gimeno_Mendes_1979_non_linear_model.ipynb | hassaniqbal209/data-assimilation | ec52d655395dbed547edf4b4f3df29f017633f1b | [
"MIT"
] | 11 | 2020-07-29T07:46:39.000Z | 2022-03-17T01:28:07.000Z | notebooks/Netto_Gimeno_Mendes_1979_non_linear_model.ipynb | hassaniqbal209/data-assimilation | ec52d655395dbed547edf4b4f3df29f017633f1b | [
"MIT"
] | 1 | 2020-07-14T11:49:17.000Z | 2020-07-29T07:43:22.000Z | notebooks/Netto_Gimeno_Mendes_1979_non_linear_model.ipynb | hassaniqbal209/data-assimilation | ec52d655395dbed547edf4b4f3df29f017633f1b | [
"MIT"
] | 10 | 2020-07-14T11:34:24.000Z | 2022-03-07T09:08:12.000Z | 1,709.442907 | 232,228 | 0.958537 | true | 3,078 | Qwen/Qwen-72B | 1. YES
2. YES | 0.79053 | 0.841826 | 0.665489 | __label__eng_Latn | 0.195089 | 0.384484 |
# Calculate near surface RH% using ERA-interim fields
* 2-m dew point
* 2-m temperature
* surface pressure
Once the yearly RH files are made, merge these data into a single file and put into the era merged time directory, then, regrid that file and place in the common grid file, such that this newly created variable ... | bbb1577ca7810c11a711957f0d29f8707540a0d4 | 256,025 | ipynb | Jupyter Notebook | Python/calculate_RH_from_d2m.ipynb | stevenjoelbrey/metSpread | 38d667f0e2f58563345fed14132bb5a6eb7022af | [
"MIT"
] | null | null | null | Python/calculate_RH_from_d2m.ipynb | stevenjoelbrey/metSpread | 38d667f0e2f58563345fed14132bb5a6eb7022af | [
"MIT"
] | null | null | null | Python/calculate_RH_from_d2m.ipynb | stevenjoelbrey/metSpread | 38d667f0e2f58563345fed14132bb5a6eb7022af | [
"MIT"
] | null | null | null | 795.108696 | 246,192 | 0.948536 | true | 1,890 | Qwen/Qwen-72B | 1. YES
2. YES | 0.904651 | 0.79053 | 0.715154 | __label__eng_Latn | 0.775865 | 0.499873 |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W2D1_DeepLearning/W2D1_Tutorial1.ipynb" target="_parent"></a> <a href="https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/NeuromatchAcademy/course-content/master/tutorials/W2D1_DeepLearn... | 1d92fc3d10dd29e692b0535d6bc23aa0620b7bfa | 72,759 | ipynb | Jupyter Notebook | tutorials/W2D1_DeepLearning/W2D1_Tutorial1.ipynb | Beilinson/course-content | b74c630bec7002abe2f827ff8e0707f9bbb43f82 | [
"CC-BY-4.0"
] | null | null | null | tutorials/W2D1_DeepLearning/W2D1_Tutorial1.ipynb | Beilinson/course-content | b74c630bec7002abe2f827ff8e0707f9bbb43f82 | [
"CC-BY-4.0"
] | null | null | null | tutorials/W2D1_DeepLearning/W2D1_Tutorial1.ipynb | Beilinson/course-content | b74c630bec7002abe2f827ff8e0707f9bbb43f82 | [
"CC-BY-4.0"
] | null | null | null | 50.492019 | 1,248 | 0.633818 | true | 14,130 | Qwen/Qwen-72B | 1. YES
2. YES | 0.795658 | 0.774583 | 0.616304 | __label__eng_Latn | 0.993485 | 0.27021 |
# 亜臨界ホップ分岐の標準形
\begin{equation}
\begin{aligned}
\dot{x}_0 = \lambda x_0 - \omega x_1 + x_0 \left[ c_1 (x_0^2 + x_1^2) - (x_0^2 + x_1^2)^2 \right],\\
\dot{x}_1 = \omega x_0 + \lambda x_1 + x_1 \left[ c_1 (x_0^2 + x_1^2) - (x_0^2 + x_1^2)^2 \right],\\
\end{aligned}
\end{equation}
```python
import numpy as np
i... | 578d019d016e5c34da29309d1a28e9c9ae10eed6 | 59,725 | ipynb | Jupyter Notebook | notebooks/continuation/subhopf.ipynb | tmiyaji/sgc164 | 660f61b72a3898f8e287feb464134f5c48f9383e | [
"BSD-3-Clause"
] | 3 | 2021-02-01T15:29:43.000Z | 2021-10-01T13:20:21.000Z | notebooks/continuation/subhopf.ipynb | tmiyaji/sgc164 | 660f61b72a3898f8e287feb464134f5c48f9383e | [
"BSD-3-Clause"
] | null | null | null | notebooks/continuation/subhopf.ipynb | tmiyaji/sgc164 | 660f61b72a3898f8e287feb464134f5c48f9383e | [
"BSD-3-Clause"
] | 1 | 2020-12-20T07:46:22.000Z | 2020-12-20T07:46:22.000Z | 70.264706 | 22,460 | 0.728221 | true | 13,386 | Qwen/Qwen-72B | 1. YES
2. YES | 0.810479 | 0.63341 | 0.513366 | __label__yue_Hant | 0.135212 | 0.03105 |
# One-dimensional Lagrange Interpolation
The problem of interpolation or finding the value of a function at an arbitrary point $X$ inside a given domain, provided we have discrete known values of the function inside the same domain is at the heart of the finite element method. In this notebooke we use Lagrange interpo... | 58a534a611940b799937c611a4ccef212bf5295a | 151,725 | ipynb | Jupyter Notebook | notebooks/.ipynb_checkpoints/LAGRANGE1D-checkpoint.ipynb | jomorlier/FEM-Notes | 3b81053aee79dc59965c3622bc0d0eb6cfc7e8ae | [
"MIT"
] | 1 | 2020-04-15T01:53:14.000Z | 2020-04-15T01:53:14.000Z | notebooks/.ipynb_checkpoints/LAGRANGE1D-checkpoint.ipynb | jomorlier/FEM-Notes | 3b81053aee79dc59965c3622bc0d0eb6cfc7e8ae | [
"MIT"
] | null | null | null | notebooks/.ipynb_checkpoints/LAGRANGE1D-checkpoint.ipynb | jomorlier/FEM-Notes | 3b81053aee79dc59965c3622bc0d0eb6cfc7e8ae | [
"MIT"
] | 1 | 2020-05-25T17:19:53.000Z | 2020-05-25T17:19:53.000Z | 81.969206 | 42,435 | 0.718853 | true | 967 | Qwen/Qwen-72B | 1. YES
2. YES | 0.957912 | 0.931463 | 0.892259 | __label__eng_Latn | 0.938223 | 0.911351 |
# Density Operator and Matrix
## Imports
```python
from IPython.display import display
```
```python
# TODO: there is a bug in density.py that is preventing this from working, uncomment to reproduce
# from sympy import init_printing
# init_printing(use_latex=True)
```
```python
from sympy import *
from sympy.co... | bd0b0a21c066c3979424793b744f88d6214a8f24 | 16,217 | ipynb | Jupyter Notebook | notebooks/density.ipynb | gvvynplaine/quantum_notebooks | 58783823596465fe2d6c494c2cc3a53ae69a9752 | [
"BSD-3-Clause"
] | 42 | 2017-10-17T22:44:27.000Z | 2022-03-28T06:26:46.000Z | notebooks/density.ipynb | gvvynplaine/quantum_notebooks | 58783823596465fe2d6c494c2cc3a53ae69a9752 | [
"BSD-3-Clause"
] | 2 | 2017-10-09T05:16:41.000Z | 2018-09-22T03:08:29.000Z | notebooks/density.ipynb | gvvynplaine/quantum_notebooks | 58783823596465fe2d6c494c2cc3a53ae69a9752 | [
"BSD-3-Clause"
] | 12 | 2017-10-09T04:22:19.000Z | 2022-03-28T06:25:21.000Z | 17.801317 | 142 | 0.446075 | true | 1,854 | Qwen/Qwen-72B | 1. YES
2. YES | 0.907312 | 0.839734 | 0.761901 | __label__yue_Hant | 0.317466 | 0.608483 |
# 베타 분포
베타 분포(Beta distribution)는 다른 확률 분포와 달리 자연계에 존재하는 데이터의 분포를 묘사하기 보다는 베이지안 추정의 결과를 묘사하기위한 목적으로 주로 사용된다. 베이지안 추정(Bayesian estimation)은 추정하고자 하는 모수의 값을 하나의 숫자로 나타내는 것이 아니라 분포로 묘사한다.
베타 분포의 확률 밀도 함수는 $a$와 $b$라는 두 개의 모수(parameter)를 가지며 수학적으로 다음과 같이 정의된다.
$$
\begin{align}
\text{Beta}(x;a,b)
& = \frac{\Gamma(a+b)}... | 6ed1612186260640ac73e5737fbb551c9c74097d | 245,850 | ipynb | Jupyter Notebook | 10. 기초 확률론3 - 확률 분포 모형/08. 베타 분포 (파이썬 버전).ipynb | zzsza/Datascience_School | da27ac760ca8ad1a563a0803a08b332d560cbdc0 | [
"MIT"
] | 39 | 2017-04-30T06:17:21.000Z | 2022-01-07T07:50:11.000Z | 10. 기초 확률론3 - 확률 분포 모형/08. 베타 분포 (파이썬 버전).ipynb | yeajunseok/Datascience_School | da27ac760ca8ad1a563a0803a08b332d560cbdc0 | [
"MIT"
] | null | null | null | 10. 기초 확률론3 - 확률 분포 모형/08. 베타 분포 (파이썬 버전).ipynb | yeajunseok/Datascience_School | da27ac760ca8ad1a563a0803a08b332d560cbdc0 | [
"MIT"
] | 32 | 2017-04-09T16:51:49.000Z | 2022-01-23T20:30:48.000Z | 39.405353 | 183 | 0.499174 | true | 874 | Qwen/Qwen-72B | 1. YES
2. YES | 0.874077 | 0.798187 | 0.697677 | __label__kor_Hang | 0.999961 | 0.459269 |
# Likevektskonsentrasjoner i en syre-base likevekt
Her skal vi regne på en syre-baselikevekt, vi tar utgangspunkt i eksempel 16.8 (side 562) fra læreboken der vi blir bedt om å finne pH i 0.036 M HNO$_2$:
$$\text{HNO}_2 \rightleftharpoons \text{NO}_2^{-} + \text{H}^{+},$$
$$K_{a} = 4.5 \times 10^{-4}.$$
Vi skal løse... | ff1c29ec23b2753bd8589738623ceb70c76c0631 | 8,389 | ipynb | Jupyter Notebook | jupyter/syrebase/syrebase.ipynb | andersle/kj1000 | 9d68e9810c5541ebbe2e4559df8d066a85780129 | [
"CC-BY-4.0"
] | null | null | null | jupyter/syrebase/syrebase.ipynb | andersle/kj1000 | 9d68e9810c5541ebbe2e4559df8d066a85780129 | [
"CC-BY-4.0"
] | 5 | 2021-06-21T15:04:15.000Z | 2021-11-10T10:58:07.000Z | jupyter/syrebase/syrebase.ipynb | andersle/kj1000 | 9d68e9810c5541ebbe2e4559df8d066a85780129 | [
"CC-BY-4.0"
] | null | null | null | 28.056856 | 418 | 0.571582 | true | 1,626 | Qwen/Qwen-72B | 1. YES
2. YES | 0.913677 | 0.843895 | 0.771047 | __label__nob_Latn | 0.996098 | 0.629733 |
```python
from galgebra.ga import Ga
from sympy import symbols
from galgebra.printer import Format
Format()
coords = (et,ex,ey,ez) = symbols('t,x,y,z',real=True)
base=Ga('e*t|x|y|z',g=[1,-1,-1,-1],coords=symbols('t,x,y,z',real=True),wedge=False)
potential=base.mv('phi','vector',f=True)
potential
```
\begin{equati... | 80b34e5cf6b2d4173d98d854c8435e03f70c3487 | 5,598 | ipynb | Jupyter Notebook | examples/ipython/second_derivative.ipynb | pygae/galgebra | 3a53b29fb141be1ae47d8df8fc7005c10869cded | [
"BSD-3-Clause"
] | 151 | 2018-09-18T12:30:14.000Z | 2022-03-16T08:02:48.000Z | examples/ipython/second_derivative.ipynb | caiomrcs/galgebra | 3a53b29fb141be1ae47d8df8fc7005c10869cded | [
"BSD-3-Clause"
] | 454 | 2018-09-19T01:42:30.000Z | 2022-01-18T14:02:00.000Z | examples/ipython/second_derivative.ipynb | caiomrcs/galgebra | 3a53b29fb141be1ae47d8df8fc7005c10869cded | [
"BSD-3-Clause"
] | 30 | 2019-02-22T08:25:50.000Z | 2022-01-15T05:20:22.000Z | 36.350649 | 741 | 0.49732 | true | 1,107 | Qwen/Qwen-72B | 1. YES
2. YES | 0.908618 | 0.805632 | 0.732012 | __label__eng_Latn | 0.188474 | 0.539041 |
# Homework 2
**For exercises in the week 20-25.11.19**
**Points: 6 + 1b**
Please solve the problems at home and bring to class a [declaration form](http://ii.uni.wroc.pl/~jmi/Dydaktyka/misc/kupony-klasyczne.pdf) to indicate which problems you are willing to present on the blackboard.
## Problem 1 [1p]
Let $(x^{(... | 1c50bf310106f232668dd6294f9c9ba1199121f1 | 8,010 | ipynb | Jupyter Notebook | ML/Homework2/Homework2.ipynb | TheFebrin/DataScience | 3e58b89315960e7d4896e44075a8105fcb78f0c0 | [
"MIT"
] | null | null | null | ML/Homework2/Homework2.ipynb | TheFebrin/DataScience | 3e58b89315960e7d4896e44075a8105fcb78f0c0 | [
"MIT"
] | null | null | null | ML/Homework2/Homework2.ipynb | TheFebrin/DataScience | 3e58b89315960e7d4896e44075a8105fcb78f0c0 | [
"MIT"
] | null | null | null | 37.605634 | 294 | 0.492135 | true | 1,600 | Qwen/Qwen-72B | 1. YES
2. YES | 0.861538 | 0.822189 | 0.708347 | __label__eng_Latn | 0.978818 | 0.48406 |
# Maximum likelihood Estimation (MLE)
based on http://python-for-signal-processing.blogspot.com/2012/10/maximum-likelihood-estimation-maximum.html
## Simulate coin flipping
- [Bernoulli distribution](https://en.wikipedia.org/wiki/Bernoulli_distribution)
is the probability distribution of a random variable which takes ... | 4940682496d40fe493ef3f22d66b75e23e264263 | 46,821 | ipynb | Jupyter Notebook | mle.ipynb | hyzhak/mle | 257d8046a950b7381052cc56d9931cf98aeb0a5c | [
"MIT"
] | 1 | 2017-10-22T09:29:36.000Z | 2017-10-22T09:29:36.000Z | mle.ipynb | hyzhak/mle | 257d8046a950b7381052cc56d9931cf98aeb0a5c | [
"MIT"
] | null | null | null | mle.ipynb | hyzhak/mle | 257d8046a950b7381052cc56d9931cf98aeb0a5c | [
"MIT"
] | 3 | 2019-01-23T04:46:01.000Z | 2020-04-21T18:38:49.000Z | 199.238298 | 21,420 | 0.894876 | true | 1,098 | Qwen/Qwen-72B | 1. YES
2. YES | 0.946597 | 0.874077 | 0.827399 | __label__eng_Latn | 0.443614 | 0.760657 |
# From transfer function to difference equation
In approximately the middle of Peter Corke's lecture [Introduction to digital control](https://youtu.be/XuR3QKVtx-g?t=34m56s), he explaines how to go from a transfer function description of a controller (or compensator) to a difference equation that can be implemented on ... | b951561490a92e552738afa4fd29e0043a64cb6c | 81,521 | ipynb | Jupyter Notebook | discrete-time-systems/notebooks/Simple-approximation.ipynb | kjartan-at-tec/mr2007-computerized-control | 16e35f5007f53870eaf344eea1165507505ab4aa | [
"MIT"
] | 2 | 2020-11-07T05:20:37.000Z | 2020-12-22T09:46:13.000Z | discrete-time-systems/notebooks/Simple-approximation.ipynb | alfkjartan/control-computarizado | 5b9a3ae67602d131adf0b306f3ffce7a4914bf8e | [
"MIT"
] | 4 | 2020-06-12T20:44:41.000Z | 2020-06-12T20:49:00.000Z | discrete-time-systems/notebooks/Simple-approximation.ipynb | kjartan-at-tec/mr2007-computerized-control | 16e35f5007f53870eaf344eea1165507505ab4aa | [
"MIT"
] | 1 | 2021-03-14T03:55:27.000Z | 2021-03-14T03:55:27.000Z | 254.753125 | 25,800 | 0.905619 | true | 2,237 | Qwen/Qwen-72B | 1. YES
2. YES | 0.877477 | 0.843895 | 0.740498 | __label__eng_Latn | 0.967305 | 0.558758 |
```julia
using CSV
using DataFrames
using PyPlot
using ScikitLearn # machine learning package
using StatsBase
using Random
using LaTeXStrings # for L"$x$" to work instead of needing to do "\$x\$"
using Printf
using PyCall
sns = pyimport("seaborn")
# (optional)change settings for all plots at once, e.g. font size
rcPa... | e7737278b9c9cc4d8c6a6510dd6d77d372dbb9d0 | 142,366 | ipynb | Jupyter Notebook | In-Class Notes/Logistic Regression/.ipynb_checkpoints/logistic regression_sparse-checkpoint.ipynb | cartemic/CHE-599-intro-to-data-science | a2afe72b51a3b9e844de94d59961bedc3534a405 | [
"MIT"
] | null | null | null | In-Class Notes/Logistic Regression/.ipynb_checkpoints/logistic regression_sparse-checkpoint.ipynb | cartemic/CHE-599-intro-to-data-science | a2afe72b51a3b9e844de94d59961bedc3534a405 | [
"MIT"
] | null | null | null | In-Class Notes/Logistic Regression/.ipynb_checkpoints/logistic regression_sparse-checkpoint.ipynb | cartemic/CHE-599-intro-to-data-science | a2afe72b51a3b9e844de94d59961bedc3534a405 | [
"MIT"
] | 2 | 2019-10-02T16:11:36.000Z | 2019-10-15T20:10:40.000Z | 283.59761 | 75,962 | 0.919117 | true | 2,372 | Qwen/Qwen-72B | 1. YES
2. YES | 0.909907 | 0.890294 | 0.810085 | __label__eng_Latn | 0.915945 | 0.720432 |
```python
from sympy import symbols, cos, sin, pi, simplify, pprint, tan, expand_trig, sqrt, trigsimp, atan2
from sympy.matrices import Matrix
```
```python
# rotation matrices in x, y, z axes
def rotx(q):
sq, cq = sin(q), cos(q)
r = Matrix([
[1., 0., 0.],
[0., cq,-sq],
[0., sq, cq]
])
ret... | 7f2212b18becef5505df731d3b029b917e679d6e | 10,729 | ipynb | Jupyter Notebook | notebooks/total_transform.ipynb | mithi/arm-ik | e7e87ef0e43b04278d2300f67d863c3f7eafb77e | [
"MIT"
] | 39 | 2017-07-29T11:40:03.000Z | 2022-02-28T14:49:48.000Z | notebooks/total_transform.ipynb | mithi/arm-ik | e7e87ef0e43b04278d2300f67d863c3f7eafb77e | [
"MIT"
] | null | null | null | notebooks/total_transform.ipynb | mithi/arm-ik | e7e87ef0e43b04278d2300f67d863c3f7eafb77e | [
"MIT"
] | 16 | 2017-10-27T13:30:21.000Z | 2022-02-10T10:08:42.000Z | 37.645614 | 628 | 0.388759 | true | 2,816 | Qwen/Qwen-72B | 1. YES
2. YES | 0.930458 | 0.79053 | 0.735555 | __label__kor_Hang | 0.057433 | 0.547274 |
## Ainsley Works on Problem Sets
Ainsley sits down on Sunday night to finish S problem sets, where S is a random variable that is equally likely to be 1, 2, 3, or 4. She learns C concepts from the problem sets and drinks D energy drinks to stay awake, where C and D are random and depend on how many problem sets she do... | 0015f8de6c6d57b341e26dce75a57c961cf09b16 | 29,398 | ipynb | Jupyter Notebook | week04/06 Homework.ipynb | infimath/Computational-Probability-and-Inference | e48cd52c45ffd9458383ba0f77468d31f781dc77 | [
"MIT"
] | 1 | 2019-04-04T03:07:47.000Z | 2019-04-04T03:07:47.000Z | week04/06 Homework.ipynb | infimath/Computational-Probability-and-Inference | e48cd52c45ffd9458383ba0f77468d31f781dc77 | [
"MIT"
] | null | null | null | week04/06 Homework.ipynb | infimath/Computational-Probability-and-Inference | e48cd52c45ffd9458383ba0f77468d31f781dc77 | [
"MIT"
] | 1 | 2021-02-27T05:33:49.000Z | 2021-02-27T05:33:49.000Z | 131.241071 | 19,852 | 0.825192 | true | 2,333 | Qwen/Qwen-72B | 1. YES
2. YES | 0.943348 | 0.908618 | 0.857142 | __label__eng_Latn | 0.993791 | 0.829763 |
# Efficiency Analysis
## Objective and Prerequisites
How can mathematical optimization be used to measure the efficiency of an organization? Find out in this example, where you’ll learn how to formulate an Efficiency Analysis model as a linear programming problem using the Gurobi Python API and then generate an opt... | e4279e7528ba062671618e83ce5ac32405490f98 | 43,763 | ipynb | Jupyter Notebook | efficiency_analysis/efficiency_analysis.ipynb | Maninaa/modeling-examples | 51575a453d28e1e9435abd865432955b182ba577 | [
"Apache-2.0"
] | 1 | 2021-11-29T07:42:12.000Z | 2021-11-29T07:42:12.000Z | efficiency_analysis/efficiency_analysis.ipynb | Maninaa/modeling-examples | 51575a453d28e1e9435abd865432955b182ba577 | [
"Apache-2.0"
] | null | null | null | efficiency_analysis/efficiency_analysis.ipynb | Maninaa/modeling-examples | 51575a453d28e1e9435abd865432955b182ba577 | [
"Apache-2.0"
] | 1 | 2021-11-29T07:41:53.000Z | 2021-11-29T07:41:53.000Z | 45.586458 | 732 | 0.616754 | true | 9,480 | Qwen/Qwen-72B | 1. YES
2. YES | 0.907312 | 0.831143 | 0.754106 | __label__yue_Hant | 0.926135 | 0.590374 |
# Bayes by Backprop
An implementation of the algorithm described in https://arxiv.org/abs/1505.05424.
This notebook accompanies the article at https://www.nitarshan.com/bayes-by-backprop.
```python
%matplotlib inline
import math
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import torch
i... | 61073acbd5cc447c0a6dcc612edf8628509f757e | 34,069 | ipynb | Jupyter Notebook | notebooks/Weight Uncertainty in Neural Networks.ipynb | MorganeAyle/SNIP-it | df2bf44d6d3f7e4ea7733242a79c916735a7b49e | [
"MIT"
] | null | null | null | notebooks/Weight Uncertainty in Neural Networks.ipynb | MorganeAyle/SNIP-it | df2bf44d6d3f7e4ea7733242a79c916735a7b49e | [
"MIT"
] | null | null | null | notebooks/Weight Uncertainty in Neural Networks.ipynb | MorganeAyle/SNIP-it | df2bf44d6d3f7e4ea7733242a79c916735a7b49e | [
"MIT"
] | null | null | null | 30.229814 | 169 | 0.540638 | true | 4,874 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.841826 | 0.746872 | __label__eng_Latn | 0.240271 | 0.573565 |
# Controlled oscillator
The controlled oscillator is an oscillator
with an extra input that controls the frequency of the oscillation.
To implement a basic oscillator,
we would use a neural ensemble of two dimensions
that has the following dynamics:
$$
\dot{x} = \begin{bmatrix} 0 && - \omega \\ \omega && 0 \end{bmat... | 8e57ed466a9d3feef61f5f036573451f75943f6d | 6,242 | ipynb | Jupyter Notebook | docs/examples/dynamics/controlled_oscillator.ipynb | pedrombmachado/nengo | abc85e1a75ce2f980e19eef195d98081f95efd28 | [
"BSD-2-Clause"
] | null | null | null | docs/examples/dynamics/controlled_oscillator.ipynb | pedrombmachado/nengo | abc85e1a75ce2f980e19eef195d98081f95efd28 | [
"BSD-2-Clause"
] | null | null | null | docs/examples/dynamics/controlled_oscillator.ipynb | pedrombmachado/nengo | abc85e1a75ce2f980e19eef195d98081f95efd28 | [
"BSD-2-Clause"
] | null | null | null | 27.619469 | 86 | 0.512816 | true | 1,143 | Qwen/Qwen-72B | 1. YES
2. YES | 0.90053 | 0.833325 | 0.750434 | __label__eng_Latn | 0.967176 | 0.581841 |
---
# What are the marginal and the conditional probabilities?
---
In this script, we show the 1-D marginal and 1-D conditional probability density functions (PDF) for a 2-D gaussian PDF.
In its matrix-form, the equation for the 2-D gausian PDF reads like this:
<blockquote> $P(\bf{x}) = \frac{1}{2\pi |\Sigma|^{0.5... | 6392f9b66e7d0733bbc0d33d40004d0b38bfaa97 | 85,517 | ipynb | Jupyter Notebook | generate_example_of_2D_PDF_with_conditional_1D_PDF.ipynb | AstroPierre/Scripts-for-figures-courses-GIF-4101-GIF-7005 | a38ad6f960cc6b8155fad00e4c4562f5e459f248 | [
"BSD-2-Clause"
] | null | null | null | generate_example_of_2D_PDF_with_conditional_1D_PDF.ipynb | AstroPierre/Scripts-for-figures-courses-GIF-4101-GIF-7005 | a38ad6f960cc6b8155fad00e4c4562f5e459f248 | [
"BSD-2-Clause"
] | null | null | null | generate_example_of_2D_PDF_with_conditional_1D_PDF.ipynb | AstroPierre/Scripts-for-figures-courses-GIF-4101-GIF-7005 | a38ad6f960cc6b8155fad00e4c4562f5e459f248 | [
"BSD-2-Clause"
] | null | null | null | 261.519878 | 75,168 | 0.916239 | true | 2,060 | Qwen/Qwen-72B | 1. YES
2. YES | 0.951142 | 0.885631 | 0.842361 | __label__eng_Latn | 0.754884 | 0.795421 |
# BASIC CONTROLLERS
This notebook describes the proportional, integral, and differential controllers.
# Preliminaries
```python
!pip install -q control
!pip install -q tellurium
!pip install -q controlSBML
import control
import controlSBML as ctl
from IPython.display import HTML, Math
import numpy as np
import p... | a5b5b2661d68197a9bb5aa8266269420fea20084 | 359,444 | ipynb | Jupyter Notebook | Lecture_19_20-Basic-Controllers/Basic-Controllers.ipynb | joseph-hellerstein/advanced-controls-lectures | dc43f6c3517616da3b0ea7c93192d911414ee202 | [
"MIT"
] | null | null | null | Lecture_19_20-Basic-Controllers/Basic-Controllers.ipynb | joseph-hellerstein/advanced-controls-lectures | dc43f6c3517616da3b0ea7c93192d911414ee202 | [
"MIT"
] | null | null | null | Lecture_19_20-Basic-Controllers/Basic-Controllers.ipynb | joseph-hellerstein/advanced-controls-lectures | dc43f6c3517616da3b0ea7c93192d911414ee202 | [
"MIT"
] | null | null | null | 294.867925 | 57,168 | 0.923256 | true | 4,221 | Qwen/Qwen-72B | 1. YES
2. YES | 0.824462 | 0.819893 | 0.675971 | __label__eng_Latn | 0.535017 | 0.408838 |
# 词频、互信息、信息熵发现中文新词
**新词发现**任务是中文自然语言处理的重要步骤。**新词**有“新”就有“旧”，属于一个相对个概念，在相对的领域（金融、医疗），在相对的时间（过去、现在）都存在新词。[文本挖掘](https://zh.wikipedia.org/wiki/文本挖掘)会先将文本[分词](https://zh.wikipedia.org/wiki/中文自动分词)，而通用分词器精度不过，通常需要添加**自定义字典**补足精度，所以发现新词并加入字典，成为文本挖掘的一个重要工作。
[**单词**](https://zh.wikipedia.org/wiki/單詞)的定义，来自维基百科的定义如下：
>在语言学中，*... | 8a7ccc78cd873593fa2d60702f23b2f60ce972a4 | 11,336 | ipynb | Jupyter Notebook | docs/wordiscovery.ipynb | KunFly/new-word-discovery | ac9c15ea3b899cc279c721c1f45eaccc37cc9fb7 | [
"MIT"
] | 45 | 2018-01-04T02:43:53.000Z | 2021-12-02T11:57:55.000Z | docs/wordiscovery.ipynb | KunFly/new-word-discovery | ac9c15ea3b899cc279c721c1f45eaccc37cc9fb7 | [
"MIT"
] | 4 | 2018-01-08T03:15:27.000Z | 2020-07-24T05:48:41.000Z | docs/wordiscovery.ipynb | KunFly/new-word-discovery | ac9c15ea3b899cc279c721c1f45eaccc37cc9fb7 | [
"MIT"
] | 13 | 2018-01-04T02:43:53.000Z | 2019-12-25T09:00:17.000Z | 34.455927 | 772 | 0.533257 | true | 4,017 | Qwen/Qwen-72B | 1. YES
2. YES | 0.835484 | 0.749087 | 0.62585 | __label__yue_Hant | 0.340731 | 0.29239 |
This notebook is part of the `clifford` documentation: https://clifford.readthedocs.io/.
# Application to Robotic Manipulators
This notebook is intended to expand upon the ideas in part of the presentation [Robots, Ganja & Screw Theory](https://slides.com/hugohadfield/game2020)
## Serial manipulator
[(slides)](ht... | 972efbcd990bfccd5465fbb65f31e4556a341b6f | 20,506 | ipynb | Jupyter Notebook | docs/tutorials/cga/robotic-manipulators.ipynb | hugohadfield/clifford | 3e15da3ba429c69a5a5a641f2103d7bcca42617d | [
"BSD-3-Clause"
] | 642 | 2017-11-17T09:49:48.000Z | 2022-03-21T22:02:25.000Z | docs/tutorials/cga/robotic-manipulators.ipynb | hugohadfield/clifford | 3e15da3ba429c69a5a5a641f2103d7bcca42617d | [
"BSD-3-Clause"
] | 347 | 2017-11-17T13:57:43.000Z | 2022-01-20T09:40:15.000Z | docs/tutorials/cga/robotic-manipulators.ipynb | hugohadfield/clifford | 3e15da3ba429c69a5a5a641f2103d7bcca42617d | [
"BSD-3-Clause"
] | 61 | 2017-11-19T17:15:26.000Z | 2022-01-15T05:18:27.000Z | 35.416235 | 254 | 0.54111 | true | 4,445 | Qwen/Qwen-72B | 1. YES
2. YES | 0.841826 | 0.727975 | 0.612828 | __label__eng_Latn | 0.581948 | 0.262136 |
# Modular Strided Intervals
Fix $N \in \{1, \ldots, 2^{23} - 1\}$.
The LLVM type $\texttt{i}N$ represents $N$-bit tuples:
$\texttt{i}N := \{0, 1\}^N$
These tuples can be interpreted as elements of $\mathbb{Z}/{2^N}$ using the isomorphism $\phi_N$ together with an appropriate map of operations:
$\phi_N \colon \text... | 5cf8125e795f1a3c3209d5cdf11cb261a1b1c334 | 200,813 | ipynb | Jupyter Notebook | spec/msi.ipynb | peterrum/po-lab-2018 | e4547288c582f36bd73d94157ea157b0a631c4ae | [
"MIT"
] | 3 | 2018-06-05T08:07:52.000Z | 2018-11-04T19:18:40.000Z | spec/msi.ipynb | peterrum/po-lab-2018 | e4547288c582f36bd73d94157ea157b0a631c4ae | [
"MIT"
] | 60 | 2018-06-05T15:14:39.000Z | 2018-11-24T07:47:28.000Z | spec/msi.ipynb | peterrum/po-lab-2018 | e4547288c582f36bd73d94157ea157b0a631c4ae | [
"MIT"
] | 4 | 2018-11-05T10:04:30.000Z | 2019-04-16T14:26:24.000Z | 48.018412 | 1,989 | 0.464895 | true | 48,487 | Qwen/Qwen-72B | 1. YES
2. YES | 0.891811 | 0.805632 | 0.718472 | __label__eng_Latn | 0.4877 | 0.507582 |
## Exploring reference frame
```python
import sympy as sp
import sympy.physics.mechanics as me
```
```python
psi = me.dynamicsymbols('psi')
x0,y0 = me.dynamicsymbols('x0 y0')
x01d,y01d = me.dynamicsymbols('x0 y0',1)
u,v = me.dynamicsymbols('u v')
```
```python
N = me.ReferenceFrame('N')
B = N.orientnew('B','Axis... | 08136f05095ce02f5cb2d8e95aac43bc854248e5 | 7,522 | ipynb | Jupyter Notebook | reference_frame.ipynb | axelande/rigidbodysimulator | a87c3eb3b7978ef01efca15e66a6de6518870cd8 | [
"MIT"
] | null | null | null | reference_frame.ipynb | axelande/rigidbodysimulator | a87c3eb3b7978ef01efca15e66a6de6518870cd8 | [
"MIT"
] | 1 | 2020-10-26T19:47:02.000Z | 2020-10-26T19:47:02.000Z | reference_frame.ipynb | axelande/rigidbodysimulator | a87c3eb3b7978ef01efca15e66a6de6518870cd8 | [
"MIT"
] | 1 | 2020-10-26T09:17:00.000Z | 2020-10-26T09:17:00.000Z | 24.031949 | 522 | 0.447089 | true | 999 | Qwen/Qwen-72B | 1. YES
2. YES | 0.941654 | 0.831143 | 0.782649 | __label__yue_Hant | 0.094596 | 0.656689 |
# Lecture 18: Numerical Solutions to the Diffusion Equation
## (Implicit Methods)
### Sections
* [Introduction](#Introduction)
* [Learning Goals](#Learning-Goals)
* [On Your Own](#On-Your-Own)
* [In Class](#In-Class)
* [Revisiting the Discrete Version of Fick's Law](#Revisiting-the-Discrete-Version-of-Fick's-... | e0c317b67f4d4b5e2bf4a31fd806e0157559a533 | 29,319 | ipynb | Jupyter Notebook | Lecture-18-Implicit-Finite-Difference.ipynb | juhimgupta/MTLE-4720 | 41797715111636067dd4e2b305a782835c05619f | [
"MIT"
] | 23 | 2017-07-19T04:04:38.000Z | 2022-02-18T19:33:43.000Z | Lecture-18-Implicit-Finite-Difference.ipynb | juhimgupta/MTLE-4720 | 41797715111636067dd4e2b305a782835c05619f | [
"MIT"
] | 2 | 2019-04-08T15:21:45.000Z | 2020-03-03T20:19:00.000Z | Lecture-18-Implicit-Finite-Difference.ipynb | juhimgupta/MTLE-4720 | 41797715111636067dd4e2b305a782835c05619f | [
"MIT"
] | 11 | 2017-07-27T02:27:49.000Z | 2022-01-27T08:16:40.000Z | 28.002865 | 565 | 0.553736 | true | 4,869 | Qwen/Qwen-72B | 1. YES
2. YES | 0.863392 | 0.843895 | 0.728612 | __label__eng_Latn | 0.988507 | 0.531141 |
```python
from sympy import *
init_printing()
```
```python
eye(3)
```
```python
Matrix([[1, 2], [3, 4]]) * Matrix([[1, 2], [3, 4]])
```
```python
x = symbols('x')
(2 * x**2 + x + 10).as_poly().all_coeffs()
```
| 55300d0dbd13a5a801b603fb6e5b9b40e4132888 | 6,550 | ipynb | Jupyter Notebook | notebooks/sympy_examples.ipynb | joebentley/simba | dd1b7bc6d22ad96566898dd1851cfa210462cb00 | [
"MIT"
] | 8 | 2020-03-19T10:59:25.000Z | 2022-01-22T22:33:07.000Z | notebooks/sympy_examples.ipynb | joebentley/simba | dd1b7bc6d22ad96566898dd1851cfa210462cb00 | [
"MIT"
] | 1 | 2022-01-22T11:24:45.000Z | 2022-01-22T11:24:45.000Z | notebooks/sympy_examples.ipynb | joebentley/simba | dd1b7bc6d22ad96566898dd1851cfa210462cb00 | [
"MIT"
] | 1 | 2020-03-19T13:27:41.000Z | 2020-03-19T13:27:41.000Z | 56.956522 | 1,708 | 0.790992 | true | 84 | Qwen/Qwen-72B | 1. YES
2. YES | 0.868827 | 0.779993 | 0.677679 | __label__yue_Hant | 0.285859 | 0.412806 |
# 4. Gyakorlat - Vasúti ütközőbak
2021.03.01
## Feladat:
```python
from IPython.display import Image
Image(filename='gyak4_1.png',width=900)
```
A mellékelt ábrán látható módon egy $m$ tömegű vasúti szerlvény egy ütközőbakba csapódik $v_0$ kezdősebességgel. Feltételezzük, hogy a folyamat során a bak mozdulatlan mar... | aff05d70289b1d6df3dcedced641e30205e27bff | 145,521 | ipynb | Jupyter Notebook | negyedik_het/gyak_4.ipynb | barnabaspiri/RezgestanPython | 3fcc4374c90d041436c816d26ded63af95b44103 | [
"MIT"
] | null | null | null | negyedik_het/gyak_4.ipynb | barnabaspiri/RezgestanPython | 3fcc4374c90d041436c816d26ded63af95b44103 | [
"MIT"
] | 12 | 2021-03-29T19:12:39.000Z | 2021-04-26T18:06:02.000Z | negyedik_het/gyak_4.ipynb | barnabaspiri/RezgestanPython | 3fcc4374c90d041436c816d26ded63af95b44103 | [
"MIT"
] | 3 | 2021-03-29T19:29:08.000Z | 2021-04-10T20:58:06.000Z | 192.743046 | 93,464 | 0.902873 | true | 2,531 | Qwen/Qwen-72B | 1. YES
2. YES | 0.835484 | 0.76908 | 0.642554 | __label__hun_Latn | 0.998235 | 0.331199 |
# 微積分
## 積分
### 不定積分
微分すると $f(x)$ になるような関数を $f(x)$ の **不定積分**（または**原始関数**）と呼び、以下のような記号で表す
$$
\int{f(x)dx}
$$
$f(x)$ の不定積分の1つを $F(x)$ とすると、定数の微分が 0 であることから、任意定数 $C$ について $F(x)+C$ も $f(x)$ の不定積分になる
そのため、一般に次のように表される
$$
\int{f(x)dx} = F(x) + C
$$
このような $C$ は **積分定数** と呼ばれる
微分の性質から、不定積分について以下が成り立つ
$$
\begin{align}... | ddc0d26c1cdccbf08fbcbead5c040d912ddcfe07 | 346,807 | ipynb | Jupyter Notebook | 02_machine-learning/02-03_calculus.ipynb | amenoyoya/julia_ml-tuto | 9c0be0923ea00ca4d1d51c0c6f61f6f2748232be | [
"MIT"
] | null | null | null | 02_machine-learning/02-03_calculus.ipynb | amenoyoya/julia_ml-tuto | 9c0be0923ea00ca4d1d51c0c6f61f6f2748232be | [
"MIT"
] | null | null | null | 02_machine-learning/02-03_calculus.ipynb | amenoyoya/julia_ml-tuto | 9c0be0923ea00ca4d1d51c0c6f61f6f2748232be | [
"MIT"
] | null | null | null | 85.483609 | 12,005 | 0.755342 | true | 6,581 | Qwen/Qwen-72B | 1. YES
2. YES | 0.879147 | 0.600188 | 0.527654 | __label__yue_Hant | 0.31668 | 0.064246 |
# Playing with Sets and Probability
In this chapter, we’ll start by learning how
we can make our programs understand and
manipulate sets of numbers. We’ll then see how
sets can help us understand basic concepts in prob-
ability. Finally, we’ll learn about generating random
numbers to simulate random events. Let’s get s... | e9761c6f7297eff294a8e5dafea90f8bb395933e | 1,895 | ipynb | Jupyter Notebook | 2021/Python-Maths/chapter5.ipynb | Muramatsu2602/python-study | c81eb5d2c343817bc29b2763dcdcabed0f6a42c6 | [
"MIT"
] | null | null | null | 2021/Python-Maths/chapter5.ipynb | Muramatsu2602/python-study | c81eb5d2c343817bc29b2763dcdcabed0f6a42c6 | [
"MIT"
] | null | null | null | 2021/Python-Maths/chapter5.ipynb | Muramatsu2602/python-study | c81eb5d2c343817bc29b2763dcdcabed0f6a42c6 | [
"MIT"
] | null | null | null | 1,895 | 1,895 | 0.691293 | true | 276 | Qwen/Qwen-72B | 1. YES
2. YES | 0.937211 | 0.831143 | 0.778956 | __label__eng_Latn | 0.995067 | 0.648109 |
# Matrix Formalism of the Equations of Movement
> Renato Naville Watanabe
> [Laboratory of Biomechanics and Motor Control](http://pesquisa.ufabc.edu.br/bmclab)
> Federal University of ABC, Brazil
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#For... | e372f94fd3bec4363e13e0683e19ffe883378854 | 408,663 | ipynb | Jupyter Notebook | notebooks/MatrixFormalism.ipynb | rnwatanabe/BMC | 545c4c28125684a707641fd97f5d92f303020c47 | [
"CC-BY-4.0"
] | 1 | 2021-03-15T20:07:52.000Z | 2021-03-15T20:07:52.000Z | notebooks/.ipynb_checkpoints/MatrixFormalism-checkpoint.ipynb | guinetn/BMC | ae2d187a5fb9da0a2711a1ed56b87a3e1da0961f | [
"CC-BY-4.0"
] | null | null | null | notebooks/.ipynb_checkpoints/MatrixFormalism-checkpoint.ipynb | guinetn/BMC | ae2d187a5fb9da0a2711a1ed56b87a3e1da0961f | [
"CC-BY-4.0"
] | 1 | 2018-10-13T17:35:16.000Z | 2018-10-13T17:35:16.000Z | 201.113681 | 273,851 | 0.866558 | true | 4,060 | Qwen/Qwen-72B | 1. YES
2. YES | 0.841826 | 0.695958 | 0.585876 | __label__eng_Latn | 0.69243 | 0.199515 |
$$ \LaTeX \text{ command declarations here.}
\newcommand{\N}{\mathcal{N}}
\newcommand{\R}{\mathbb{R}}
\renewcommand{\vec}[1]{\mathbf{#1}}
\newcommand{\norm}[1]{\|#1\|_2}
\newcommand{\d}{\mathop{}\!\mathrm{d}}
\newcommand{\qed}{\qquad \mathbf{Q.E.D.}}
\newcommand{\vx}{\mathbf{x}}
\newcommand{\vy}{\mathbf{y}}
\newcommand... | 408856fd3539ddb6830f7c690986f3888070e954 | 26,814 | ipynb | Jupyter Notebook | lecture06_logistic_regression/lecture06_logistic-regression.ipynb | xipengwang/umich-eecs445-f16 | 298407af9fd417c1b6daa6127b17cb2c34c2c772 | [
"MIT"
] | 97 | 2016-09-11T23:15:35.000Z | 2022-02-22T08:03:24.000Z | lecture06_logistic_regression/lecture06_logistic-regression.ipynb | eecs445-f16/umich-eecs445-f16 | 298407af9fd417c1b6daa6127b17cb2c34c2c772 | [
"MIT"
] | null | null | null | lecture06_logistic_regression/lecture06_logistic-regression.ipynb | eecs445-f16/umich-eecs445-f16 | 298407af9fd417c1b6daa6127b17cb2c34c2c772 | [
"MIT"
] | 77 | 2016-09-12T20:50:46.000Z | 2022-01-03T14:41:23.000Z | 33.601504 | 545 | 0.470351 | true | 5,896 | Qwen/Qwen-72B | 1. YES
2. YES | 0.808067 | 0.647798 | 0.523464 | __label__eng_Latn | 0.637619 | 0.054513 |
## Week 3 MA544
---
**Objectives and Plan**
1. Pseudo-inverse (Perron-Frobenius inverse) Properties
1. Linear Systems of Equations and Gaussian Elimination with pivoting
1. LU Decomposition of A
1. QR Decomposition of a matrix
1. Iterative solution of Linear Systems
```python
#IMPORT
import numpy as np
import matpl... | cfa70841a5d6f982f2bdcad37ec67c89733d9e1d | 11,208 | ipynb | Jupyter Notebook | course_notes/MA544 Share/NB3 MA544.ipynb | jschmidtnj/ma544-final-project | 61fb57d344ad4f693eb697015ed926988402186f | [
"MIT"
] | 2 | 2021-03-23T01:48:51.000Z | 2022-02-01T22:49:47.000Z | course_notes/MA544 Share/NB3 MA544.ipynb | jschmidtnj/ma544-final-project | 61fb57d344ad4f693eb697015ed926988402186f | [
"MIT"
] | null | null | null | course_notes/MA544 Share/NB3 MA544.ipynb | jschmidtnj/ma544-final-project | 61fb57d344ad4f693eb697015ed926988402186f | [
"MIT"
] | 1 | 2021-05-05T01:35:11.000Z | 2021-05-05T01:35:11.000Z | 30.622951 | 154 | 0.461367 | true | 2,332 | Qwen/Qwen-72B | 1. YES
2. YES | 0.815232 | 0.824462 | 0.672128 | __label__eng_Latn | 0.910279 | 0.39991 |
```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("... | de83b15b3fe4b129cb368659f73239a39a9d2d9b | 685,590 | ipynb | Jupyter Notebook | 03.2 Evaluation Metrics.ipynb | bagustris/advanced_training | 9b96ecc0bb6c913d12cd33b51cfe6ba80a5a58b0 | [
"BSD-2-Clause"
] | 132 | 2016-06-06T17:30:23.000Z | 2021-11-16T13:51:36.000Z | 03.2 Evaluation Metrics.ipynb | afcarl/advanced_training | 1ef9246e2f70b82295bb3c4dc9a283e32fd427fb | [
"BSD-2-Clause"
] | 1 | 2017-03-08T19:49:13.000Z | 2017-03-08T19:55:03.000Z | 03.2 Evaluation Metrics.ipynb | afcarl/advanced_training | 1ef9246e2f70b82295bb3c4dc9a283e32fd427fb | [
"BSD-2-Clause"
] | 47 | 2016-06-07T09:39:22.000Z | 2021-09-01T01:45:44.000Z | 94.096898 | 182,361 | 0.748599 | true | 5,227 | Qwen/Qwen-72B | 1. YES
2. YES | 0.826712 | 0.83762 | 0.69247 | __label__eng_Latn | 0.339803 | 0.447172 |
# Numerical Integration
#### Preliminaries
We have to import the array library `numpy` and the plotting library `matplotlib.pyplot`, note that we define shorter aliases for these.
Next we import from `numpy` some of the functions that we will use more frequently and from an utility library functions to format convenie... | cff4c799de5df2b5a1c2d3fdbed691ac91776afd | 430,156 | ipynb | Jupyter Notebook | dati_2015/ha03/03_Numerical_Integration.ipynb | shishitao/boffi_dynamics | 365f16d047fb2dbfc21a2874790f8bef563e0947 | [
"MIT"
] | null | null | null | dati_2015/ha03/03_Numerical_Integration.ipynb | shishitao/boffi_dynamics | 365f16d047fb2dbfc21a2874790f8bef563e0947 | [
"MIT"
] | null | null | null | dati_2015/ha03/03_Numerical_Integration.ipynb | shishitao/boffi_dynamics | 365f16d047fb2dbfc21a2874790f8bef563e0947 | [
"MIT"
] | 2 | 2019-06-23T12:32:39.000Z | 2021-08-15T18:33:55.000Z | 500.181395 | 77,539 | 0.92503 | true | 5,127 | Qwen/Qwen-72B | 1. YES
2. YES | 0.934395 | 0.849971 | 0.794209 | __label__eng_Latn | 0.480749 | 0.683546 |
$\newcommand{\Normal}{\mathcal{N}}
\newcommand{\lp}{\left(}
\newcommand{\rp}{\right)}
\newcommand{\lf}{\left\{}
\newcommand{\rf}{\right\}}
\newcommand{\ls}{\left[}
\newcommand{\rs}{\right]}
\newcommand{\lv}{\left|}
\newcommand{\rv}{\right|}
\newcommand{\state}{x}
\newcommand{\State}{\boldx}
\newcommand{\StateR}{\boldX}... | b94019c1d35568539fa92dbab7366c090f599ab9 | 164,006 | ipynb | Jupyter Notebook | road situation analysis/research/road/state estimation/kalman_filter_demo.ipynb | MikhailKitikov/DrivingMonitor | 0b698d1ba644ce74e1c7d88c3e18a1ef997aabc0 | [
"MIT"
] | null | null | null | road situation analysis/research/road/state estimation/kalman_filter_demo.ipynb | MikhailKitikov/DrivingMonitor | 0b698d1ba644ce74e1c7d88c3e18a1ef997aabc0 | [
"MIT"
] | null | null | null | road situation analysis/research/road/state estimation/kalman_filter_demo.ipynb | MikhailKitikov/DrivingMonitor | 0b698d1ba644ce74e1c7d88c3e18a1ef997aabc0 | [
"MIT"
] | null | null | null | 258.684543 | 62,916 | 0.856731 | true | 5,311 | Qwen/Qwen-72B | 1. YES
2. YES | 0.851953 | 0.808067 | 0.688435 | __label__kor_Hang | 0.090227 | 0.437797 |
# Tarea 1
_Tarea 1_ de _Benjamín Rivera_ para el curso de __Métodos Numéricos__ impartido por _Joaquín Peña Acevedo_. Fecha limite de entrega __6 de Septiembre de 2020__.
## Como ejecutar
#### Requerimientos
Este programa se ejecuto en mi computadora con la version de __Python 3.8.2__ y con estos
[requerimientos](... | a028084c370652fc96246bfbb566de2081d01990 | 27,373 | ipynb | Jupyter Notebook | MN/Tareas/T1/Tarea1.ipynb | BenchHPZ/UG-Compu | fa3551a862ee04b59a5ba97a791f39a77ce2df60 | [
"MIT"
] | null | null | null | MN/Tareas/T1/Tarea1.ipynb | BenchHPZ/UG-Compu | fa3551a862ee04b59a5ba97a791f39a77ce2df60 | [
"MIT"
] | null | null | null | MN/Tareas/T1/Tarea1.ipynb | BenchHPZ/UG-Compu | fa3551a862ee04b59a5ba97a791f39a77ce2df60 | [
"MIT"
] | null | null | null | 34.693283 | 377 | 0.542725 | true | 6,039 | Qwen/Qwen-72B | 1. YES
2. YES | 0.705785 | 0.890294 | 0.628356 | __label__spa_Latn | 0.887242 | 0.298213 |
# Demo - LISA Horizon Distance
This demo shows how to use ``LEGWORK`` to compute the horizon distance for a collection of sources.
```python
%matplotlib inline
```
```python
import legwork as lw
import numpy as np
import astropy.units as u
import matplotlib.pyplot as plt
```
```python
%config InlineBackend.figure... | 81209efd8039bee79ea9424a0e98810a5ee18235 | 503,543 | ipynb | Jupyter Notebook | docs/demos/HorizonDistance.ipynb | arfon/LEGWORK | 91ca299d00ed6892acdf5980f33826421fa348ef | [
"MIT"
] | 14 | 2021-09-28T21:53:24.000Z | 2022-02-05T14:29:44.000Z | docs/demos/HorizonDistance.ipynb | arfon/LEGWORK | 91ca299d00ed6892acdf5980f33826421fa348ef | [
"MIT"
] | 44 | 2021-10-31T15:04:26.000Z | 2022-03-15T19:01:40.000Z | docs/demos/HorizonDistance.ipynb | arfon/LEGWORK | 91ca299d00ed6892acdf5980f33826421fa348ef | [
"MIT"
] | 4 | 2021-11-18T09:20:53.000Z | 2022-03-16T11:30:44.000Z | 1,379.569863 | 491,724 | 0.955205 | true | 2,478 | Qwen/Qwen-72B | 1. YES
2. YES | 0.824462 | 0.721743 | 0.59505 | __label__eng_Latn | 0.807482 | 0.22083 |
# Week 5 worksheet 1: Introduction to numerical integration
This notebook is modified from one created by Charlotte Desvages.
This week, we investigate numerical methods to estimate integrals.
The best way to learn programming is to write code. Don't hesitate to edit the code in the example cells, or add your own co... | 63449f7a6ab230cb1d6d7c60bf9fe3fa6c9897cb | 12,753 | ipynb | Jupyter Notebook | Workshops/W05-W1_NMfCE_Numerical_integration.ipynb | DrFriedrich/nmfce-2021-22 | 2ccee5a97b24bd5c1e80e531957240ffb7163897 | [
"MIT"
] | null | null | null | Workshops/W05-W1_NMfCE_Numerical_integration.ipynb | DrFriedrich/nmfce-2021-22 | 2ccee5a97b24bd5c1e80e531957240ffb7163897 | [
"MIT"
] | null | null | null | Workshops/W05-W1_NMfCE_Numerical_integration.ipynb | DrFriedrich/nmfce-2021-22 | 2ccee5a97b24bd5c1e80e531957240ffb7163897 | [
"MIT"
] | null | null | null | 31.488889 | 388 | 0.549204 | true | 2,506 | Qwen/Qwen-72B | 1. YES
2. YES | 0.803174 | 0.839734 | 0.674452 | __label__eng_Latn | 0.973284 | 0.40531 |
# A demonstration of SuSiE's motivations
This document explains with toy example illustration the unique type of inference SuSiE is interested in.
## The inference problem
We assume our audience are familiar or interested in large scale regression. Similar to eg LASSO, SuSiE is a method for variable selection in lar... | 688d3f536259d7265d26318da7a5622644bb2bb7 | 57,029 | ipynb | Jupyter Notebook | manuscript_results/motivating_example.ipynb | llgai508/susie-paper | 7633d734b5c02ae9f102d7d7c0e4d249a007afd9 | [
"MIT"
] | null | null | null | manuscript_results/motivating_example.ipynb | llgai508/susie-paper | 7633d734b5c02ae9f102d7d7c0e4d249a007afd9 | [
"MIT"
] | null | null | null | manuscript_results/motivating_example.ipynb | llgai508/susie-paper | 7633d734b5c02ae9f102d7d7c0e4d249a007afd9 | [
"MIT"
] | null | null | null | 66.467366 | 7,393 | 0.769258 | true | 3,452 | Qwen/Qwen-72B | 1. YES
2. YES | 0.795658 | 0.746139 | 0.593672 | __label__eng_Latn | 0.871123 | 0.217628 |
<p>
<div align="right">
Massimo Nocentini<br>
<small>
<br>March and April 2018: cleanup
<br>November 2016: splitting from "big" notebook
</small>
</div>
</p>
<br>
<br>
<div align="center">
<b>Abstract</b><br>
Theory of matrix functions, with applications to Pascal array $\mathcal{P}$.
</div>
```python
from sympy imp... | 078383e36e9b9326334bfa1553e6ef758e140bfb | 514,402 | ipynb | Jupyter Notebook | notes/matrices-functions/pascal-riordan-array.ipynb | massimo-nocentini/simulation-methods | f7578a9719b1a22e5a25a8de85cc229aef4c259d | [
"MIT"
] | null | null | null | notes/matrices-functions/pascal-riordan-array.ipynb | massimo-nocentini/simulation-methods | f7578a9719b1a22e5a25a8de85cc229aef4c259d | [
"MIT"
] | null | null | null | notes/matrices-functions/pascal-riordan-array.ipynb | massimo-nocentini/simulation-methods | f7578a9719b1a22e5a25a8de85cc229aef4c259d | [
"MIT"
] | null | null | null | 196.561712 | 37,800 | 0.774643 | true | 1,801 | Qwen/Qwen-72B | 1. YES
2. YES | 0.843895 | 0.76908 | 0.649023 | __label__eng_Latn | 0.251207 | 0.346229 |
.. meta::
:description: A guide which introduces the most important steps to get started with pymoo, an open-source multi-objective optimization framework in Python.
.. meta::
:keywords: Multi-Criteria Decision Making, Multi-objective Optimization, Python, Evolutionary Computation, Optimization Test Problem
``... | a05d8e43642e0f7dcb039450379f305c832f4e33 | 221,749 | ipynb | Jupyter Notebook | source/getting_started/part_3.ipynb | SunTzunami/pymoo-doc | f82d8908fe60792d49a7684c4bfba4a6c1339daf | [
"Apache-2.0"
] | 2 | 2021-09-11T06:43:49.000Z | 2021-11-10T13:36:09.000Z | source/getting_started/part_3.ipynb | SunTzunami/pymoo-doc | f82d8908fe60792d49a7684c4bfba4a6c1339daf | [
"Apache-2.0"
] | 3 | 2021-09-21T14:04:47.000Z | 2022-03-07T13:46:09.000Z | source/getting_started/part_3.ipynb | SunTzunami/pymoo-doc | f82d8908fe60792d49a7684c4bfba4a6c1339daf | [
"Apache-2.0"
] | 3 | 2021-10-09T02:47:26.000Z | 2022-02-10T07:02:37.000Z | 416.0394 | 52,936 | 0.935319 | true | 1,588 | Qwen/Qwen-72B | 1. YES
2. YES | 0.888759 | 0.851953 | 0.757181 | __label__eng_Latn | 0.989204 | 0.597516 |
# SIRD: A Epidemic Model with Social Distancing
**Prof. Tony Saad (<a href='www.tsaad.net'>www.tsaad.net</a>) <br/>Department of Chemical Engineering <br/>University of Utah**
<hr/>
```python
#HIDDEN
from routines import plot_sird_model
import ipywidgets as widgets
from ipywidgets import interact, interact_manual
%ma... | f0cb7e3eeb8df2424cb013c86eed846fec54f5e4 | 8,660 | ipynb | Jupyter Notebook | SIRD.ipynb | saadtony/SIRD | 87888445826aa9db0e386cf23f0f262bb4f9ab55 | [
"MIT"
] | null | null | null | SIRD.ipynb | saadtony/SIRD | 87888445826aa9db0e386cf23f0f262bb4f9ab55 | [
"MIT"
] | null | null | null | SIRD.ipynb | saadtony/SIRD | 87888445826aa9db0e386cf23f0f262bb4f9ab55 | [
"MIT"
] | null | null | null | 39.907834 | 390 | 0.618245 | true | 1,514 | Qwen/Qwen-72B | 1. YES
2. YES | 0.888759 | 0.815232 | 0.724545 | __label__eng_Latn | 0.758744 | 0.521693 |
# Eq. (4.17) and (4.18)
Equation (4.17) is
\begin{equation}
\overline{\phi}^{(k,\alpha,\beta)}_m = \gamma^{(k,\alpha,\beta)}_{m} \phi^{(k/2, \alpha+{k}/{2}, \beta+{k}/{2})}_{m}, \label{eq:phiover}
\end{equation}
where
\begin{equation}
\gamma^{(k,\alpha,\beta)}_{n} = \frac{\psi^{(k/2,\alpha,\beta)}_{n+k} g^{(\a... | d63fca795a73562fb2a6c02372de916232f1996e | 7,823 | ipynb | Jupyter Notebook | binder/Equations (4.17-4.18).ipynb | spectralDNS/PG-paper-2022 | 0bfa82e1ff77a5bb9a6ec5be930b4c00b449a1e0 | [
"BSD-2-Clause"
] | null | null | null | binder/Equations (4.17-4.18).ipynb | spectralDNS/PG-paper-2022 | 0bfa82e1ff77a5bb9a6ec5be930b4c00b449a1e0 | [
"BSD-2-Clause"
] | null | null | null | binder/Equations (4.17-4.18).ipynb | spectralDNS/PG-paper-2022 | 0bfa82e1ff77a5bb9a6ec5be930b4c00b449a1e0 | [
"BSD-2-Clause"
] | null | null | null | 36.556075 | 396 | 0.471303 | true | 2,104 | Qwen/Qwen-72B | 1. YES
2. YES | 0.79053 | 0.76908 | 0.607981 | __label__eng_Latn | 0.281736 | 0.250874 |
# Workshop 12: Introduction to Numerical ODE Solutions
*Source: http://phys.csuchico.edu/ayars/312 *
**Submit this notebook to bCourses to receive a grade for this Workshop.**
Please complete workshop activities in code cells in this iPython notebook. The activities titled **Practice** are purely for you to explore ... | cd093ba0a69615acb938450dcdf89979fa8eb845 | 142,995 | ipynb | Jupyter Notebook | Fall2020_DeCal_Material/Resources/Workshop12_solutions.ipynb | emilyma53/Python_DeCal | 1b98351ecd16f93a5357c9e00af18dde82c813b1 | [
"MIT"
] | 2 | 2021-02-01T22:53:16.000Z | 2022-02-18T19:04:52.000Z | Fall_2020_DeCal_Material/Resources/Workshop12_solutions.ipynb | James11222/Python_DeCal_2020 | 7e7d28bce2248812446ef2e2e141230308b318c4 | [
"MIT"
] | null | null | null | Fall_2020_DeCal_Material/Resources/Workshop12_solutions.ipynb | James11222/Python_DeCal_2020 | 7e7d28bce2248812446ef2e2e141230308b318c4 | [
"MIT"
] | 1 | 2021-09-30T23:10:25.000Z | 2021-09-30T23:10:25.000Z | 328.724138 | 64,980 | 0.92483 | true | 2,720 | Qwen/Qwen-72B | 1. YES
2. YES | 0.859664 | 0.859664 | 0.739022 | __label__eng_Latn | 0.945979 | 0.555327 |
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('classic')
%matplotlib inline
```
# Class 11: Introduction to Dynamic Optimization: A Two-Period Cake-Eating Problem
Dynamic optimization, the optimal choice over elements in a time series, is at the heart of macroeconomic ... | f429bfb77666f943eca34902a5d478a3cdb68524 | 12,830 | ipynb | Jupyter Notebook | Lecture Notebooks/Econ126_Class_11_blank.ipynb | t-hdd/econ126 | 17029937bd6c40e606d145f8d530728585c30a1d | [
"MIT"
] | null | null | null | Lecture Notebooks/Econ126_Class_11_blank.ipynb | t-hdd/econ126 | 17029937bd6c40e606d145f8d530728585c30a1d | [
"MIT"
] | null | null | null | Lecture Notebooks/Econ126_Class_11_blank.ipynb | t-hdd/econ126 | 17029937bd6c40e606d145f8d530728585c30a1d | [
"MIT"
] | null | null | null | 40.473186 | 580 | 0.413952 | true | 1,700 | Qwen/Qwen-72B | 1. YES
2. YES | 0.917303 | 0.83762 | 0.768351 | __label__eng_Latn | 0.993864 | 0.623469 |
# Review of Basics of Linear Algebra
---
**Agenda**
>1. Matrix Vector Operations using NumPy
>1. Vector Spaces and Matrices: Four fundamental fubspaces
>1. Motivating Examples: Image and text manipulations
>1. Eigen-decomposition, determinant and trace
>1. Special Matrices: Orthogonal Matrices
>1. Norms
>
```python
... | 8be5d78b0f2ba1a9f5853cfbcf145f7f7fedb4f2 | 430,902 | ipynb | Jupyter Notebook | course_notes/MA544 Share/NB1 MA544 updated.ipynb | jschmidtnj/ma544-final-project | 61fb57d344ad4f693eb697015ed926988402186f | [
"MIT"
] | 2 | 2021-03-23T01:48:51.000Z | 2022-02-01T22:49:47.000Z | course_notes/MA544 Share/NB1 MA544 updated.ipynb | jschmidtnj/ma544-final-project | 61fb57d344ad4f693eb697015ed926988402186f | [
"MIT"
] | null | null | null | course_notes/MA544 Share/NB1 MA544 updated.ipynb | jschmidtnj/ma544-final-project | 61fb57d344ad4f693eb697015ed926988402186f | [
"MIT"
] | 1 | 2021-05-05T01:35:11.000Z | 2021-05-05T01:35:11.000Z | 287.45964 | 128,512 | 0.91483 | true | 10,689 | Qwen/Qwen-72B | 1. YES
2. YES | 0.909907 | 0.885631 | 0.805842 | __label__eng_Latn | 0.800774 | 0.710574 |
# Programovanie
Letná škola FKS 2018
Maťo Gažo, Fero Dráček
(& vykradnuté materiály od Mateja Badina, Feriho Hermana, Kuba, Peťa, Jarných škôl FX a kade-tade po internete)
V tomto kurze si ukážeme základy programovania a naučíme sa programovať matematiku a fyziku.
Takéto vedomosti sú skvelé a budete vďaka nim:
* ve... | 9da35c83665e7c910b8dde4550a4c612a5e68ca1 | 62,337 | ipynb | Jupyter Notebook | Programko.ipynb | matoga/LetnaSkolaFKS_notebooks | 26faa2d30ee942e18246fe466d9bf42f16cc1433 | [
"MIT"
] | null | null | null | Programko.ipynb | matoga/LetnaSkolaFKS_notebooks | 26faa2d30ee942e18246fe466d9bf42f16cc1433 | [
"MIT"
] | null | null | null | Programko.ipynb | matoga/LetnaSkolaFKS_notebooks | 26faa2d30ee942e18246fe466d9bf42f16cc1433 | [
"MIT"
] | null | null | null | 20.40491 | 376 | 0.474181 | true | 10,834 | Qwen/Qwen-72B | 1. YES
2. YES | 0.928409 | 0.907312 | 0.842357 | __label__slk_Latn | 0.989819 | 0.79541 |
## 高斯判别模型(GDA)
高斯判别模型作分类,假设有两类,则:
\begin{equation}
y \sim Bernouli(\phi) \\
x|y=0 \sim N(\mu_0, \Sigma) \\
x|y=1 \sim N(\mu_1, \Sigma)
\end{equation}
令$\theta = (\phi, \mu_0, \mu_1, \Sigma)$,则参数的似然估计为:
\begin{align*}
L(\theta) &= log(\prod_{i=1}^m P(x^{(i)},y^{(i)})) \\
&= log(\prod_{i=1}^m P(x^{(i)}|y^... | 0d0df6fbc2b8cff7b3dbe99f4a0bc1949ce35279 | 84,817 | ipynb | Jupyter Notebook | ML/GMM/GDA.ipynb | tianqichongzhen/ProgramPrac | 5e575f394179709a4964483308b91796c341e45f | [
"Apache-2.0"
] | 2 | 2019-01-12T13:54:52.000Z | 2021-09-13T12:47:25.000Z | ML/GMM/GDA.ipynb | Johnwei386/Warehouse | 5e575f394179709a4964483308b91796c341e45f | [
"Apache-2.0"
] | null | null | null | ML/GMM/GDA.ipynb | Johnwei386/Warehouse | 5e575f394179709a4964483308b91796c341e45f | [
"Apache-2.0"
] | null | null | null | 253.943114 | 18,120 | 0.9018 | true | 2,574 | Qwen/Qwen-72B | 1. YES
2. YES | 0.927363 | 0.7773 | 0.720839 | __label__eng_Latn | 0.070863 | 0.513083 |
# Upper envelope
This notebook shows how to use the **upperenvelope** module from the **consav** package.
# Model
Consider a **standard consumption-saving** model
\begin{align}
v_{t}(m_{t})&=\max_{c_{t}}\frac{c_{t}^{1-\rho}}{1-\rho}+\beta v_{t+1}(m_{t+1})
\end{align}
where
\begin{align}
a_{t} &=m_{t}-c_{t} \\
m_... | d149f393fcbb87ec686d6fceac19abdd9f862507 | 81,699 | ipynb | Jupyter Notebook | Tools/Upper envelope.ipynb | ThomasHJorgensen/ConsumptionSavingNotebooks | badbdfb1da226d5494026de2adcfec171c7f40ea | [
"MIT"
] | 1 | 2021-11-07T23:37:25.000Z | 2021-11-07T23:37:25.000Z | Tools/Upper envelope.ipynb | ThomasHJorgensen/ConsumptionSavingNotebooks | badbdfb1da226d5494026de2adcfec171c7f40ea | [
"MIT"
] | null | null | null | Tools/Upper envelope.ipynb | ThomasHJorgensen/ConsumptionSavingNotebooks | badbdfb1da226d5494026de2adcfec171c7f40ea | [
"MIT"
] | null | null | null | 151.575139 | 13,028 | 0.886192 | true | 2,218 | Qwen/Qwen-72B | 1. YES
2. YES | 0.851953 | 0.824462 | 0.702403 | __label__eng_Latn | 0.404823 | 0.470248 |
**Problem 1** (7 pts)
In the finite space all norms are equivalent.
This means that given any two norms $\|\cdot\|_*$ and $\|\cdot\|_{**}$ over $\mathbb{C}^{n\times 1}$, inequality
$$
c_1 \Vert x \Vert_* \leq \Vert x \Vert_{**} \leq c_2 \Vert x \Vert_*
$$
holds for every $x\in \mathbb{C}^{n\times 1}$ for some con... | 6057a7498fa78bf10bd942e04e6f5246623f2db3 | 5,309 | ipynb | Jupyter Notebook | problems/Pset2.ipynb | oseledets/NLA | d16d47bc8e20df478d98b724a591d33d734ec74b | [
"MIT"
] | 14 | 2015-01-20T13:24:38.000Z | 2022-02-03T05:54:09.000Z | problems/Pset2.ipynb | oseledets/NLA | d16d47bc8e20df478d98b724a591d33d734ec74b | [
"MIT"
] | null | null | null | problems/Pset2.ipynb | oseledets/NLA | d16d47bc8e20df478d98b724a591d33d734ec74b | [
"MIT"
] | 4 | 2015-09-10T09:14:10.000Z | 2019-10-09T04:36:07.000Z | 40.219697 | 281 | 0.530797 | true | 1,183 | Qwen/Qwen-72B | 1. YES
2. YES | 0.92944 | 0.912436 | 0.848055 | __label__eng_Latn | 0.923686 | 0.808649 |
# AutoDiff by Symboic Representation in Julia
```julia
using Symbolics
```
```julia
i(x) = x
f(x) = 3x^2
g(x) = 2x^2
h(x) = x^2
w_vec = [i, h, g, f]
@variables x
```
\begin{equation}
\left[
\begin{array}{c}
x \\
\end{array}
\right]
\end{equation}
```julia
function forward_fn(w_vec, x, i::Int)
y = w_v... | 3a1b7ff4c7fffa8397f2aaa76972e1f16fb89f52 | 10,755 | ipynb | Jupyter Notebook | diffprog/julia_dp/autodiff_chain_rule-symb.ipynb | jskDr/keraspp_2021 | dc46ebb4f4dea48612135136c9837da7c246534a | [
"MIT"
] | 4 | 2021-09-21T15:35:04.000Z | 2021-12-14T12:14:44.000Z | diffprog/julia_dp/autodiff_chain_rule-symb.ipynb | jskDr/keraspp_2021 | dc46ebb4f4dea48612135136c9837da7c246534a | [
"MIT"
] | null | null | null | diffprog/julia_dp/autodiff_chain_rule-symb.ipynb | jskDr/keraspp_2021 | dc46ebb4f4dea48612135136c9837da7c246534a | [
"MIT"
] | null | null | null | 19.378378 | 68 | 0.406509 | true | 1,211 | Qwen/Qwen-72B | 1. YES
2. YES | 0.938124 | 0.885631 | 0.830832 | __label__yue_Hant | 0.239012 | 0.768634 |
```
from sympy import *
from ga import Ga
from printer import Format, Fmt
Format()
```
```
xyz_coords = (x, y, z) = symbols('x y z', real=True)
(o3d, ex, ey, ez) = Ga.build('e', g=[1, 1, 1], coords=xyz_coords, norm=True)
```
```
f = o3d.mv('f', 'scalar', f=True)
F = o3d.mv('F', 'vector', f=True)
lap = o3d.grad*o3d.... | fab731e58e557fc7f87f54d3d2fc968aef5f7e12 | 14,158 | ipynb | Jupyter Notebook | examples/ipython/dop.ipynb | moble/galgebra | f77305eae1366eb2bb6e5e5c47b9788e22bd46e8 | [
"BSD-3-Clause"
] | 1 | 2018-03-06T15:00:36.000Z | 2018-03-06T15:00:36.000Z | examples/ipython/dop.ipynb | rschwiebert/galgebra | 852f6d2574780718ef327f172cd1b8fa7d0a9879 | [
"BSD-3-Clause"
] | null | null | null | examples/ipython/dop.ipynb | rschwiebert/galgebra | 852f6d2574780718ef327f172cd1b8fa7d0a9879 | [
"BSD-3-Clause"
] | 1 | 2018-12-04T02:06:14.000Z | 2018-12-04T02:06:14.000Z | 35.306733 | 573 | 0.439116 | true | 2,017 | Qwen/Qwen-72B | 1. YES
2. YES | 0.939913 | 0.831143 | 0.781202 | __label__eng_Latn | 0.159955 | 0.653327 |
## Graphical Models
```python
import graphviz as gz
import numpy as np
```
GMs are depictions of independence/dependence relationships for distributions. All GMs have their strengths and weaknesses. Belief networks is one type of a GM, they are useful to represent ancestral conditional independence; however, they ca... | 9baab462b46ce5f64a6a805251de67f205235287 | 70,344 | ipynb | Jupyter Notebook | BRML/notebooks/chapter4.ipynb | eozd/brml-notes | 46a14ae7ea22e9786a750b99293bea70c8a11af9 | [
"MIT"
] | null | null | null | BRML/notebooks/chapter4.ipynb | eozd/brml-notes | 46a14ae7ea22e9786a750b99293bea70c8a11af9 | [
"MIT"
] | null | null | null | BRML/notebooks/chapter4.ipynb | eozd/brml-notes | 46a14ae7ea22e9786a750b99293bea70c8a11af9 | [
"MIT"
] | 1 | 2020-03-23T00:44:06.000Z | 2020-03-23T00:44:06.000Z | 45.94644 | 329 | 0.499673 | true | 4,125 | Qwen/Qwen-72B | 1. YES
2. YES | 0.824462 | 0.894789 | 0.73772 | __label__eng_Latn | 0.991746 | 0.552302 |
# Quantum phase estimation
Your task in this notebook is to implement the quantum Fourier transform on a set of 3 qubits in Qiskit, and then use it to estimate the eigenvalue of a simple Hamiltonian.
### Part A: Implementing the QFT
```python
import numpy as np
from qiskit import QuantumRegister, ClassicalRegister... | 84ff879c13ccd4909b689b4e7092630655f5a77f | 7,870 | ipynb | Jupyter Notebook | 02-gate-model-applications/notebooks/Solved-Quantum-Phase-Estimation.ipynb | a-capra/Intro-QC-TRIUMF | 9738e6a49f226367247cf7bc05a00751f7bf2fe5 | [
"MIT"
] | 27 | 2019-05-09T17:40:20.000Z | 2021-12-15T12:23:17.000Z | 02-gate-model-applications/notebooks/Solved-Quantum-Phase-Estimation.ipynb | a-capra/Intro-QC-TRIUMF | 9738e6a49f226367247cf7bc05a00751f7bf2fe5 | [
"MIT"
] | 1 | 2021-09-29T07:34:09.000Z | 2021-09-29T21:01:29.000Z | 02-gate-model-applications/notebooks/Solved-Quantum-Phase-Estimation.ipynb | a-capra/Intro-QC-TRIUMF | 9738e6a49f226367247cf7bc05a00751f7bf2fe5 | [
"MIT"
] | 14 | 2019-05-09T18:45:49.000Z | 2021-12-15T12:23:21.000Z | 29.365672 | 381 | 0.57014 | true | 1,435 | Qwen/Qwen-72B | 1. YES
2. YES | 0.938124 | 0.853913 | 0.801076 | __label__eng_Latn | 0.952179 | 0.699501 |
# Subsampling approaches to MCMC for tall data
Last modified on 11th May 2015
This notebook illustrates various approaches to subsampling MCMC, see (Bardenet, Doucet, and Holmes, ICML'14 and a 2015 arxiv preprint entitled "On MCMC for tall data" by the same authors. By default, executing cells from top to bottom will... | 52d6666ca08c614505d605d694006c87fa7e95cd | 368,581 | ipynb | Jupyter Notebook | .ipynb_checkpoints/examples-checkpoint.ipynb | rbardenet/rbardenet.github.io | 48168e60ffa12c05c4866db3691b6b7a968841ad | [
"MIT"
] | null | null | null | .ipynb_checkpoints/examples-checkpoint.ipynb | rbardenet/rbardenet.github.io | 48168e60ffa12c05c4866db3691b6b7a968841ad | [
"MIT"
] | 1 | 2020-04-29T22:46:33.000Z | 2020-04-29T22:46:33.000Z | .ipynb_checkpoints/examples-checkpoint.ipynb | rbardenet/rbardenet.github.io | 48168e60ffa12c05c4866db3691b6b7a968841ad | [
"MIT"
] | null | null | null | 227.941249 | 230,972 | 0.881432 | true | 11,898 | Qwen/Qwen-72B | 1. YES
2. YES | 0.675765 | 0.782662 | 0.528896 | __label__eng_Latn | 0.545017 | 0.067131 |
## Simple qubit rotation old version of TFQ, with manual GD
In this jupyter file we define a variational quantum circuit $V(\theta)$ that rotates an initial state $|0000\rangle$ into a target state with equal superposition $\sum_{\sigma_i} | \sigma_i \rangle$. The aim is that $\langle 1111 | V(\theta) | 0000\rangle =... | dd75b978161b0a1d3bc4f8f7277843e923bd5c84 | 17,354 | ipynb | Jupyter Notebook | Simple_qubit_rotation_TFQ_with_manual_GD.ipynb | PatrickHuembeli/Pennaylane_and_TFQ | bbee7f2ddaa0f5d4c7eb1768164663bc7c985327 | [
"Apache-2.0"
] | 5 | 2020-03-18T05:31:51.000Z | 2020-09-03T22:43:36.000Z | Simple_qubit_rotation_TFQ_with_manual_GD.ipynb | PatrickHuembeli/Pennaylane_and_TFQ | bbee7f2ddaa0f5d4c7eb1768164663bc7c985327 | [
"Apache-2.0"
] | null | null | null | Simple_qubit_rotation_TFQ_with_manual_GD.ipynb | PatrickHuembeli/Pennaylane_and_TFQ | bbee7f2ddaa0f5d4c7eb1768164663bc7c985327 | [
"Apache-2.0"
] | 2 | 2020-03-17T09:10:21.000Z | 2020-07-30T16:08:47.000Z | 65.486792 | 8,993 | 0.599285 | true | 1,200 | Qwen/Qwen-72B | 1. YES
2. YES | 0.845942 | 0.798187 | 0.67522 | __label__eng_Latn | 0.975151 | 0.407094 |
```python
from typing import List, Dict
from sympy import Point, Point2D, Segment
import matplotlib.pyplot as plt
from collections import defaultdict
from itertools import combinations
import numpy as np
# Load data
txt_lines = open("input.txt").read().splitlines()
```
# Visualization
Horizontal and vertical lines:
... | f3213c7aa014ae9d8f1c0341b017399b7addac04 | 250,465 | ipynb | Jupyter Notebook | day_05/task.ipynb | codebude/aoc-2021 | 8d0867ee5d710bfb7b8f4cccf5de59f8889bbade | [
"MIT"
] | null | null | null | day_05/task.ipynb | codebude/aoc-2021 | 8d0867ee5d710bfb7b8f4cccf5de59f8889bbade | [
"MIT"
] | null | null | null | day_05/task.ipynb | codebude/aoc-2021 | 8d0867ee5d710bfb7b8f4cccf5de59f8889bbade | [
"MIT"
] | null | null | null | 1,098.530702 | 187,126 | 0.955551 | true | 1,133 | Qwen/Qwen-72B | 1. YES
2. YES | 0.879147 | 0.727975 | 0.639997 | __label__eng_Latn | 0.44332 | 0.325259 |
# Iterative Solvers 4 - Preconditioning
## The basic idea
For both the GMRES method and CG we have seen that the eigenvalue distribution is crucial for fast convergence. In both cases we would like the eigenvalues of the matrix be clustered close together and be well separated from zero. Unfortunately, in many applic... | 1c4c69858aadf50d15b5f81d4fb2ced09ee2d9ed | 45,692 | ipynb | Jupyter Notebook | hpc_lecture_notes/it_solvers4.ipynb | tbetcke/hpc_lecture_notes | f061401a54ef467c8f8d0fb90294d63d83e3a9e1 | [
"BSD-3-Clause"
] | 3 | 2020-10-02T11:11:58.000Z | 2022-03-14T10:40:51.000Z | hpc_lecture_notes/it_solvers4.ipynb | tbetcke/hpc_lecture_notes | f061401a54ef467c8f8d0fb90294d63d83e3a9e1 | [
"BSD-3-Clause"
] | null | null | null | hpc_lecture_notes/it_solvers4.ipynb | tbetcke/hpc_lecture_notes | f061401a54ef467c8f8d0fb90294d63d83e3a9e1 | [
"BSD-3-Clause"
] | 3 | 2020-11-18T15:21:30.000Z | 2022-01-26T12:38:25.000Z | 122.827957 | 24,612 | 0.871203 | true | 1,762 | Qwen/Qwen-72B | 1. YES
2. YES | 0.872347 | 0.812867 | 0.709103 | __label__eng_Latn | 0.997646 | 0.485815 |
```
%load_ext autoreload
```
```
autoreload 2
```
```
%matplotlib inline
```
```
import matplotlib.pyplot as plt
import numpy as np
import sympy as sym
import inputs
import models
import solvers
```
# Example:
## Worker skill and firm productivity are $\sim U[a, b]$...
```
# define some workers skill
x, a, ... | 83cce094b47b38a24865f13041a97d92f94f8071 | 234,416 | ipynb | Jupyter Notebook | examples.ipynb | davidrpugh/assortative-matching-large-firms | e475dbc04e59ea066fae681b830fecdb8981c1d6 | [
"MIT"
] | 2 | 2019-07-31T06:34:01.000Z | 2020-07-29T10:32:37.000Z | examples.ipynb | davidrpugh/assortative-matching-large-firms | e475dbc04e59ea066fae681b830fecdb8981c1d6 | [
"MIT"
] | null | null | null | examples.ipynb | davidrpugh/assortative-matching-large-firms | e475dbc04e59ea066fae681b830fecdb8981c1d6 | [
"MIT"
] | 8 | 2016-11-13T19:55:54.000Z | 2021-09-17T07:20:22.000Z | 80.777395 | 43,755 | 0.715612 | true | 18,865 | Qwen/Qwen-72B | 1. YES
2. YES | 0.891811 | 0.771843 | 0.688339 | __label__eng_Latn | 0.188054 | 0.437572 |
# Mass Matrix
The 2 DOF dynamical system in figure is composed of two massless rigid bodies and a massive one.
Compute the mass matrix of the system with reference to the degrees of freedom indicated in figure, in the hypotesis of small displacements.
## Solution
We are going to use symbols for the relevant quant... | 4ebd5ec998a238f010338dc7413db904656667d5 | 8,830 | ipynb | Jupyter Notebook | dati_2017/wt05/MassMatrix.ipynb | shishitao/boffi_dynamics | 365f16d047fb2dbfc21a2874790f8bef563e0947 | [
"MIT"
] | null | null | null | dati_2017/wt05/MassMatrix.ipynb | shishitao/boffi_dynamics | 365f16d047fb2dbfc21a2874790f8bef563e0947 | [
"MIT"
] | null | null | null | dati_2017/wt05/MassMatrix.ipynb | shishitao/boffi_dynamics | 365f16d047fb2dbfc21a2874790f8bef563e0947 | [
"MIT"
] | 2 | 2019-06-23T12:32:39.000Z | 2021-08-15T18:33:55.000Z | 26.596386 | 225 | 0.503171 | true | 1,438 | Qwen/Qwen-72B | 1. YES
2. YES | 0.779993 | 0.803174 | 0.62647 | __label__eng_Latn | 0.821245 | 0.29383 |
# Debiasing with Orthogonalization
Previously, we saw how to evaluate a causal model. By itself, that's a huge deed. Causal models estimates the elasticity $\frac{\delta y}{\delta t}$, which is an unseen quantity. Hence, since we can't see the ground truth of what our model is estimating, we had to be very creative in... | e6ac047c68944f9609772ef0094434a97fee9926 | 455,086 | ipynb | Jupyter Notebook | causal-inference-for-the-brave-and-true/Debiasing-with-Orthogonalization.ipynb | keesterbrugge/python-causality-handbook | 4075476ee99422ed04ef3b2f8cabc982698f96b5 | [
"MIT"
] | 1 | 2021-12-21T12:59:17.000Z | 2021-12-21T12:59:17.000Z | causal-inference-for-the-brave-and-true/Debiasing-with-Orthogonalization.ipynb | HAlicia/python-causality-handbook | d2614cb1fbf8ae621d08be0e71df39b7a0d9e524 | [
"MIT"
] | null | null | null | causal-inference-for-the-brave-and-true/Debiasing-with-Orthogonalization.ipynb | HAlicia/python-causality-handbook | d2614cb1fbf8ae621d08be0e71df39b7a0d9e524 | [
"MIT"
] | null | null | null | 479.037895 | 87,180 | 0.931536 | true | 7,287 | Qwen/Qwen-72B | 1. YES
2. YES | 0.899121 | 0.870597 | 0.782773 | __label__eng_Latn | 0.997291 | 0.656975 |
# Asymptotic solutions in short-times
Projectile motion in a linear potential field with images is described by the equation
$$y_{\tau \tau} + \alpha \frac{1}{(1 + \epsilon y)^2} + 1= 0,$$
with $y(0) = \epsilon$ and $y_{\tau}(0)=1$, and where $\epsilon \ll 1$ is expected.
```python
import sympy as sym
from sympy i... | 0fbe3edc396fd2af8b4abdfe50105d4276d5e4f9 | 315,280 | ipynb | Jupyter Notebook | src/asymptotic-short.ipynb | 7deeptide/Thesis_scratch | d776d57f642de4df718c1f655f080c8fe402e092 | [
"MIT"
] | null | null | null | src/asymptotic-short.ipynb | 7deeptide/Thesis_scratch | d776d57f642de4df718c1f655f080c8fe402e092 | [
"MIT"
] | null | null | null | src/asymptotic-short.ipynb | 7deeptide/Thesis_scratch | d776d57f642de4df718c1f655f080c8fe402e092 | [
"MIT"
] | null | null | null | 335.404255 | 139,820 | 0.911215 | true | 3,245 | Qwen/Qwen-72B | 1. YES
2. YES | 0.817574 | 0.727975 | 0.595174 | __label__kor_Hang | 0.13463 | 0.221119 |
# Introduction to the Harmonic Oscillator
*Note:* Much of this is adapted/copied from https://flothesof.github.io/harmonic-oscillator-three-methods-solution.html
This week week we are going to begin studying molecular dynamics, which uses classical mechanics to study molecular systems. Our "hydrogen atom" in this sec... | a0d6cd6f0c288eb69d25ad4db8b32c8f916abb06 | 262,903 | ipynb | Jupyter Notebook | harmonic_oscillator.ipynb | sju-chem264-2019/10-24-19-introduction-to-harmonic-oscillator-jonathanyuan123 | a52d4d8d63d8dc11feb307649c721768c5c0c005 | [
"MIT"
] | null | null | null | harmonic_oscillator.ipynb | sju-chem264-2019/10-24-19-introduction-to-harmonic-oscillator-jonathanyuan123 | a52d4d8d63d8dc11feb307649c721768c5c0c005 | [
"MIT"
] | null | null | null | harmonic_oscillator.ipynb | sju-chem264-2019/10-24-19-introduction-to-harmonic-oscillator-jonathanyuan123 | a52d4d8d63d8dc11feb307649c721768c5c0c005 | [
"MIT"
] | null | null | null | 254.258221 | 28,316 | 0.921184 | true | 2,726 | Qwen/Qwen-72B | 1. YES
2. YES | 0.847968 | 0.868827 | 0.736737 | __label__eng_Latn | 0.935202 | 0.550019 |
```python
import numpy as np
import matplotlib.pyplot as plt
import sympy as sp
import control as co
s = sp.Symbol('s', real=True)
k = sp.Symbol('k', real=True)
```
```python
Ka = 1/((s+1)*(s+2)*(s+3))
Ka
```
$\displaystyle \frac{1}{\left(s + 1\right) \left(s + 2\right) \left(s + 3\right)}$
```python
Ka = sp... | f51b01a5cc7421012cc45e5345baacba3b5ae3fc | 2,818 | ipynb | Jupyter Notebook | CW/CW8/a.ipynb | John15321/TR | 82dd73425dde3a5f3f50411b2ee03be88e8bf65e | [
"MIT"
] | 8 | 2021-02-04T10:39:41.000Z | 2021-04-15T13:32:46.000Z | CW/CW8/a.ipynb | John15321/TR | 82dd73425dde3a5f3f50411b2ee03be88e8bf65e | [
"MIT"
] | null | null | null | CW/CW8/a.ipynb | John15321/TR | 82dd73425dde3a5f3f50411b2ee03be88e8bf65e | [
"MIT"
] | null | null | null | 22.365079 | 249 | 0.456352 | true | 334 | Qwen/Qwen-72B | 1. YES
2. YES | 0.94079 | 0.731059 | 0.687772 | __label__kor_Hang | 0.259864 | 0.436257 |
# Generative Learning Algorithms
## Discriminative & Generative
discriminative：try to learn $p(y|x)$ directly, such as logistic regression<br>
or try to learn mappings from the space of inputs to the labels $\left \{ 0, 1\right \}$ directly, such as perceptron
generative：algorithms that try to model $p(x|y)$ and $p(... | 59b547f5630df49258a1321e5bed3f25cdab49ef | 12,386 | ipynb | Jupyter Notebook | _build/html/_sources/04_generative_learning_algorithms.ipynb | newfacade/machine-learning-notes | 1e59fe7f9b21e16151654dee888ceccc726274d3 | [
"MIT"
] | null | null | null | _build/html/_sources/04_generative_learning_algorithms.ipynb | newfacade/machine-learning-notes | 1e59fe7f9b21e16151654dee888ceccc726274d3 | [
"MIT"
] | null | null | null | _build/html/_sources/04_generative_learning_algorithms.ipynb | newfacade/machine-learning-notes | 1e59fe7f9b21e16151654dee888ceccc726274d3 | [
"MIT"
] | null | null | null | 41.563758 | 296 | 0.471016 | true | 3,906 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91848 | 0.857768 | 0.787843 | __label__eng_Latn | 0.62705 | 0.668756 |
```python
from kanren import run, var, fact
import kanren.assoccomm as la
# 足し算(add)と掛け算（mul)
# addとmulはルールの名前なだけ
add = 'addition'
mul = 'multiplication'
# 足し算、掛け算は交換法則（commutative)、結合法則(associative)を持つ事をfactを使って宣言する
# 交換法則とは、入れ替えても結果が変わらない事。足し算も掛け算も入れ替えても答えは変わらない
# 結合法則とは、カッコの位置を変えても変わらない事。足し算だけの式、掛け算だけの式はカッコの位置が変わっ... | c5f093799ad88b181cfde6094677648d6bb7a20f | 3,470 | ipynb | Jupyter Notebook | Ex/Chapter6/Chapter6-5.ipynb | tryoutlab/python-ai-oreilly | 111a0db4a9d5bf7ec4c07b1e9e357ed4fa225f28 | [
"Unlicense"
] | null | null | null | Ex/Chapter6/Chapter6-5.ipynb | tryoutlab/python-ai-oreilly | 111a0db4a9d5bf7ec4c07b1e9e357ed4fa225f28 | [
"Unlicense"
] | null | null | null | Ex/Chapter6/Chapter6-5.ipynb | tryoutlab/python-ai-oreilly | 111a0db4a9d5bf7ec4c07b1e9e357ed4fa225f28 | [
"Unlicense"
] | null | null | null | 22.679739 | 80 | 0.491643 | true | 805 | Qwen/Qwen-72B | 1. YES
2. YES | 0.839734 | 0.685949 | 0.576015 | __label__eng_Latn | 0.130228 | 0.176606 |
```python
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
import random
import sympy
```
Bad key "text.kerning_factor" on line 4 in
/home/sc/anaconda3/envs/old_nx/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.
You probably need to get an... | d4567b7d8e35d2f5bd9c65695a1121b500602256 | 218,821 | ipynb | Jupyter Notebook | FormalExperiments/Experiment-Facebook-diffT.ipynb | CyanideBoy/Accelerated-MCMC | aeca24ff4ac03d3a62c39b9041d004135b5beca6 | [
"MIT"
] | null | null | null | FormalExperiments/Experiment-Facebook-diffT.ipynb | CyanideBoy/Accelerated-MCMC | aeca24ff4ac03d3a62c39b9041d004135b5beca6 | [
"MIT"
] | null | null | null | FormalExperiments/Experiment-Facebook-diffT.ipynb | CyanideBoy/Accelerated-MCMC | aeca24ff4ac03d3a62c39b9041d004135b5beca6 | [
"MIT"
] | null | null | null | 370.883051 | 54,640 | 0.928247 | true | 3,738 | Qwen/Qwen-72B | 1. YES
2. YES | 0.835484 | 0.79053 | 0.660475 | __label__kor_Hang | 0.134617 | 0.372836 |
# M10. Amdahl's Law
The most useful corollaries to what is now known as *Amdahl's Law* are hardly profound. The notion of prioritizing the improvements *with the greatest bearing on the overall result* is almost common sense (which is maybe to suggest that it isn't common at all). Violations of the Law are prevalent (... | 76328778b3295307b089cf6516bb36c8abe806c0 | 2,960 | ipynb | Jupyter Notebook | M10_Amdahl's_Law.ipynb | brekekekex/computer_organization_memoranda | d16dc251075d3da49aaf01f148676f857d05dc4b | [
"Unlicense"
] | 2 | 2020-01-17T16:34:17.000Z | 2020-02-23T22:06:07.000Z | M10_Amdahl's_Law.ipynb | brekekekex/computer_organization_memoranda | d16dc251075d3da49aaf01f148676f857d05dc4b | [
"Unlicense"
] | null | null | null | M10_Amdahl's_Law.ipynb | brekekekex/computer_organization_memoranda | d16dc251075d3da49aaf01f148676f857d05dc4b | [
"Unlicense"
] | null | null | null | 47.741935 | 395 | 0.664189 | true | 528 | Qwen/Qwen-72B | 1. YES
2. YES | 0.904651 | 0.897695 | 0.812101 | __label__eng_Latn | 0.999896 | 0.725114 |
```python
%run header.ipynb
```
```python
from sympy import pi
from sympy.physics.units import meter, foot
```
```python
# dictionary that holds all values.
# if already defined (such as by another notebook) then don't override
if "values" not in vars():
values={"d": 12.1 * meter}
Formula.set_global_values(val... | a81067a5180c536fec5ca95882cec8dad3a79409 | 2,035 | ipynb | Jupyter Notebook | notebooks/Circle multifile/Circle Area.ipynb | alugowski/jupyter-forchaps | c46904286df8b60a8e5200e0c8b6bafca3379c9d | [
"BSD-2-Clause"
] | 1 | 2020-02-12T11:25:37.000Z | 2020-02-12T11:25:37.000Z | notebooks/Circle multifile/Circle Area.ipynb | alugowski/jupyter-forchaps | c46904286df8b60a8e5200e0c8b6bafca3379c9d | [
"BSD-2-Clause"
] | null | null | null | notebooks/Circle multifile/Circle Area.ipynb | alugowski/jupyter-forchaps | c46904286df8b60a8e5200e0c8b6bafca3379c9d | [
"BSD-2-Clause"
] | null | null | null | 22.362637 | 233 | 0.50516 | true | 154 | Qwen/Qwen-72B | 1. YES
2. YES | 0.874077 | 0.709019 | 0.619738 | __label__eng_Latn | 0.88786 | 0.278188 |
## Optimal Power Flow
_**[Power Systems Optimization](https://github.com/east-winds/power-systems-optimization)**_
_by Michael R. Davidson, Jesse D. Jenkins, and Sambuddha Chakrabarti_
This notebook consists an introductory glimpse of and a few hands-on activities and demostrations of the Optimal Power Flow (OPF) pro... | a14411ae2d618522ce56b738a289f41f944a5d5f | 12,600 | ipynb | Jupyter Notebook | Notebooks/06-OPF-problem_other.ipynb | sambuddhac/power-systems-optimization | f65ec4b718807703452cf6723105926dac73c649 | [
"CC-BY-4.0",
"MIT"
] | null | null | null | Notebooks/06-OPF-problem_other.ipynb | sambuddhac/power-systems-optimization | f65ec4b718807703452cf6723105926dac73c649 | [
"CC-BY-4.0",
"MIT"
] | null | null | null | Notebooks/06-OPF-problem_other.ipynb | sambuddhac/power-systems-optimization | f65ec4b718807703452cf6723105926dac73c649 | [
"CC-BY-4.0",
"MIT"
] | null | null | null | 45.818182 | 940 | 0.59619 | true | 2,411 | Qwen/Qwen-72B | 1. YES
2. YES | 0.891811 | 0.851953 | 0.759781 | __label__eng_Latn | 0.946954 | 0.603558 |
<a href="https://colab.research.google.com/github/gherbin/ComputerVisionKUL/blob/master/CV_Group9_assignment.ipynb" target="_parent"></a>
# Hi there!
> *\[14 Apr 2020] A notebook written by Geoffroy Herbin, group9, r0426473, in the context of the Computer Vision course [H02A5](https://p.cygnus.cc.kuleuven.be/webapps... | a2c1356b939c519562dd290350f405781677ea90 | 401,157 | ipynb | Jupyter Notebook | assignment2/CV_Group9_assignment.ipynb | gherbin/ComputerVisionKUL | c1367c812007d4533aca24d0d09b06590c0193a5 | [
"MIT"
] | null | null | null | assignment2/CV_Group9_assignment.ipynb | gherbin/ComputerVisionKUL | c1367c812007d4533aca24d0d09b06590c0193a5 | [
"MIT"
] | null | null | null | assignment2/CV_Group9_assignment.ipynb | gherbin/ComputerVisionKUL | c1367c812007d4533aca24d0d09b06590c0193a5 | [
"MIT"
] | null | null | null | 44.302264 | 672 | 0.543859 | true | 65,258 | Qwen/Qwen-72B | 1. YES
2. YES | 0.712232 | 0.779993 | 0.555536 | __label__eng_Latn | 0.985515 | 0.129026 |
# One layer model
Here we show how to run our two-layer model as a single-layer model. There are two different ways to do this, which we present below.
## Imports and loading data
```python
# NBVAL_IGNORE_OUTPUT
import os.path
import numpy as np
import pandas as pd
from openscm_units import unit_registry as ur
imp... | cdd6043b49bd3ac6e59dafea50c75cefbc1018cb | 277,048 | ipynb | Jupyter Notebook | docs/source/usage/one-layer-model.ipynb | sadielbartholomew/openscm-twolayermodel | 19b030571892a3238082765671e161ddd4c2ab97 | [
"BSD-3-Clause"
] | 6 | 2020-10-13T00:34:04.000Z | 2022-02-16T23:33:48.000Z | docs/source/usage/one-layer-model.ipynb | sadielbartholomew/openscm-twolayermodel | 19b030571892a3238082765671e161ddd4c2ab97 | [
"BSD-3-Clause"
] | 16 | 2020-04-16T11:17:05.000Z | 2021-06-15T00:58:09.000Z | docs/source/usage/one-layer-model.ipynb | sadielbartholomew/openscm-twolayermodel | 19b030571892a3238082765671e161ddd4c2ab97 | [
"BSD-3-Clause"
] | 6 | 2020-10-12T13:24:28.000Z | 2021-06-22T12:54:15.000Z | 180.604954 | 87,816 | 0.639109 | true | 7,804 | Qwen/Qwen-72B | 1. YES
2. YES | 0.83762 | 0.774583 | 0.648806 | __label__eng_Latn | 0.358364 | 0.345726 |
# Reduced Helmholtz equation of state: carbon dioxide
**Water equation of state:** You can see the full, state-of-the-art equation of state for water, which also uses a reduced Helmholtz approach: the IAPWS 1995 formulation {cite}`Wagner2002`. This equation is state is available using CoolProp with the `Water` fluid.
... | 8ebaf4a95839c234dfcf936f837bdef8958296e1 | 123,089 | ipynb | Jupyter Notebook | book/content/properties-pure/reduced-helmholtz.ipynb | kyleniemeyer/computational-thermo | 3f0d1d4a6d4247ac3bf3b74867411f2090c70cbd | [
"CC-BY-4.0",
"BSD-3-Clause"
] | 13 | 2020-04-01T05:52:06.000Z | 2022-03-27T20:25:59.000Z | book/content/properties-pure/reduced-helmholtz.ipynb | kyleniemeyer/computational-thermo | 3f0d1d4a6d4247ac3bf3b74867411f2090c70cbd | [
"CC-BY-4.0",
"BSD-3-Clause"
] | 1 | 2020-04-28T04:02:05.000Z | 2020-04-29T17:49:52.000Z | book/content/properties-pure/reduced-helmholtz.ipynb | kyleniemeyer/computational-thermo | 3f0d1d4a6d4247ac3bf3b74867411f2090c70cbd | [
"CC-BY-4.0",
"BSD-3-Clause"
] | 6 | 2020-04-03T14:52:24.000Z | 2022-03-29T02:29:43.000Z | 369.636637 | 86,980 | 0.921707 | true | 2,587 | Qwen/Qwen-72B | 1. YES
2. YES | 0.879147 | 0.73412 | 0.645399 | __label__eng_Latn | 0.633737 | 0.337808 |
# All together now
We have now discretized the two first order equations over a single cell. What is left is to assemble and solve the DC system over the entire mesh. To implement the divergence on the full mesh, the stencil of $\pm 1$'s must index into $\mathbf{j}$ on the entire mesh (instead of four elements). Altho... | a79e69c48767cc564038f85213177cb089a0d6e6 | 28,565 | ipynb | Jupyter Notebook | notebooks/fundamentals/pixels_and_neighbors/all_together_now.ipynb | ahartikainen/computation | 2b7f0fd2fe2d9f1fc494cb52f57764a09ba0617e | [
"MIT"
] | 13 | 2017-03-09T06:01:04.000Z | 2021-12-15T07:40:40.000Z | notebooks/fundamentals/pixels_and_neighbors/all_together_now.ipynb | ahartikainen/computation | 2b7f0fd2fe2d9f1fc494cb52f57764a09ba0617e | [
"MIT"
] | 14 | 2016-03-29T18:08:09.000Z | 2017-03-07T16:34:22.000Z | notebooks/fundamentals/pixels_and_neighbors/all_together_now.ipynb | ahartikainen/computation | 2b7f0fd2fe2d9f1fc494cb52f57764a09ba0617e | [
"MIT"
] | 6 | 2017-06-19T15:42:02.000Z | 2020-03-02T03:29:21.000Z | 213.171642 | 23,562 | 0.901873 | true | 767 | Qwen/Qwen-72B | 1. YES
2. YES | 0.835484 | 0.907312 | 0.758044 | __label__eng_Latn | 0.996644 | 0.599523 |
# Causal Impact
Will Fuks
https://github.com/WillianFuks/tfcausalimpact
[LinkedIn](https://www.linkedin.com/in/willian-fuks-62622217/)
https://github.com/WillianFuks/pyDataSP-tfcausalimpact
```sh
git clone git@github.com:WillianFuks/pyDataSP-tfcausalimpact.git
cd pyDataSP-tfcausalimpact/
python3.9 -m venv .env... | 81d9c36a9b07531f942d04616fdf50cc824a018d | 47,109 | ipynb | Jupyter Notebook | pyDataSP - tfcausalimpact.ipynb | WillianFuks/pyDataSP-tfcausalimpact | 612a9e6326e278cf21fbee118a204fa5c2e95b92 | [
"MIT"
] | 1 | 2021-08-22T09:59:34.000Z | 2021-08-22T09:59:34.000Z | pyDataSP - tfcausalimpact.ipynb | WillianFuks/pyDataSP-tfcausalimpact | 612a9e6326e278cf21fbee118a204fa5c2e95b92 | [
"MIT"
] | null | null | null | pyDataSP - tfcausalimpact.ipynb | WillianFuks/pyDataSP-tfcausalimpact | 612a9e6326e278cf21fbee118a204fa5c2e95b92 | [
"MIT"
] | null | null | null | 23.126657 | 157 | 0.517332 | true | 6,515 | Qwen/Qwen-72B | 1. YES
2. YES | 0.760651 | 0.709019 | 0.539316 | __label__eng_Latn | 0.159494 | 0.091341 |
# 823 HW2
## https://yiyangzhang2020.github.io/yz628-823-blog/
## Number theory and a Google recruitment puzzle
### Find the first 10-digit prime in the decimal expansion of 17π.
### The first 5 digits in the decimal expansion of π are 14159. The first 4-digit prime in the decimal expansion of π are 4159. You are ask... | 8039d14c6774d3a80550c307fbbf2bc4bf618ace | 16,685 | ipynb | Jupyter Notebook | _notebooks/2021-09-17-Yiyang-Zhang-823-HW2.ipynb | yiyangzhang2020/yz628-823-blog | 12ea3947c3e2f0fb0eb5d5acc4c1baf8e4954aec | [
"Apache-2.0"
] | null | null | null | _notebooks/2021-09-17-Yiyang-Zhang-823-HW2.ipynb | yiyangzhang2020/yz628-823-blog | 12ea3947c3e2f0fb0eb5d5acc4c1baf8e4954aec | [
"Apache-2.0"
] | null | null | null | _notebooks/2021-09-17-Yiyang-Zhang-823-HW2.ipynb | yiyangzhang2020/yz628-823-blog | 12ea3947c3e2f0fb0eb5d5acc4c1baf8e4954aec | [
"Apache-2.0"
] | null | null | null | 32.587891 | 592 | 0.538388 | true | 2,392 | Qwen/Qwen-72B | 1. YES
2. YES | 0.877477 | 0.851953 | 0.747569 | __label__eng_Latn | 0.990043 | 0.575185 |
# Circuito RLC paralelo sem fonte
Jupyter Notebook desenvolvido por [Gustavo S.S.](https://github.com/GSimas)
Circuitos RLC em paralelo têm diversas aplicações, como em projetos de filtros
e redes de comunicação. Suponha que a corrente inicial I0 no indutor e a tensão inicial V0 no capacitor sejam:
\begin{align}
{\L... | 324c716e4d05506b0ae48282565bf5f02765f9c9 | 12,760 | ipynb | Jupyter Notebook | Aula 14 - Circuito RLC paralelo.ipynb | ofgod2/Circuitos-electricos-Boylestad-12ed-Portugues | 60e815f6904858f3cda8b5c7ead8ea77aa09c7fd | [
"MIT"
] | 7 | 2019-08-13T13:33:15.000Z | 2021-11-16T16:46:06.000Z | Aula 14 - Circuito RLC paralelo.ipynb | ofgod2/Circuitos-electricos-Boylestad-12ed-Portugues | 60e815f6904858f3cda8b5c7ead8ea77aa09c7fd | [
"MIT"
] | 1 | 2017-08-24T17:36:15.000Z | 2017-08-24T17:36:15.000Z | Aula 14 - Circuito RLC paralelo.ipynb | ofgod2/Circuitos-electricos-Boylestad-12ed-Portugues | 60e815f6904858f3cda8b5c7ead8ea77aa09c7fd | [
"MIT"
] | 8 | 2019-03-29T14:31:49.000Z | 2021-12-30T17:59:23.000Z | 28.482143 | 119 | 0.447571 | true | 3,520 | Qwen/Qwen-72B | 1. YES
2. YES | 0.826712 | 0.843895 | 0.697658 | __label__por_Latn | 0.459219 | 0.459225 |
<p align="center">
</p>
## Interactive Hypothesis Testing Demonstration
### Boostrap and Analytical Methods for Hypothesis Testing, Difference in Means
* we calculate the hypothesis test for different in means with boostrap and compare to the analytical expression
* **Welch's t-test**: we assume the features ... | 21d100e460e2365a9d25d1e9d2c96f4f62cf0800 | 23,805 | ipynb | Jupyter Notebook | Interactive_Hypothesis_Testing.ipynb | Skipper6931/PythonNumericalDemos | 9822f8252ed8d714e029163eaede75a5d1232bc9 | [
"MIT"
] | 403 | 2017-10-15T02:07:38.000Z | 2022-03-30T15:27:14.000Z | Interactive_Hypothesis_Testing.ipynb | ahmeduncc/PythonNumericalDemos | c4702799196f549c72a2b2378ffe8ee4e7d66c8c | [
"MIT"
] | 4 | 2019-08-21T10:35:09.000Z | 2021-02-04T04:57:13.000Z | Interactive_Hypothesis_Testing.ipynb | ahmeduncc/PythonNumericalDemos | c4702799196f549c72a2b2378ffe8ee4e7d66c8c | [
"MIT"
] | 276 | 2018-06-27T11:20:30.000Z | 2022-03-25T16:04:24.000Z | 51.75 | 512 | 0.619492 | true | 4,966 | Qwen/Qwen-72B | 1. YES
2. YES | 0.841826 | 0.859664 | 0.723687 | __label__eng_Latn | 0.895487 | 0.519699 |
(c) Juan Gomez 2019. Thanks to Universidad EAFIT for support. This material is part of the course Introduction to Finite Element Analysis
# Elasticity in a notebook
## Introduction
This notebook sumarizes the boundary value problem (BVP) for the linearized theory of elasticity. It is assumed that the student is fami... | 02b5e79d9d16899070949b88d438856ba590fda1 | 54,469 | ipynb | Jupyter Notebook | notebooks/07_elasticity.ipynb | AppliedMechanics-EAFIT/Introductory-Finite-Elements | a4b44d8bf29bcd40185e51ee036f38102f9c6a72 | [
"MIT"
] | 39 | 2019-11-26T13:28:30.000Z | 2022-02-16T17:57:11.000Z | notebooks/07_elasticity.ipynb | jgomezc1/Introductory-Finite-Elements | a4b44d8bf29bcd40185e51ee036f38102f9c6a72 | [
"MIT"
] | null | null | null | notebooks/07_elasticity.ipynb | jgomezc1/Introductory-Finite-Elements | a4b44d8bf29bcd40185e51ee036f38102f9c6a72 | [
"MIT"
] | 18 | 2020-02-17T07:24:59.000Z | 2022-03-02T07:54:28.000Z | 57.822718 | 10,221 | 0.703299 | true | 5,750 | Qwen/Qwen-72B | 1. YES
2. YES | 0.800692 | 0.766294 | 0.613565 | __label__eng_Latn | 0.964321 | 0.263848 |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Introduction" data-toc-modified-id="Introduction-1"><span class="toc-item-num">1 </span>Introduction</a></span><ul class="toc-item"><li><span><a href="#Simulation" data-toc-modified-id="Simulatio... | 974181cdda2591045b211f4b2164c40eb51b5b4e | 62,983 | ipynb | Jupyter Notebook | experiments/multiscale_gmrf/optimal_upscaling_01.ipynb | hvanwyk/quadmesh | 12561f1fae1ce4cd27aa70c71041c3fdb417267c | [
"MIT"
] | 6 | 2019-06-04T09:26:15.000Z | 2021-10-21T05:00:23.000Z | experiments/multiscale_gmrf/optimal_upscaling_01.ipynb | hvanwyk/quadmesh | 12561f1fae1ce4cd27aa70c71041c3fdb417267c | [
"MIT"
] | 9 | 2018-02-28T22:04:43.000Z | 2022-02-18T17:14:30.000Z | experiments/multiscale_gmrf/optimal_upscaling_01.ipynb | hvanwyk/quadmesh | 12561f1fae1ce4cd27aa70c71041c3fdb417267c | [
"MIT"
] | null | null | null | 141.217489 | 33,192 | 0.872267 | true | 2,874 | Qwen/Qwen-72B | 1. YES
2. YES | 0.833325 | 0.721743 | 0.601446 | __label__eng_Latn | 0.924616 | 0.235692 |
(page_topic1)=
Errors in Numerical Differentiation
=======================
In order for the finite difference formulas derived in the previous section to be useful, we need to have some idea of the errors involved in using these formulas. As mentioned, we are replacing $f(x)$ with its polynomial interpolant $p_n(x)$ ... | 38f9e419837c1ef2e9d38db098660d8e2e66d61a | 36,538 | ipynb | Jupyter Notebook | class/NDiffInt/NumDiffInt_Errors.ipynb | CDenniston/NumericalAnalysis | 8f4ccaa864461c36e269824a0e9038bc14ef10b1 | [
"MIT"
] | null | null | null | class/NDiffInt/NumDiffInt_Errors.ipynb | CDenniston/NumericalAnalysis | 8f4ccaa864461c36e269824a0e9038bc14ef10b1 | [
"MIT"
] | null | null | null | class/NDiffInt/NumDiffInt_Errors.ipynb | CDenniston/NumericalAnalysis | 8f4ccaa864461c36e269824a0e9038bc14ef10b1 | [
"MIT"
] | null | null | null | 132.865455 | 23,100 | 0.816793 | true | 3,613 | Qwen/Qwen-72B | 1. YES
2. YES | 0.826712 | 0.857768 | 0.709127 | __label__eng_Latn | 0.978302 | 0.485871 |
```python
import numpy as np
import sympy as sp
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import CurrentDistribution as current
currentPath = current.Solenoid(10, 10, 1, sp.pi/64)
offset=5
testPointNum = 16
xPoints = np.linspace(int(currentPath.xmin-offset), int(currentPath.xmax+offs... | eb85ef7969c289f87635bde61d655936e3075afc | 99,908 | ipynb | Jupyter Notebook | Magnetic Field Distributions/example_3d_plot.ipynb | phys2331/EM-Notebooks | a62d0ded01ce5c7228d85b39964d17c1a98839af | [
"Unlicense"
] | null | null | null | Magnetic Field Distributions/example_3d_plot.ipynb | phys2331/EM-Notebooks | a62d0ded01ce5c7228d85b39964d17c1a98839af | [
"Unlicense"
] | 1 | 2020-10-12T20:37:11.000Z | 2020-10-12T20:37:11.000Z | Magnetic Field Distributions/example_3d_plot.ipynb | phys2331/EM-Notebooks | a62d0ded01ce5c7228d85b39964d17c1a98839af | [
"Unlicense"
] | 1 | 2020-10-10T00:53:03.000Z | 2020-10-10T00:53:03.000Z | 1,314.578947 | 98,044 | 0.958091 | true | 229 | Qwen/Qwen-72B | 1. YES
2. YES | 0.875787 | 0.705785 | 0.618117 | __label__eng_Latn | 0.282724 | 0.274424 |
# Basic Neural Network from Scratch
>## Objective:
To understand and build a basic one hidden layer Neutral Network from scratch
> ## Approach :
- **Getting the Dataset**
- **Logistic Regression**
- **Neural Network : Understanding**
- Neural Network structure
- Activation functions : Softmax... | 7273c9247943f40caf1bbd39f1327a1d33942ec4 | 77,923 | ipynb | Jupyter Notebook | Basic Neural Network from Scratch/Basic Neural Network from Scratch (To-Do Template).ipynb | abhisngh/Data-Science | c7fa9e4d81c427382fb9a9d3b97912ef2b21f3ae | [
"MIT"
] | 1 | 2020-05-29T20:07:49.000Z | 2020-05-29T20:07:49.000Z | Basic Neural Network from Scratch/Basic Neural Network from Scratch (To-Do Template).ipynb | abhisngh/Data-Science | c7fa9e4d81c427382fb9a9d3b97912ef2b21f3ae | [
"MIT"
] | null | null | null | Basic Neural Network from Scratch/Basic Neural Network from Scratch (To-Do Template).ipynb | abhisngh/Data-Science | c7fa9e4d81c427382fb9a9d3b97912ef2b21f3ae | [
"MIT"
] | null | null | null | 91.244731 | 43,360 | 0.80491 | true | 6,074 | Qwen/Qwen-72B | 1. YES
2. YES | 0.861538 | 0.841826 | 0.725265 | __label__eng_Latn | 0.99788 | 0.523365 |
```
from pylab import plot, semilogy
from numpy import loadtxt, linspace
from numpy.fft import fft, fftfreq
from sympy import exp, sin, pi, var, Integral
var("x")
L = 2 # domain [0, L]
rho = exp(sin(2*pi*x/L))
#rho = sin(2*pi*x/L)
integ = Integral(rho, (x, 0, L)).n()
rho -= integ / L
print "rho(x) =", rho
print "Integr... | c82e213f618336f1e2d924e1b0632607c4523985 | 61,319 | ipynb | Jupyter Notebook | src/tests/fem/plots/fft.ipynb | certik/hfsolver | b4c50c1979fb7e468b1852b144ba756f5a51788d | [
"BSD-2-Clause"
] | 20 | 2015-03-24T13:06:39.000Z | 2022-03-29T00:14:02.000Z | src/tests/fem/plots/fft.ipynb | certik/hfsolver | b4c50c1979fb7e468b1852b144ba756f5a51788d | [
"BSD-2-Clause"
] | 6 | 2015-03-25T04:59:43.000Z | 2017-06-06T23:00:09.000Z | src/tests/fem/plots/fft.ipynb | certik/hfsolver | b4c50c1979fb7e468b1852b144ba756f5a51788d | [
"BSD-2-Clause"
] | 5 | 2016-01-20T13:38:22.000Z | 2020-11-24T15:35:43.000Z | 352.408046 | 30,588 | 0.920612 | true | 733 | Qwen/Qwen-72B | 1. YES
2. YES | 0.912436 | 0.731059 | 0.667044 | __label__eng_Latn | 0.291208 | 0.388098 |
<a href="https://colab.research.google.com/github/GalinaZh/Appl_alg2021/blob/main/Applied_Alg_Lab_1.ipynb" target="_parent"></a>
# Лабораторная работа 1
# Прикладная алгебра и численные методы
## Псевдообратная матрица, ортогонализация Грама-Шмидта, LU, QR, МНК, полиномы Лагранжа, Чебышева, сплайны, кривые Безье, норм... | 009711f0ec02beda39a02c55fa95f59b8a3388ae | 7,122 | ipynb | Jupyter Notebook | Applied_Alg_Lab_1.ipynb | GalinaZh/Appl_alg2021 | 09761b56eb2bdfee4cd5f12cd96562ca146fcb15 | [
"MIT"
] | null | null | null | Applied_Alg_Lab_1.ipynb | GalinaZh/Appl_alg2021 | 09761b56eb2bdfee4cd5f12cd96562ca146fcb15 | [
"MIT"
] | null | null | null | Applied_Alg_Lab_1.ipynb | GalinaZh/Appl_alg2021 | 09761b56eb2bdfee4cd5f12cd96562ca146fcb15 | [
"MIT"
] | null | null | null | 25.255319 | 265 | 0.48975 | true | 1,119 | Qwen/Qwen-72B | 1. YES
2. YES | 0.872347 | 0.76908 | 0.670905 | __label__rus_Cyrl | 0.972141 | 0.397068 |
# Model
The generalized Roy model is characterized by the following set of equations.
**Potential Outcomes**
\begin{align}
Y_1 &= X\beta_1 + U_1 \\
Y_0 &= X\beta_0 + U_0
\end{align}
**Cost**
\begin{align}
C = Z\gamma + U_C
\end{align}
**Choice**
\begin{align}
S &= Y_1 - Y_0 - C\\
D &= I[S > 0]
\end{align}
Col... | db1ae1e9abfb02e4649a4446f72a28e080300631 | 11,943 | ipynb | Jupyter Notebook | lectures/economic_models/generalized_roy/model/lecture.ipynb | snowdj/course | 7caff2b0fd9958c315168791810a05521153d2e7 | [
"MIT"
] | null | null | null | lectures/economic_models/generalized_roy/model/lecture.ipynb | snowdj/course | 7caff2b0fd9958c315168791810a05521153d2e7 | [
"MIT"
] | null | null | null | lectures/economic_models/generalized_roy/model/lecture.ipynb | snowdj/course | 7caff2b0fd9958c315168791810a05521153d2e7 | [
"MIT"
] | null | null | null | 38.775974 | 840 | 0.557314 | true | 2,383 | Qwen/Qwen-72B | 1. YES
2. YES | 0.843895 | 0.727975 | 0.614335 | __label__eng_Latn | 0.618922 | 0.265636 |
# Periodic homogenization of linear elastic materials
## Introduction
This tour will show how to perform periodic homogenization of linear elastic materials. The considered 2D plane strain problem deals with a skewed unit cell of dimensions $1\times \sqrt{3}/2$ consisting of circular inclusions (numbered $1$) of radi... | f438e69c97bc9de7b803684a6f86d24a7645c8f3 | 24,836 | ipynb | Jupyter Notebook | periodic_homog_elas.ipynb | alhermann/FEniCS-Code | 4d39b89de66f4f9d8cf818f40f31c83092579a8c | [
"MIT"
] | null | null | null | periodic_homog_elas.ipynb | alhermann/FEniCS-Code | 4d39b89de66f4f9d8cf818f40f31c83092579a8c | [
"MIT"
] | null | null | null | periodic_homog_elas.ipynb | alhermann/FEniCS-Code | 4d39b89de66f4f9d8cf818f40f31c83092579a8c | [
"MIT"
] | null | null | null | 63.845758 | 6,492 | 0.694234 | true | 4,034 | Qwen/Qwen-72B | 1. YES
2. YES | 0.896251 | 0.793106 | 0.710822 | __label__eng_Latn | 0.94617 | 0.48981 |
```python
import numpy as np
import sympy as sy
from sympy.utilities.codegen import codegen
import control.matlab as cm
import re
import matplotlib.pyplot as plt
from scipy import signal
```
# Designing RST controller for the harmonic oscillator
## The plant model
```python
z = sy.symbols('z', real=False)
# The pla... | 728704eb87c5ce9bcbea4ae21ead64bff59cce5f | 27,443 | ipynb | Jupyter Notebook | polynomial-design/notebooks/.ipynb_checkpoints/hw3-ht2018-harmonic-oscillator-checkpoint.ipynb | kjartan-at-tec/mr2007-computerized-control | 16e35f5007f53870eaf344eea1165507505ab4aa | [
"MIT"
] | 2 | 2020-11-07T05:20:37.000Z | 2020-12-22T09:46:13.000Z | polynomial-design/notebooks/.ipynb_checkpoints/hw3-ht2018-harmonic-oscillator-checkpoint.ipynb | kjartan-at-tec/mr2007-computerized-control | 16e35f5007f53870eaf344eea1165507505ab4aa | [
"MIT"
] | 4 | 2020-06-12T20:44:41.000Z | 2020-06-12T20:49:00.000Z | polynomial-design/notebooks/.ipynb_checkpoints/hw3-ht2018-harmonic-oscillator-checkpoint.ipynb | kjartan-at-tec/mr2007-computerized-control | 16e35f5007f53870eaf344eea1165507505ab4aa | [
"MIT"
] | 1 | 2021-03-14T03:55:27.000Z | 2021-03-14T03:55:27.000Z | 52.775 | 7,940 | 0.665088 | true | 3,029 | Qwen/Qwen-72B | 1. YES
2. YES | 0.822189 | 0.675765 | 0.555606 | __label__kor_Hang | 0.069262 | 0.129189 |
## SurfinPy
#### Tutorial 2 - Introducing temperature dependence
In tutorial 1 we generated a phase diagram at 0K. However this is not representative of normal conditions. Temperature is an important consideration for materials chemists and we may wish to evaluate the state of a solid electrolyte at the operating tem... | 75491feb8c605e884fd406ad47b4b238a234beb8 | 24,731 | ipynb | Jupyter Notebook | examples/Notebooks/Surfaces/Tutorial_2.ipynb | jstse/SurfinPy | ff3a79f9415c170885e109ab881368271f3dcc19 | [
"MIT"
] | 30 | 2019-01-28T17:47:24.000Z | 2022-03-22T03:26:00.000Z | examples/Notebooks/Surfaces/Tutorial_2.ipynb | jstse/SurfinPy | ff3a79f9415c170885e109ab881368271f3dcc19 | [
"MIT"
] | 14 | 2018-09-03T15:49:06.000Z | 2022-02-08T22:09:51.000Z | examples/Notebooks/Surfaces/Tutorial_2.ipynb | jstse/SurfinPy | ff3a79f9415c170885e109ab881368271f3dcc19 | [
"MIT"
] | 19 | 2019-02-11T09:11:29.000Z | 2022-03-11T08:47:24.000Z | 97.750988 | 16,356 | 0.835146 | true | 1,904 | Qwen/Qwen-72B | 1. YES
2. YES | 0.815232 | 0.682574 | 0.556456 | __label__eng_Latn | 0.927251 | 0.131164 |
```python
import cirq
```
We have already seen that quantum circuits can be used to transfer information efficiently.
Now, we will see for the first time how we can use a quantum circuit to solve a problem in a more efficient way than it is possible with a classical probabilistic Turing machine. While the problem we ... | 3d52956a869085d58f9460e3c74b3b261424f735 | 7,136 | ipynb | Jupyter Notebook | src/3. The Deutsch Algorithm.ipynb | phyjonas/QC | bbb3ace33dc7c5e64ba051c2908ea1fd2f88f4ee | [
"MIT"
] | null | null | null | src/3. The Deutsch Algorithm.ipynb | phyjonas/QC | bbb3ace33dc7c5e64ba051c2908ea1fd2f88f4ee | [
"MIT"
] | null | null | null | src/3. The Deutsch Algorithm.ipynb | phyjonas/QC | bbb3ace33dc7c5e64ba051c2908ea1fd2f88f4ee | [
"MIT"
] | null | null | null | 30.891775 | 401 | 0.557455 | true | 1,367 | Qwen/Qwen-72B | 1. YES
2. YES | 0.901921 | 0.888759 | 0.80159 | __label__eng_Latn | 0.973545 | 0.700695 |
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