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Sascha Spors,
Professorship Signal Theory and Digital Signal Processing,
Institute of Communications Engineering (INT),
Faculty of Computer Science and Electrical Engineering (IEF),
University of Rostock,
Germany
# Tutorial Digital Signal Processing
**Uniform Quantization, Dithering, Noiseshaping**,
Winter Semester 2... | 69a5735a69c5eefe3674ac76502d340b7de666f4 | 42,573 | ipynb | Jupyter Notebook | quantization/quantization.ipynb | spatialaudio/digital-signal-processing-exercises | 0e16bc05cb8ed3dee0537371dbb5826db21c86b3 | [
"CC-BY-4.0"
] | 13 | 2019-10-24T14:27:43.000Z | 2022-02-22T02:14:43.000Z | quantization/quantization.ipynb | spatialaudio/digital-signal-processing-exercises | 0e16bc05cb8ed3dee0537371dbb5826db21c86b3 | [
"CC-BY-4.0"
] | 2 | 2019-11-05T12:51:46.000Z | 2021-12-17T19:46:19.000Z | quantization/quantization.ipynb | spatialaudio/digital-signal-processing-exercises | 0e16bc05cb8ed3dee0537371dbb5826db21c86b3 | [
"CC-BY-4.0"
] | 6 | 2019-10-24T14:27:51.000Z | 2021-08-06T17:33:24.000Z | 36.387179 | 589 | 0.561929 | true | 8,927 | Qwen/Qwen-72B | 1. YES
2. YES | 0.737158 | 0.779993 | 0.574978 | __label__eng_Latn | 0.940257 | 0.174197 |
# Lecture 2 - Introduction to Probability Theory
> Probability theory is nothing but common sense reduced to calculation. P. Laplace (1812)
## Objectives
+ To use probability theory to represent states of knowledge.
+ To use probability theory to extend Aristotelian logic to reason under uncertainty.
+ To learn about... | 0b9379f5e2cc007f964bf8ad3e9ae1d0d82b2d1e | 25,294 | ipynb | Jupyter Notebook | lectures/lec_02.ipynb | GiveMeData/uq-course | da255bf80ed41cdaca04573ab2dad252cf1ca500 | [
"MIT"
] | 5 | 2018-01-14T00:48:35.000Z | 2021-01-08T01:30:05.000Z | lectures/lec_02.ipynb | GiveMeData/uq-course | da255bf80ed41cdaca04573ab2dad252cf1ca500 | [
"MIT"
] | null | null | null | lectures/lec_02.ipynb | GiveMeData/uq-course | da255bf80ed41cdaca04573ab2dad252cf1ca500 | [
"MIT"
] | 5 | 2018-01-01T14:24:40.000Z | 2021-06-24T22:09:48.000Z | 37.306785 | 510 | 0.537598 | true | 5,909 | Qwen/Qwen-72B | 1. YES
2. YES | 0.692642 | 0.828939 | 0.574158 | __label__eng_Latn | 0.992314 | 0.172291 |
```python
from scipy import stats
from statistics import mean, stdev
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams["font.family"] = "Times New Roman"
import sys
import os
if "../" not in sys.path:
sys.path.append("../")
import os
os.chdir("..")
from envs.data_handler import Dat... | 47972b560c76cbcfc02defa6e55444e50f65a26e | 102,904 | ipynb | Jupyter Notebook | data_analysis/03_shifting.ipynb | hpi-sam/RL_4_Feedback_Control | 7e30e660f426f7f62a740e9fd4dafb32e3222690 | [
"MIT"
] | null | null | null | data_analysis/03_shifting.ipynb | hpi-sam/RL_4_Feedback_Control | 7e30e660f426f7f62a740e9fd4dafb32e3222690 | [
"MIT"
] | 57 | 2022-01-11T08:06:44.000Z | 2022-03-10T10:31:34.000Z | data_analysis/03_shifting.ipynb | hpi-sam/RL_4_Feedback_Control | 7e30e660f426f7f62a740e9fd4dafb32e3222690 | [
"MIT"
] | 1 | 2022-01-06T08:47:09.000Z | 2022-01-06T08:47:09.000Z | 263.181586 | 52,792 | 0.912618 | true | 1,999 | Qwen/Qwen-72B | 1. YES
2. YES | 0.863392 | 0.805632 | 0.695576 | __label__eng_Latn | 0.792992 | 0.454388 |
# Solving Max-Cut Problem with QAOA
<em> Copyright (c) 2021 Institute for Quantum Computing, Baidu Inc. All Rights Reserved. </em>
## Overview
In the [tutorial on Quantum Approximate Optimization Algorithm](./QAOA_EN.ipynb), we talked about how to encode a classical combinatorial optimization problem into a quantum ... | ddebe3addda1166af869d519ae2c4f871aa10660 | 67,248 | ipynb | Jupyter Notebook | tutorial/combinatorial_optimization/MAXCUT_EN.ipynb | gsq7474741/Quantum | 16e7d3bf2dba7e94e6faf5c853faf0e913e1f268 | [
"Apache-2.0"
] | 1 | 2020-07-14T14:10:23.000Z | 2020-07-14T14:10:23.000Z | tutorial/combinatorial_optimization/MAXCUT_EN.ipynb | gsq7474741/Quantum | 16e7d3bf2dba7e94e6faf5c853faf0e913e1f268 | [
"Apache-2.0"
] | null | null | null | tutorial/combinatorial_optimization/MAXCUT_EN.ipynb | gsq7474741/Quantum | 16e7d3bf2dba7e94e6faf5c853faf0e913e1f268 | [
"Apache-2.0"
] | null | null | null | 93.790795 | 15,648 | 0.769019 | true | 5,156 | Qwen/Qwen-72B | 1. YES
2. YES | 0.861538 | 0.795658 | 0.68549 | __label__eng_Latn | 0.966615 | 0.430954 |
```python
import os, sys
import h5py
import numpy as np
from scipy.io import loadmat
import cv2
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from numpy import matrix as mat
from sympy import *
from numpy import linalg as la
```
```python
def getFx(para,... | 419398d133b527499ed6b7355d94028cd05aa0f1 | 265,756 | ipynb | Jupyter Notebook | annot/BA and LM --- 1F6P #3.ipynb | XiaotengLu/Human-Torso-Pose-Estimation | 997990e74e95832cd377922ea7cc43ec50f82ae0 | [
"MIT"
] | null | null | null | annot/BA and LM --- 1F6P #3.ipynb | XiaotengLu/Human-Torso-Pose-Estimation | 997990e74e95832cd377922ea7cc43ec50f82ae0 | [
"MIT"
] | null | null | null | annot/BA and LM --- 1F6P #3.ipynb | XiaotengLu/Human-Torso-Pose-Estimation | 997990e74e95832cd377922ea7cc43ec50f82ae0 | [
"MIT"
] | null | null | null | 291.079956 | 97,228 | 0.903227 | true | 8,914 | Qwen/Qwen-72B | 1. YES
2. YES | 0.899121 | 0.637031 | 0.572768 | __label__kor_Hang | 0.054551 | 0.169062 |
```c++
// Copyright (c) 2020 Patrick Diehl
//
// SPDX-License-Identifier: BSL-1.0
// Distributed under the Boost Software License, Version 1.0. (See accompanying
// file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
```
# Exercise 1: Classical linear elasticity model
Let $\Omega = (0... | 8cd5d2aa1c474aa162c044feca2dc85799442565 | 50,268 | ipynb | Jupyter Notebook | exercise/Exercise1.ipynb | STEllAR-GROUP/HPXjupyterTutorial | 2deeb2086473a5bf12c6f64e794e0a61fb1cf595 | [
"BSL-1.0"
] | 1 | 2021-09-30T13:39:19.000Z | 2021-09-30T13:39:19.000Z | exercise/Exercise1.ipynb | STEllAR-GROUP/HPXjupyterTutorial | 2deeb2086473a5bf12c6f64e794e0a61fb1cf595 | [
"BSL-1.0"
] | null | null | null | exercise/Exercise1.ipynb | STEllAR-GROUP/HPXjupyterTutorial | 2deeb2086473a5bf12c6f64e794e0a61fb1cf595 | [
"BSL-1.0"
] | 1 | 2021-09-30T13:45:40.000Z | 2021-09-30T13:45:40.000Z | 72.120516 | 33,619 | 0.807134 | true | 2,242 | Qwen/Qwen-72B | 1. YES
2. YES
| 0.740174 | 0.800692 | 0.592652 | __label__eng_Latn | 0.889212 | 0.215259 |
# Numpy (Numeric python)
> - 패키지 이름과 같이 **수리적 파이썬 활용**을 위한 파이썬 패키지
> - **선형대수학 구현**과 **과학적 컴퓨팅 연산**을 위한 함수를 제공
> - (key) `nparray` 다차원 배열을 사용하여 **벡터의 산술 연산**이 가능
> - **브로드캐스팅**을 활용하여 shape(형태 혹은 모양)이 다른 데이터의 연산이 가능
>> - 기존 언어에서는 제공 X
>> - 굉장히 파워풀한 기능으로서 빅데이터 연산에 굉장히 효율이 좋음
## Numpy 설치 와 import
> - 선행 학습을 통해 클래스와 ... | 37befcb53e5804c7b7f02bac823d4a10b4affb58 | 62,797 | ipynb | Jupyter Notebook | python/210910-Python-numpy.ipynb | PpangPpang93/TIL | 63e541f223f2317fff06d98cac79f372fd11ed1d | [
"MIT"
] | null | null | null | python/210910-Python-numpy.ipynb | PpangPpang93/TIL | 63e541f223f2317fff06d98cac79f372fd11ed1d | [
"MIT"
] | null | null | null | python/210910-Python-numpy.ipynb | PpangPpang93/TIL | 63e541f223f2317fff06d98cac79f372fd11ed1d | [
"MIT"
] | null | null | null | 21.287119 | 622 | 0.466232 | true | 9,102 | Qwen/Qwen-72B | 1. YES
2. YES | 0.912436 | 0.782662 | 0.71413 | __label__kor_Hang | 0.992799 | 0.497494 |
*Sebastian Raschka*
last modified: 03/31/2014
<hr>
I am really looking forward to your comments and suggestions to improve and extend this tutorial! Just send me a quick note
via Twitter: [@rasbt](https://twitter.com/rasbt)
or Email: [bluewoodtree@gmail.com](mailto:bluewoodtree@gmail.com)
<hr>
### Problem Cate... | a1319cad159a2934145e5d33d3914c4ebdea8394 | 76,655 | ipynb | Jupyter Notebook | tests/others/2_stat_superv_parametric.ipynb | gopala-kr/ds-notebooks | bc35430ecdd851f2ceab8f2437eec4d77cb59423 | [
"MIT"
] | 1 | 2019-05-10T09:16:23.000Z | 2019-05-10T09:16:23.000Z | tests/others/2_stat_superv_parametric.ipynb | gopala-kr/ds-notebooks | bc35430ecdd851f2ceab8f2437eec4d77cb59423 | [
"MIT"
] | null | null | null | tests/others/2_stat_superv_parametric.ipynb | gopala-kr/ds-notebooks | bc35430ecdd851f2ceab8f2437eec4d77cb59423 | [
"MIT"
] | 1 | 2019-10-14T07:30:18.000Z | 2019-10-14T07:30:18.000Z | 170.723831 | 23,708 | 0.886987 | true | 2,810 | Qwen/Qwen-72B | 1. YES
2. YES | 0.785309 | 0.731059 | 0.574107 | __label__eng_Latn | 0.329487 | 0.172172 |
## 유클리드 유사도 vs 코사인 유사도
- 유클리드 유사도 구하는 함수 구현하기
- 코사인 유사도 구하는 함수 구현하기
- 코드설명
- 두 결과 비교
- 응용분야 예시
#### Similarity
```The similairt measure is the measure of how much alike
two data objects are.
Similarity measure in a data mining context is a distance with
dimensions representing features of the objects.
If this di... | df647fd8a7e8e13ed94480c57a98fba34ff8b6d1 | 67,443 | ipynb | Jupyter Notebook | 01_linearalgebra/01_Euclidean Distance & Cosine distance.ipynb | seokyeongheo/study-math-with-python | 18266dc137e46ea299cbd89241e474d7fd610122 | [
"MIT"
] | null | null | null | 01_linearalgebra/01_Euclidean Distance & Cosine distance.ipynb | seokyeongheo/study-math-with-python | 18266dc137e46ea299cbd89241e474d7fd610122 | [
"MIT"
] | null | null | null | 01_linearalgebra/01_Euclidean Distance & Cosine distance.ipynb | seokyeongheo/study-math-with-python | 18266dc137e46ea299cbd89241e474d7fd610122 | [
"MIT"
] | 1 | 2018-06-07T05:57:02.000Z | 2018-06-07T05:57:02.000Z | 63.806055 | 36,248 | 0.744658 | true | 3,926 | Qwen/Qwen-72B | 1. YES
2. YES | 0.936285 | 0.819893 | 0.767654 | __label__eng_Latn | 0.217104 | 0.621849 |
# Laboratório 5: Câncer e Tratamento Químico
### Referente ao capítulo 10
Queremos minimizar a densidade de um tumor em um organismo e os efeitos colaterais das drogas para o tratamento de câncer por quimioterapia em um período de tempo fixo. É assumido que o tumor tenha um crescimento Gompertzian. A hipótese *log-k... | 5a776214ccedf4b2ee94d7f932cfc25f8dbdb0bc | 265,469 | ipynb | Jupyter Notebook | notebooks/.ipynb_checkpoints/Laboratory5-checkpoint.ipynb | lucasmoschen/optimal-control-biological | 642a12b6a3cb351429018120e564b31c320c44c5 | [
"MIT"
] | 1 | 2021-11-03T16:27:39.000Z | 2021-11-03T16:27:39.000Z | notebooks/.ipynb_checkpoints/Laboratory5-checkpoint.ipynb | lucasmoschen/optimal-control-biological | 642a12b6a3cb351429018120e564b31c320c44c5 | [
"MIT"
] | null | null | null | notebooks/.ipynb_checkpoints/Laboratory5-checkpoint.ipynb | lucasmoschen/optimal-control-biological | 642a12b6a3cb351429018120e564b31c320c44c5 | [
"MIT"
] | null | null | null | 539.571138 | 48,892 | 0.941541 | true | 2,538 | Qwen/Qwen-72B | 1. YES
2. YES | 0.853913 | 0.847968 | 0.72409 | __label__por_Latn | 0.967536 | 0.520637 |
```
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import sympy
#from astropy.visualization import astropy_mpl_style, quantity_support
#from google.colab import drive
#drive.mount('/content/drive')
c=299792458
```
Una matriz en python y algunas operaciones. Con esto uds van a c... | c9bd3220e1cc5badfc41eb492edd196101ab4c43 | 3,424 | ipynb | Jupyter Notebook | datos/colabs/Transformaciones de Lorentz.ipynb | Sekilloda/sekilloda.github.io | 1a272eb607400a71a2971569e6ac2426f81661f7 | [
"MIT"
] | null | null | null | datos/colabs/Transformaciones de Lorentz.ipynb | Sekilloda/sekilloda.github.io | 1a272eb607400a71a2971569e6ac2426f81661f7 | [
"MIT"
] | null | null | null | datos/colabs/Transformaciones de Lorentz.ipynb | Sekilloda/sekilloda.github.io | 1a272eb607400a71a2971569e6ac2426f81661f7 | [
"MIT"
] | null | null | null | 3,424 | 3,424 | 0.726343 | true | 665 | Qwen/Qwen-72B | 1. YES
2. YES | 0.956634 | 0.679179 | 0.649726 | __label__spa_Latn | 0.374144 | 0.347861 |
# Linear Regression with Regularization
Regularization is a way to prevent overfitting and allows the model to generalize better. We'll cover the *Ridge* and *Lasso* regression here.
## The Need for Regularization
Unlike polynomial fitting, it's hard to imagine how linear regression can overfit the data, since it's ... | ae3c1d979dcc6c2f9f9c9b741f3938a5b50781ab | 11,357 | ipynb | Jupyter Notebook | blog_content_source/linear_regression/linear_regression_regularized.ipynb | aunnnn/ml-tutorial | b40a6fb04dd4dc560f87486f464b292d84f02fdf | [
"MIT"
] | null | null | null | blog_content_source/linear_regression/linear_regression_regularized.ipynb | aunnnn/ml-tutorial | b40a6fb04dd4dc560f87486f464b292d84f02fdf | [
"MIT"
] | null | null | null | blog_content_source/linear_regression/linear_regression_regularized.ipynb | aunnnn/ml-tutorial | b40a6fb04dd4dc560f87486f464b292d84f02fdf | [
"MIT"
] | null | null | null | 36.400641 | 469 | 0.579378 | true | 2,096 | Qwen/Qwen-72B | 1. YES
2. YES | 0.803174 | 0.91118 | 0.731836 | __label__eng_Latn | 0.999355 | 0.538631 |
# Variablen
Wenn Sie ein neues Jupyter Notebook erstellen, wählen Sie `Python 3.6` als Typ des Notebooks aus.
Innerhalb des Notebooks arbeiten Sie dann mit Python in der Version 3.6. Um zu verstehen, welche Bedeutung Variablen haben, müssen Sie also Variablen in Python 3.6 verstehen.
In Python ist eine Variable ein ... | 36e883b6d78e79d9e4d5e5a6226591b05d222628 | 8,996 | ipynb | Jupyter Notebook | src/03-Variablen_lsg.ipynb | w-meiners/anb-first-steps | 6cb3583f77ae853922acd86fa9e48e9cf5188596 | [
"MIT"
] | null | null | null | src/03-Variablen_lsg.ipynb | w-meiners/anb-first-steps | 6cb3583f77ae853922acd86fa9e48e9cf5188596 | [
"MIT"
] | null | null | null | src/03-Variablen_lsg.ipynb | w-meiners/anb-first-steps | 6cb3583f77ae853922acd86fa9e48e9cf5188596 | [
"MIT"
] | null | null | null | 21.419048 | 290 | 0.521787 | true | 1,224 | Qwen/Qwen-72B | 1. YES
2. YES | 0.882428 | 0.896251 | 0.790877 | __label__deu_Latn | 0.99769 | 0.675805 |
$$
\def\abs#1{\left\lvert #1 \right\rvert}
\def\Set#1{\left\{ #1 \right\}}
\def\mc#1{\mathcal{#1}}
\def\M#1{\boldsymbol{#1}}
\def\R#1{\mathsf{#1}}
\def\RM#1{\boldsymbol{\mathsf{#1}}}
\def\op#1{\operatorname{#1}}
\def\E{\op{E}}
\def\d{\mathrm{\mathstrut d}}
\DeclareMathOperator{\Tr}{Tr}
\DeclareMathOperator*{\argmin}{ar... | ece557c6386cb4ddf4efe5dcfa5fe5fb26961420 | 149,751 | ipynb | Jupyter Notebook | part3/Noisy_logits.ipynb | ccha23/miml | 6a41de1c0bb41d38e3cdc6e9c27363215b7729b9 | [
"MIT"
] | 1 | 2021-08-17T15:16:11.000Z | 2021-08-17T15:16:11.000Z | part3/Noisy_logits.ipynb | ccha23/miml | 6a41de1c0bb41d38e3cdc6e9c27363215b7729b9 | [
"MIT"
] | null | null | null | part3/Noisy_logits.ipynb | ccha23/miml | 6a41de1c0bb41d38e3cdc6e9c27363215b7729b9 | [
"MIT"
] | null | null | null | 220.222059 | 89,968 | 0.892542 | true | 5,863 | Qwen/Qwen-72B | 1. YES
2. YES | 0.875787 | 0.76908 | 0.67355 | __label__eng_Latn | 0.985763 | 0.403214 |
# 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 (Wagner 2002). This equation is state is available using CoolProp with the `Water` fluid.
One ... | 6f06dd9bbc1022316a219d9c7ad0d2c55634f68c | 123,395 | ipynb | Jupyter Notebook | content/properties-pure/reduced-helmholtz.ipynb | msb002/computational-thermo | 9302288217a36e0ce29e320688a3f574921909a5 | [
"CC-BY-4.0",
"BSD-3-Clause"
] | null | null | null | content/properties-pure/reduced-helmholtz.ipynb | msb002/computational-thermo | 9302288217a36e0ce29e320688a3f574921909a5 | [
"CC-BY-4.0",
"BSD-3-Clause"
] | null | null | null | content/properties-pure/reduced-helmholtz.ipynb | msb002/computational-thermo | 9302288217a36e0ce29e320688a3f574921909a5 | [
"CC-BY-4.0",
"BSD-3-Clause"
] | null | null | null | 353.567335 | 87,044 | 0.919624 | true | 2,663 | Qwen/Qwen-72B | 1. YES
2. YES | 0.884039 | 0.746139 | 0.659616 | __label__eng_Latn | 0.63647 | 0.37084 |
**Notas para contenedor de docker:**
Comando de docker para ejecución de la nota de forma local:
nota: cambiar `dir_montar` por la ruta de directorio que se desea mapear a `/datos` dentro del contenedor de docker.
```
dir_montar=<ruta completa de mi máquina a mi directorio>#aquí colocar la ruta al directorio a monta... | 7254c7209156004f5a3b0f2899d5ddbcbf9e4ff3 | 27,737 | ipynb | Jupyter Notebook | Python/clases/2_calculo_DeI/0_modulo_sympy.ipynb | CarlosJChV/Propedeutico | d903192ffa64a7576faace68c2256e69bc11087c | [
"Apache-2.0"
] | 29 | 2019-07-07T07:51:19.000Z | 2022-03-04T18:17:36.000Z | Python/clases/2_calculo_DeI/0_modulo_sympy.ipynb | CarlosJChV/Propedeutico | d903192ffa64a7576faace68c2256e69bc11087c | [
"Apache-2.0"
] | 18 | 2019-06-12T01:15:41.000Z | 2021-08-01T18:20:04.000Z | Python/clases/2_calculo_DeI/0_modulo_sympy.ipynb | CarlosJChV/Propedeutico | d903192ffa64a7576faace68c2256e69bc11087c | [
"Apache-2.0"
] | 102 | 2019-06-07T15:24:05.000Z | 2021-07-27T03:05:41.000Z | 21.173282 | 393 | 0.510077 | true | 2,531 | Qwen/Qwen-72B | 1. YES
2. YES | 0.658418 | 0.865224 | 0.569679 | __label__spa_Latn | 0.721653 | 0.161884 |
```python
from sympy import *
init_printing()
'''
r_GEO = 36000 + 6371 KM
r_LEO = 2000 + 6371 KM
G = 6.674e-11
Me = 5.972e24
'''
M, E = symbols("M E", Functions = True)
e_c, a, G, M_e, r, mu = symbols("e_c a G M_e r mu", Contstants = True)
T_circular, T_elliptical, T_GEO, T_GTO, T_LEO, r_LEO, r_GEO, T_tot = symbols(... | 1e53111d5c0a13baee4c6e7b73cb80cf171ffb77 | 108,030 | ipynb | Jupyter Notebook | Python Notebooks/Hohmann Transfer.ipynb | Yaamani/Satellite-Simulation | f9b3363e79b62a30724c53c99fdb097a68ff324d | [
"MIT"
] | null | null | null | Python Notebooks/Hohmann Transfer.ipynb | Yaamani/Satellite-Simulation | f9b3363e79b62a30724c53c99fdb097a68ff324d | [
"MIT"
] | null | null | null | Python Notebooks/Hohmann Transfer.ipynb | Yaamani/Satellite-Simulation | f9b3363e79b62a30724c53c99fdb097a68ff324d | [
"MIT"
] | null | null | null | 125.762515 | 21,864 | 0.848301 | true | 1,628 | Qwen/Qwen-72B | 1. YES
2. YES | 0.952574 | 0.913677 | 0.870345 | __label__eng_Latn | 0.769415 | 0.860436 |
**This notebook is an exercise in the [Computer Vision](https://www.kaggle.com/learn/computer-vision) course. You can reference the tutorial at [this link](https://www.kaggle.com/ryanholbrook/convolution-and-relu).**
---
# Introduction #
In this exercise, you'll work on building some intuition around feature extra... | adee5c7500430b75308db90d56f0d9efe32fcce4 | 412,969 | ipynb | Jupyter Notebook | Computer_Vision/exercise-convolution-and-relu.ipynb | olonok69/kaggle | bf61ba510c83fd55262939ac6c5a62b7c855ba53 | [
"MIT"
] | null | null | null | Computer_Vision/exercise-convolution-and-relu.ipynb | olonok69/kaggle | bf61ba510c83fd55262939ac6c5a62b7c855ba53 | [
"MIT"
] | null | null | null | Computer_Vision/exercise-convolution-and-relu.ipynb | olonok69/kaggle | bf61ba510c83fd55262939ac6c5a62b7c855ba53 | [
"MIT"
] | null | null | null | 412,969 | 412,969 | 0.960028 | true | 2,085 | Qwen/Qwen-72B | 1. YES
2. YES | 0.779993 | 0.689306 | 0.537653 | __label__eng_Latn | 0.988035 | 0.087479 |
# Semantics: PrefScLTL.
In this notebook, we ensure that semantics of our proposed preference logic are sound.
Proposed semantics:
* $(w_1, w_2) \models \alpha_1~\trianglerighteq~\alpha_2$ iff $w_1 \models \alpha_1$ and $w_2 \models \alpha_2 \land \neg \alpha_1$
We expect the remaining operator semantics to follow... | c3ae95004eb2cdc198f001b9e7fd0ae583162ac9 | 5,763 | ipynb | Jupyter Notebook | jp-notebooks/pref-scltl-semantics.ipynb | abhibp1993/preference-planning | a6384457debee65735eb24eed678f8f98f69d113 | [
"BSD-3-Clause"
] | null | null | null | jp-notebooks/pref-scltl-semantics.ipynb | abhibp1993/preference-planning | a6384457debee65735eb24eed678f8f98f69d113 | [
"BSD-3-Clause"
] | null | null | null | jp-notebooks/pref-scltl-semantics.ipynb | abhibp1993/preference-planning | a6384457debee65735eb24eed678f8f98f69d113 | [
"BSD-3-Clause"
] | null | null | null | 28.529703 | 201 | 0.557175 | true | 1,231 | Qwen/Qwen-72B | 1. YES
2. YES | 0.774583 | 0.737158 | 0.57099 | __label__eng_Latn | 0.856931 | 0.164932 |
# CS-109A Introduction to Data Science
## Lab 11: Neural Network Basics - Introduction to `tf.keras`
**Harvard University**<br>
**Fall 2019**<br>
**Instructors:** Pavlos Protopapas, Kevin Rader, Chris Tanner<br>
**Lab Instructors:** Chris Tanner and Eleni Kaxiras. <br>
**Authors:** Eleni Kaxiras, David Sondak, and... | e35c0849445fd7a49f0d2faade404062704b0ed5 | 558,685 | ipynb | Jupyter Notebook | content/labs/lab11/notes/lab11_MLP_solutions_part1.ipynb | chksi/2019-CS109A | 4b925115f8a0ad5a4f5b95d3d616fabf60bfc3c0 | [
"MIT"
] | null | null | null | content/labs/lab11/notes/lab11_MLP_solutions_part1.ipynb | chksi/2019-CS109A | 4b925115f8a0ad5a4f5b95d3d616fabf60bfc3c0 | [
"MIT"
] | null | null | null | content/labs/lab11/notes/lab11_MLP_solutions_part1.ipynb | chksi/2019-CS109A | 4b925115f8a0ad5a4f5b95d3d616fabf60bfc3c0 | [
"MIT"
] | null | null | null | 300.691604 | 68,408 | 0.921381 | true | 11,338 | Qwen/Qwen-72B | 1. YES
2. YES | 0.754915 | 0.815232 | 0.615431 | __label__eng_Latn | 0.888463 | 0.268183 |
# `sympy`
`scipy` 계열은 [`sympy`](https://www.sympy.org)라는 *기호 처리기*도 포함하고 있다.<br>
`scipy` stack also includes [`sympy`](https://www.sympy.org), a *symbolic processor*.
2006년 이후 2019 까지 800명이 넘는 개발자가 작성한 코드를 제공하였다.<br>
Since 2006, more than 800 developers contributed so far in 2019.
## 기호 연산 예<br>Examples of symb... | e50ac68a04bda1da28083127d607b4e5d0de7770 | 13,280 | ipynb | Jupyter Notebook | 70_sympy/10_sympy.ipynb | kangwonlee/2009eca-nmisp-template | 46a09c988c5e0c4efd493afa965d4a17d32985e8 | [
"BSD-3-Clause"
] | null | null | null | 70_sympy/10_sympy.ipynb | kangwonlee/2009eca-nmisp-template | 46a09c988c5e0c4efd493afa965d4a17d32985e8 | [
"BSD-3-Clause"
] | null | null | null | 70_sympy/10_sympy.ipynb | kangwonlee/2009eca-nmisp-template | 46a09c988c5e0c4efd493afa965d4a17d32985e8 | [
"BSD-3-Clause"
] | null | null | null | 17.382199 | 174 | 0.466265 | true | 1,549 | Qwen/Qwen-72B | 1. YES
2. YES | 0.950411 | 0.884039 | 0.840201 | __label__kor_Hang | 0.668793 | 0.790401 |
# PaBiRoboy dynamic equations
First, import the necessary functions from SymPy that will allow us to construct time varying vectors in the reference frames.
```python
from __future__ import print_function, division
from sympy import symbols, simplify, Matrix
from sympy import trigsimp
from sympy.physics.mechanics im... | 9cca2b44019c33fcf24e519985f511a0801b3ec4 | 147,670 | ipynb | Jupyter Notebook | python/PaBiRoboy_dynamics.ipynb | Roboy/roboy_dynamics | a0a0012bad28029d01b6aead507faeee4509dd62 | [
"BSD-3-Clause"
] | null | null | null | python/PaBiRoboy_dynamics.ipynb | Roboy/roboy_dynamics | a0a0012bad28029d01b6aead507faeee4509dd62 | [
"BSD-3-Clause"
] | null | null | null | python/PaBiRoboy_dynamics.ipynb | Roboy/roboy_dynamics | a0a0012bad28029d01b6aead507faeee4509dd62 | [
"BSD-3-Clause"
] | null | null | null | 81.048299 | 14,543 | 0.455292 | true | 30,303 | Qwen/Qwen-72B | 1. YES
2. YES | 0.917303 | 0.785309 | 0.720366 | __label__kor_Hang | 0.183477 | 0.511982 |
<a href="https://colab.research.google.com/github/robfalck/dymos_tutorial/blob/main/01_dymos_simple_driver_boundary_value_problem.ipynb" target="_parent"></a>
# Dymos: Using an Optimizer to Solve a Simple Boundary Value Problem
In the previous notebook, we demonstrated
- how to install Dymos
- how to define a simple ... | c3b255e1d5fb313dc718cbd98022aa960a12249d | 43,261 | ipynb | Jupyter Notebook | 01_dymos_simple_driver_boundary_value_problem.ipynb | robfalck/dymos_tutorial | 04ec3b4804c601818503b3aa10679a42ab13fece | [
"Apache-2.0"
] | null | null | null | 01_dymos_simple_driver_boundary_value_problem.ipynb | robfalck/dymos_tutorial | 04ec3b4804c601818503b3aa10679a42ab13fece | [
"Apache-2.0"
] | null | null | null | 01_dymos_simple_driver_boundary_value_problem.ipynb | robfalck/dymos_tutorial | 04ec3b4804c601818503b3aa10679a42ab13fece | [
"Apache-2.0"
] | null | null | null | 37.749564 | 493 | 0.513257 | true | 3,272 | Qwen/Qwen-72B | 1. YES
2. YES | 0.699254 | 0.805632 | 0.563342 | __label__eng_Latn | 0.971541 | 0.147162 |
# Modeling Stock Movement
Brian Bahmanyar
***
```python
import numpy as np
import pandas as pd
import scipy.optimize as opt
import seaborn as sns
import sys
sys.path.append('./src/')
from plots import *
```
```python
%matplotlib inline
```
```python
tech = pd.read_csv('data/tech_bundle.csv', index_col=0)
tech.i... | 78a03238bda446e724c8a9d5b313cfbba2341529 | 265,148 | ipynb | Jupyter Notebook | Example-Project/03-LogNormalRandomWalk.ipynb | wileong/data_science_projects_directory | 1a2e018bd6e8e0b97a8b6df1fa074f1a369d4318 | [
"MIT"
] | null | null | null | Example-Project/03-LogNormalRandomWalk.ipynb | wileong/data_science_projects_directory | 1a2e018bd6e8e0b97a8b6df1fa074f1a369d4318 | [
"MIT"
] | null | null | null | Example-Project/03-LogNormalRandomWalk.ipynb | wileong/data_science_projects_directory | 1a2e018bd6e8e0b97a8b6df1fa074f1a369d4318 | [
"MIT"
] | null | null | null | 348.878947 | 83,730 | 0.918012 | true | 3,877 | Qwen/Qwen-72B | 1. YES
2. YES | 0.888759 | 0.851953 | 0.757181 | __label__eng_Latn | 0.672849 | 0.597516 |
# Tutorial
## Regime-Switching Model
`regime_switch_model` is a set of algorithms for learning and inference on regime-switching model. Let $y_t$ be a $p\times 1$ observed time series and $h_t$ be a homogenous and stationary hidden Markov
chain taking values in $\{1, 2, \dots, m\}$ with transition probabilities
\... | df3ebad12407026b9c838ae10451ec067ca44092 | 10,266 | ipynb | Jupyter Notebook | examples/tutorial.ipynb | Liuyi-Hu/regime_switch_model | 1da6ab9cf989f3b6363f628c88138eebf3215277 | [
"BSD-3-Clause"
] | 13 | 2018-04-16T20:44:01.000Z | 2022-03-27T13:03:37.000Z | examples/tutorial.ipynb | arita37/regime_switch_model | 1da6ab9cf989f3b6363f628c88138eebf3215277 | [
"BSD-3-Clause"
] | 2 | 2019-06-29T18:56:13.000Z | 2020-04-06T04:04:57.000Z | examples/tutorial.ipynb | arita37/regime_switch_model | 1da6ab9cf989f3b6363f628c88138eebf3215277 | [
"BSD-3-Clause"
] | 8 | 2018-02-01T07:44:10.000Z | 2021-07-03T12:25:05.000Z | 31.587692 | 349 | 0.504578 | true | 2,293 | Qwen/Qwen-72B | 1. YES
2. YES | 0.926304 | 0.812867 | 0.752962 | __label__eng_Latn | 0.777291 | 0.587715 |
# Bayesian Linear Regression
## What is the problem?
Given inputs $X$ and outputs $\mathbf{y}$, we want to find the best parameters $\boldsymbol{\theta}$, such that predictions $\hat{\mathbf{y}} = X\boldsymbol{\theta}$ can estimate $\mathbf{y}$ very well. In other words, we want L2 norm of errors $||\hat{\mathbf{y}} ... | 0d05335fe186302c056309541eb50d3aca9f16c0 | 5,368 | ipynb | Jupyter Notebook | linear-regression.ipynb | patel-zeel/bayesian-ml | 2b7657f22fbf70953a91b2ab2bc321bb451fa5a5 | [
"MIT"
] | null | null | null | linear-regression.ipynb | patel-zeel/bayesian-ml | 2b7657f22fbf70953a91b2ab2bc321bb451fa5a5 | [
"MIT"
] | null | null | null | linear-regression.ipynb | patel-zeel/bayesian-ml | 2b7657f22fbf70953a91b2ab2bc321bb451fa5a5 | [
"MIT"
] | null | null | null | 47.504425 | 369 | 0.564456 | true | 1,351 | Qwen/Qwen-72B | 1. YES
2. YES | 0.945801 | 0.887205 | 0.839119 | __label__eng_Latn | 0.423247 | 0.787888 |
# Node Embeddings and Skip Gram Examples
**Purpose:** - to explore the node embedding methods used for methods such as Word2Vec.
**Introduction-** one of the key methods used in node classification actually draws inspiration from natural language processing. This based in the fact that one approach for natural langua... | b32e0d1c240dd77a99edca86372a5cf9ee8e0833 | 334,510 | ipynb | Jupyter Notebook | Notes/Node Embeddings and Skip Gram Examples.ipynb | poc1673/ML-for-Networks | 201ca30ab51954a7b1471740eb404b98f1d26213 | [
"MIT"
] | null | null | null | Notes/Node Embeddings and Skip Gram Examples.ipynb | poc1673/ML-for-Networks | 201ca30ab51954a7b1471740eb404b98f1d26213 | [
"MIT"
] | null | null | null | Notes/Node Embeddings and Skip Gram Examples.ipynb | poc1673/ML-for-Networks | 201ca30ab51954a7b1471740eb404b98f1d26213 | [
"MIT"
] | null | null | null | 70.586622 | 1,189 | 0.38754 | true | 141,121 | Qwen/Qwen-72B | 1. YES
2. YES | 0.718594 | 0.721743 | 0.518641 | __label__krc_Cyrl | 0.976854 | 0.043305 |
# Worksheet 5
```
%matplotlib inline
```
## Question 1
Explain when multistep methods such as Adams-Bashforth are useful and when multistage methods such as RK methods are better.
### Answer Question 1
Multistep methods are more computationally efficient (fewer function evaluations) and more accurate than multist... | 2962f79baf57212f929725c9439fd61d611649ff | 153,678 | ipynb | Jupyter Notebook | Worksheets/Worksheet5_Notebook.ipynb | alistairwalsh/NumericalMethods | fa10f9dfc4512ea3a8b54287be82f9511858bd22 | [
"MIT"
] | 1 | 2021-12-01T09:15:04.000Z | 2021-12-01T09:15:04.000Z | Worksheets/Worksheet5_Notebook.ipynb | indranilsinharoy/NumericalMethods | 989e0205565131057c9807ed9d55b6c1a5a38d42 | [
"MIT"
] | null | null | null | Worksheets/Worksheet5_Notebook.ipynb | indranilsinharoy/NumericalMethods | 989e0205565131057c9807ed9d55b6c1a5a38d42 | [
"MIT"
] | 1 | 2021-04-13T02:58:54.000Z | 2021-04-13T02:58:54.000Z | 243.546751 | 39,589 | 0.884967 | true | 4,271 | Qwen/Qwen-72B | 1. YES
2. YES | 0.746139 | 0.887205 | 0.661978 | __label__eng_Latn | 0.869151 | 0.376328 |
```python
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint
```
### O Metódo de Euler Explicito ou Forward Euler
#### Expansão em Taylor de uma função y
Expansão em Série de Taylor de $y(t)$ centrada em $t_0$ é dada por
$$
y(t) = \sum_{n=0}^{\infty} \frac{y^{(n)}(t_0)}{n!}(t-t_0)... | 6d91e40d70885540198327df5fd837201f0950b4 | 858,502 | ipynb | Jupyter Notebook | analise-numerica-edo-2019-1/RK e Eulers.ipynb | mirandagil/university-courses | e70ce5262555e84cffb13e53e139e7eec21e8907 | [
"MIT"
] | 1 | 2019-12-23T16:39:01.000Z | 2019-12-23T16:39:01.000Z | analise-numerica-edo-2019-1/RK e Eulers.ipynb | mirandagil/university-courses | e70ce5262555e84cffb13e53e139e7eec21e8907 | [
"MIT"
] | null | null | null | analise-numerica-edo-2019-1/RK e Eulers.ipynb | mirandagil/university-courses | e70ce5262555e84cffb13e53e139e7eec21e8907 | [
"MIT"
] | null | null | null | 759.064545 | 142,096 | 0.948121 | true | 7,292 | Qwen/Qwen-72B | 1. YES
2. YES | 0.882428 | 0.909907 | 0.802927 | __label__por_Latn | 0.84211 | 0.703802 |
# Principal Component Analysis: lecture
## 1. Introduction
Up until now, We have focused on supervised learning. This group of methods aims at predicting labels based on training data that is labeled as well. Principal Componant Analysis is our first so-called "unsupervised" estimator. Generally, the aim of unsupervi... | 33028a56cf1d2c9d1ad7039ac5d1024d9bdc5ab5 | 33,446 | ipynb | Jupyter Notebook | Principal Component Analysis.ipynb | learn-co-students/ml-pca-staff | cb364402d9cfec4f3064942f9c3cc053b900ecf9 | [
"BSD-4-Clause-UC"
] | 2 | 2018-05-27T21:48:21.000Z | 2018-05-27T21:48:27.000Z | Principal Component Analysis.ipynb | learn-co-students/ml-pca-staff | cb364402d9cfec4f3064942f9c3cc053b900ecf9 | [
"BSD-4-Clause-UC"
] | null | null | null | Principal Component Analysis.ipynb | learn-co-students/ml-pca-staff | cb364402d9cfec4f3064942f9c3cc053b900ecf9 | [
"BSD-4-Clause-UC"
] | null | null | null | 60.810909 | 6,212 | 0.775429 | true | 2,767 | Qwen/Qwen-72B | 1. YES
2. YES | 0.868827 | 0.870597 | 0.756398 | __label__eng_Latn | 0.947634 | 0.595699 |
# Understanding the impact of timing on defaults
> How do delays in recognising defaults impact the apparent profitability of Afterpay?
- toc: true
- badges: true
- comments: true
- categories: [Sympy,Finance,Afterpay]
- image: images/2020-10-03-Afterpay-Customer-Defaults-Part-7/header.png
## The Context
The t... | bef5b31222796ebaec79d9b70dde2edf6ead1bdf | 144,518 | ipynb | Jupyter Notebook | _notebooks/2020-10-03-Afterpay-Customer-Defaults-Part-7.ipynb | CGCooke/Blog | ab1235939011d55674c0888dba4501ff7e4008c6 | [
"Apache-2.0"
] | 1 | 2020-10-29T06:32:23.000Z | 2020-10-29T06:32:23.000Z | _notebooks/2020-10-03-Afterpay-Customer-Defaults-Part-7.ipynb | CGCooke/Blog | ab1235939011d55674c0888dba4501ff7e4008c6 | [
"Apache-2.0"
] | 20 | 2020-04-04T09:39:50.000Z | 2022-03-25T12:30:56.000Z | _notebooks/2020-10-03-Afterpay-Customer-Defaults-Part-7.ipynb | CGCooke/Blog | ab1235939011d55674c0888dba4501ff7e4008c6 | [
"Apache-2.0"
] | null | null | null | 305.534884 | 39,740 | 0.933579 | true | 1,821 | Qwen/Qwen-72B | 1. YES
2. YES | 0.885631 | 0.849971 | 0.752761 | __label__eng_Latn | 0.967538 | 0.587249 |
```python
%matplotlib inline
```
# Visualize the hemodynamic response
In this example, we describe how the hemodynamic response function was
estimated in the previous model. We fit the same ridge model as in the previous
example, and further describe the need to delay the features in time to account
for the delayed ... | f21ff95ce588509223b19c983a23c999eaadde8b | 16,499 | ipynb | Jupyter Notebook | tutorials/notebooks/movies_3T/03_plot_hemodynamic_response.ipynb | gallantlab/voxelwise_tutorials | 3df639dd5fb957410f41b4a3b986c9f903f5333b | [
"BSD-3-Clause"
] | 12 | 2021-09-08T22:22:26.000Z | 2022-02-10T18:06:33.000Z | tutorials/notebooks/movies_3T/03_plot_hemodynamic_response.ipynb | gallantlab/voxelwise_tutorials | 3df639dd5fb957410f41b4a3b986c9f903f5333b | [
"BSD-3-Clause"
] | 2 | 2021-09-11T16:06:44.000Z | 2021-12-16T23:39:40.000Z | tutorials/notebooks/movies_3T/03_plot_hemodynamic_response.ipynb | gallantlab/voxelwise_tutorials | 3df639dd5fb957410f41b4a3b986c9f903f5333b | [
"BSD-3-Clause"
] | 4 | 2021-09-13T19:11:00.000Z | 2022-03-26T04:35:11.000Z | 45.830556 | 1,500 | 0.617492 | true | 2,666 | Qwen/Qwen-72B | 1. YES
2. YES | 0.705785 | 0.72487 | 0.511603 | __label__eng_Latn | 0.984017 | 0.026953 |
```python
%reload_ext autoreload
%aimport trochoid
%autoreload 1
```
```python
import math
import numpy as np
# %matplotlib notebook
import matplotlib.pyplot as plt
plt.style.use('seaborn-colorblind')
plt.style.use('seaborn-whitegrid')
plt.rcParams['figure.figsize'] = 800/72,800/72
plt.rcParams["font.size"] = 21
#... | 78d509b5bc4c1a7bfbd8deba09f8b024307690a9 | 435,722 | ipynb | Jupyter Notebook | demo.ipynb | botamochi6277/trochoid-py | 13efc06c86ed60e4b682d4b5c98d3ee6ad401a25 | [
"MIT"
] | null | null | null | demo.ipynb | botamochi6277/trochoid-py | 13efc06c86ed60e4b682d4b5c98d3ee6ad401a25 | [
"MIT"
] | null | null | null | demo.ipynb | botamochi6277/trochoid-py | 13efc06c86ed60e4b682d4b5c98d3ee6ad401a25 | [
"MIT"
] | null | null | null | 1,081.19603 | 261,132 | 0.954308 | true | 2,240 | Qwen/Qwen-72B | 1. YES
2. YES | 0.863392 | 0.808067 | 0.697678 | __label__yue_Hant | 0.084524 | 0.459272 |
# Mass Maps From Mass-Luminosity Inference Posterior
In this notebook we start to explore the potential of using a mass-luminosity relation posterior to refine mass maps.
Content:
- [Math](#Math)
- [Imports, Constants, Utils, Data](#Imports,-Constants,-Utils,-Data)
- [Probability Functions](#Probability-Functions)
-... | c9c84f045d7357dc237216cb26b7bb1889131558 | 93,243 | ipynb | Jupyter Notebook | MassLuminosityProject/SummerResearch/MassMapsFromMassLuminosity_20170626.ipynb | davidthomas5412/PanglossNotebooks | 719a3b9a5d0e121f0e9bc2a92a968abf7719790f | [
"MIT"
] | null | null | null | MassLuminosityProject/SummerResearch/MassMapsFromMassLuminosity_20170626.ipynb | davidthomas5412/PanglossNotebooks | 719a3b9a5d0e121f0e9bc2a92a968abf7719790f | [
"MIT"
] | 2 | 2016-12-13T02:05:57.000Z | 2017-01-21T02:16:27.000Z | MassLuminosityProject/SummerResearch/MassMapsFromMassLuminosity_20170626.ipynb | davidthomas5412/PanglossNotebooks | 719a3b9a5d0e121f0e9bc2a92a968abf7719790f | [
"MIT"
] | null | null | null | 293.216981 | 68,576 | 0.903768 | true | 2,138 | Qwen/Qwen-72B | 1. YES
2. YES | 0.90053 | 0.654895 | 0.589752 | __label__eng_Latn | 0.57468 | 0.208522 |
```python
import numpy as np
import sympy as sym
x_1, x_2, x_3, x_4 = sym.symbols('x_1, x_2, x_3, x_4')
y_1, y_2, y_3, y_4 = sym.symbols('y_1, y_2, y_3, y_4')
XY = sym.Matrix([sym.symbols('x_1, x_2, x_3, x_4'), sym.symbols('y_1, y_2, y_3, y_4')]).transpose()
xi, eta = sym.symbols('xi, eta')
basis = sym.Matrix([xi, eta... | e11011c15250383c3c63214d0a31e762f294cf01 | 21,573 | ipynb | Jupyter Notebook | equations4twoDimensionalElement.ipynb | AndrewWangJZ/pyfem | 8e7df6aa69c1c761bb8ec67302847e30a83190b4 | [
"MIT"
] | 1 | 2022-03-10T17:22:53.000Z | 2022-03-10T17:22:53.000Z | equations4twoDimensionalElement.ipynb | AndrewWangJZ/pyfem | 8e7df6aa69c1c761bb8ec67302847e30a83190b4 | [
"MIT"
] | null | null | null | equations4twoDimensionalElement.ipynb | AndrewWangJZ/pyfem | 8e7df6aa69c1c761bb8ec67302847e30a83190b4 | [
"MIT"
] | 2 | 2022-03-10T12:47:34.000Z | 2022-03-10T13:25:18.000Z | 41.013308 | 3,835 | 0.46484 | true | 4,981 | Qwen/Qwen-72B | 1. YES
2. YES | 0.957912 | 0.763484 | 0.73135 | __label__yue_Hant | 0.14196 | 0.537504 |
# Descriptive Statistics.
Working with dataset:
- https://archive.ics.uci.edu/ml/datasets/Vertebral+Column
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
```
Vamos a ver aquí algunas medidas iniciales para el análisis de datos. El análisis inicial de los datos es muy importante pa... | eff9a206b7016237a647fea29a2d094c0409c2a3 | 170,671 | ipynb | Jupyter Notebook | Notebooks/descriptive_statistics.ipynb | sierraporta/Data_Mining_Excersices | 3790466d1d8314d83178b61035fc6c28b567ab59 | [
"MIT"
] | null | null | null | Notebooks/descriptive_statistics.ipynb | sierraporta/Data_Mining_Excersices | 3790466d1d8314d83178b61035fc6c28b567ab59 | [
"MIT"
] | null | null | null | Notebooks/descriptive_statistics.ipynb | sierraporta/Data_Mining_Excersices | 3790466d1d8314d83178b61035fc6c28b567ab59 | [
"MIT"
] | null | null | null | 73.155165 | 46,972 | 0.694875 | true | 16,292 | Qwen/Qwen-72B | 1. YES
2. YES | 0.851953 | 0.831143 | 0.708095 | __label__kor_Hang | 0.123892 | 0.483473 |
# Models, Data, Learning Problems
In this lab we start our first data analysis on a concrete problem. We are using Fisher's famous <a href="https://en.wikipedia.org/wiki/Iris_flower_data_set">Iris data set</a>. The goal is to classify flowers from the Iris family into one of three species, that look as follows:
<tabl... | 767802580f170bbd2f134fa0331849daf2be5c93 | 78,015 | ipynb | Jupyter Notebook | Lab2/.ipynb_checkpoints/Models, Data, Learning Problems-checkpoint.ipynb | pratik98/Machine-LearningSummer2020 | ab1ab87c2bd3c9ffb42a88dfb1b93891ed8aa746 | [
"MIT"
] | null | null | null | Lab2/.ipynb_checkpoints/Models, Data, Learning Problems-checkpoint.ipynb | pratik98/Machine-LearningSummer2020 | ab1ab87c2bd3c9ffb42a88dfb1b93891ed8aa746 | [
"MIT"
] | null | null | null | Lab2/.ipynb_checkpoints/Models, Data, Learning Problems-checkpoint.ipynb | pratik98/Machine-LearningSummer2020 | ab1ab87c2bd3c9ffb42a88dfb1b93891ed8aa746 | [
"MIT"
] | null | null | null | 165.286017 | 22,572 | 0.888406 | true | 2,687 | Qwen/Qwen-72B | 1. YES
2. YES | 0.888759 | 0.907312 | 0.806382 | __label__eng_Latn | 0.977139 | 0.711828 |
```python
import pyzx as zx
import sympy
from fractions import Fraction
```
```python
gamma = sympy.Symbol('gamma')
```
```python
g=zx.graph.GraphSym()
v= g.add_vertex(zx.VertexType.Z, qubit=0, row=1, phase=gamma)
w= g.add_vertex(zx.VertexType.Z, qubit=1, row=1, phase=1)
x= g.add_vertex(zx.VertexType.Z, qubit=2, ro... | ab0ae4fb568902f150646992c1dfbda41a1962cd | 3,596 | ipynb | Jupyter Notebook | scratchpads/symbolic_diagrams.ipynb | tobsto/pyzx | 9d92f9163bec91315e60423703fb7a7ad0d96fc8 | [
"Apache-2.0"
] | null | null | null | scratchpads/symbolic_diagrams.ipynb | tobsto/pyzx | 9d92f9163bec91315e60423703fb7a7ad0d96fc8 | [
"Apache-2.0"
] | null | null | null | scratchpads/symbolic_diagrams.ipynb | tobsto/pyzx | 9d92f9163bec91315e60423703fb7a7ad0d96fc8 | [
"Apache-2.0"
] | null | null | null | 27.037594 | 332 | 0.576474 | true | 208 | Qwen/Qwen-72B | 1. YES
2. YES | 0.90053 | 0.685949 | 0.617718 | __label__kor_Hang | 0.192167 | 0.273496 |
Create a reduced basis for a simple sinusoid model
```
%matplotlib inline
import numpy as np
from misc import *
import matplotlib.pyplot as plt
from lalapps import pulsarpputils as pppu
```
Create the signal model:
\begin{equation}
h(t) = \frac{h_0}{2}\left[\frac{1}{2}F_+ (1+\cos{}^2\iota)\cos{\phi_0} + F_{\times}\... | cf113704f31a6e081450678be604e7f6a67ad44d | 245,425 | ipynb | Jupyter Notebook | ROQ/Reduced basis for CW signal model (no frequency range).ipynb | mattpitkin/random_scripts | 8fcfc1d25d8ca7ef66778b7b30be564962e3add3 | [
"MIT"
] | null | null | null | ROQ/Reduced basis for CW signal model (no frequency range).ipynb | mattpitkin/random_scripts | 8fcfc1d25d8ca7ef66778b7b30be564962e3add3 | [
"MIT"
] | null | null | null | ROQ/Reduced basis for CW signal model (no frequency range).ipynb | mattpitkin/random_scripts | 8fcfc1d25d8ca7ef66778b7b30be564962e3add3 | [
"MIT"
] | null | null | null | 88.633081 | 72,003 | 0.800526 | true | 4,438 | Qwen/Qwen-72B | 1. YES
2. YES | 0.863392 | 0.771844 | 0.666403 | __label__eng_Latn | 0.870399 | 0.386609 |
EE 502 P: Analytical Methods for Electrical Engineering
# Homework 1: Python Setup
## Due October 10, 2021 by 11:59 PM
### <span style="color: red">Mayank Kumar</span>
Copyright © 2021, University of Washington
<hr>
**Instructions**: Please use this notebook as a template. Answer all questions using well ... | c5ec5ce4038952500bb6fd46a3be752742e5f055 | 59,648 | ipynb | Jupyter Notebook | Basics/HW_01_Python_EEP502_Mayank Kumar.ipynb | krmayankb/Analytical_Methods | a1bf58e1b9056949f5aa0fb25070e2d0ffbf5c4f | [
"MIT"
] | null | null | null | Basics/HW_01_Python_EEP502_Mayank Kumar.ipynb | krmayankb/Analytical_Methods | a1bf58e1b9056949f5aa0fb25070e2d0ffbf5c4f | [
"MIT"
] | null | null | null | Basics/HW_01_Python_EEP502_Mayank Kumar.ipynb | krmayankb/Analytical_Methods | a1bf58e1b9056949f5aa0fb25070e2d0ffbf5c4f | [
"MIT"
] | null | null | null | 95.743178 | 38,892 | 0.810002 | true | 4,156 | Qwen/Qwen-72B | 1. YES
2. YES | 0.785309 | 0.798187 | 0.626823 | __label__eng_Latn | 0.94997 | 0.29465 |
# Selección óptima de portafolios II
Entonces, tenemos que:
- La LAC describe las posibles selecciones de riesgo-rendimiento entre un activo libre de riesgo y un activo riesgoso.
- Su pendiente es igual al radio de Sharpe del activo riesgoso.
- La asignación óptima de capital para cualquier inversionista es el punto... | ef4a4284245f8350e3dbb12ffdd0567ea76fab56 | 98,645 | ipynb | Jupyter Notebook | Modulo3/Clase14_SeleccionOptimaPortII.ipynb | if722399/porinvo2021 | 19ee9421b806f711c71b2affd1633bbfba40a9eb | [
"MIT"
] | null | null | null | Modulo3/Clase14_SeleccionOptimaPortII.ipynb | if722399/porinvo2021 | 19ee9421b806f711c71b2affd1633bbfba40a9eb | [
"MIT"
] | null | null | null | Modulo3/Clase14_SeleccionOptimaPortII.ipynb | if722399/porinvo2021 | 19ee9421b806f711c71b2affd1633bbfba40a9eb | [
"MIT"
] | null | null | null | 74.787718 | 32,600 | 0.77269 | true | 7,105 | Qwen/Qwen-72B | 1. YES
2. YES | 0.658418 | 0.835484 | 0.550097 | __label__spa_Latn | 0.676811 | 0.116389 |
# Week 2 worksheet 3: Gaussian elimination
This notebook was created by Charlotte Desvages.
Gaussian elimination is a direct method to solve linear systems of the form $Ax = b$, with $A \in \mathbb{R}^{n\times n}$ and $b \in \mathbb{R}^n$, to find the unknown $x \in \mathbb{R}^n$. This week, we put what we have seen ... | 031b6b6b2db1e915d9224a4d190df4e81a503f8f | 28,072 | ipynb | Jupyter Notebook | Workshops/W02-W3_NMfCE_Gaussian_elimination.ipynb | DrFriedrich/nmfce-2021-22 | 2ccee5a97b24bd5c1e80e531957240ffb7163897 | [
"MIT"
] | null | null | null | Workshops/W02-W3_NMfCE_Gaussian_elimination.ipynb | DrFriedrich/nmfce-2021-22 | 2ccee5a97b24bd5c1e80e531957240ffb7163897 | [
"MIT"
] | null | null | null | Workshops/W02-W3_NMfCE_Gaussian_elimination.ipynb | DrFriedrich/nmfce-2021-22 | 2ccee5a97b24bd5c1e80e531957240ffb7163897 | [
"MIT"
] | null | null | null | 35.534177 | 568 | 0.555215 | true | 5,730 | Qwen/Qwen-72B | 1. YES
2. YES | 0.893309 | 0.903294 | 0.806921 | __label__eng_Latn | 0.992964 | 0.713081 |
```python
'''import numpy as np
import math
import sympy
import sympy as sp
from sympy import Eq, IndexedBase, symbols, Idx, Indexed, Sum, S, N
from sympy.functions.special.tensor_functions import KroneckerDelta
from sympy.vector import Vector, CoordSys3D, AxisOrienter, BodyOrienter, Del, curl, divergence, gradient, is... | f1364f9bfba7c6242df4854ee74b9b3b8f8ade39 | 39,526 | ipynb | Jupyter Notebook | .ipynb_checkpoints/Robotics-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks | 91a1d98354a270d214316eba21e4a435b3e17f5d | [
"MIT"
] | null | null | null | .ipynb_checkpoints/Robotics-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks | 91a1d98354a270d214316eba21e4a435b3e17f5d | [
"MIT"
] | null | null | null | .ipynb_checkpoints/Robotics-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks | 91a1d98354a270d214316eba21e4a435b3e17f5d | [
"MIT"
] | null | null | null | 86.11329 | 27,536 | 0.831579 | true | 1,096 | Qwen/Qwen-72B | 1. YES
2. YES | 0.932453 | 0.826712 | 0.77087 | __label__eng_Latn | 0.362223 | 0.629322 |
| |Pierre Proulx, ing, professeur|
|:---|:---|
|Département de génie chimique et de génie biotechnologique |** GCH200-Phénomènes d'échanges I **|
### Section10.4, chauffage par effet Brinkman: source de chauffage causé par la viscosité
>
>> Ici on traitera le problème de façon légèrement différente de Transport Phen... | 9fc8807afd7b991f7f0e53625a5b3166f1535334 | 72,584 | ipynb | Jupyter Notebook | Chap-10-Section-10-4.ipynb | pierreproulx/GCH200 | 66786aa96ceb2124b96c93ee3d928a295f8e9a03 | [
"MIT"
] | 1 | 2018-02-26T16:29:58.000Z | 2018-02-26T16:29:58.000Z | Chap-10-Section-10-4.ipynb | pierreproulx/GCH200 | 66786aa96ceb2124b96c93ee3d928a295f8e9a03 | [
"MIT"
] | null | null | null | Chap-10-Section-10-4.ipynb | pierreproulx/GCH200 | 66786aa96ceb2124b96c93ee3d928a295f8e9a03 | [
"MIT"
] | 2 | 2018-02-27T15:04:33.000Z | 2021-06-03T16:38:07.000Z | 222.650307 | 51,672 | 0.902596 | true | 1,130 | Qwen/Qwen-72B | 1. YES
2. YES | 0.882428 | 0.835484 | 0.737254 | __label__fra_Latn | 0.840327 | 0.55122 |
```julia
using MomentClosure, Latexify, OrdinaryDiffEq, Catalyst
```
┌ Info: Precompiling MomentClosure [01a1b25a-ecf0-48c5-ae58-55bfd5393600]
└ @ Base loading.jl:1278
$$ G \stackrel{c_1}{\rightarrow} G+P, \\
G^* \stackrel{c_2}{\rightarrow} G^*+P, \\
P \stackrel{c_3}{\rightarrow} 0 \\
G+P \underset{... | fb7923c865a0af7e118573c7723fd305ec0182f1 | 435,385 | ipynb | Jupyter Notebook | examples/conditional_TEST_J.ipynb | palmtree2013/MomentClosure.jl | 171d18818657bf1e93240e4b24d393fac3eea72d | [
"MIT"
] | 27 | 2021-02-21T00:44:05.000Z | 2022-03-25T23:48:52.000Z | examples/conditional_TEST_J.ipynb | palmtree2013/MomentClosure.jl | 171d18818657bf1e93240e4b24d393fac3eea72d | [
"MIT"
] | 10 | 2021-02-26T15:44:04.000Z | 2022-03-16T12:48:27.000Z | examples/conditional_TEST_J.ipynb | palmtree2013/MomentClosure.jl | 171d18818657bf1e93240e4b24d393fac3eea72d | [
"MIT"
] | 3 | 2021-02-21T01:20:10.000Z | 2022-03-24T13:18:07.000Z | 192.73351 | 25,709 | 0.679396 | true | 5,708 | Qwen/Qwen-72B | 1. YES
2. YES | 0.870597 | 0.793106 | 0.690476 | __label__eng_Latn | 0.306386 | 0.442538 |
```python
import sys
if not '..' in sys.path:
sys.path.insert(0, '..')
import control
import sympy
import numpy as np
import matplotlib.pyplot as plt
import ulog_tools as ut
import ulog_tools.control_opt as opt
%matplotlib inline
%load_ext autoreload
%autoreload 2
```
The autoreload extension is already loade... | aa968dd62ad677f36cf9425507b2e8b35e29dbb9 | 5,975 | ipynb | Jupyter Notebook | notebooks/old/System ID.ipynb | dronecrew/ulog_tools | 65aabdf8c729d8307b6bab2e0897ecf2a222d6bb | [
"BSD-3-Clause"
] | 13 | 2017-07-07T15:26:45.000Z | 2021-01-29T11:00:37.000Z | notebooks/old/System ID.ipynb | dronecrew/ulog_tools | 65aabdf8c729d8307b6bab2e0897ecf2a222d6bb | [
"BSD-3-Clause"
] | 8 | 2017-09-05T18:56:57.000Z | 2021-09-12T09:35:19.000Z | notebooks/old/System ID.ipynb | dronecrew/ulog_tools | 65aabdf8c729d8307b6bab2e0897ecf2a222d6bb | [
"BSD-3-Clause"
] | 5 | 2017-07-03T19:48:30.000Z | 2021-07-06T14:26:27.000Z | 27.036199 | 521 | 0.546109 | true | 638 | Qwen/Qwen-72B | 1. YES
2. YES | 0.903294 | 0.798187 | 0.720998 | __label__yue_Hant | 0.121748 | 0.51345 |
# Jupyter notebook example
## Simple plots
Loading the necessary modules (maybe _numpy_ is superceeded by _scipy_)
```python
import numpy as npy
import scipy as scy
# import sympy as spy
# import timeit
```
The sine function, $\sin(t)$, and its Fourier-transformation.
$$f_{max}=\frac1{2\Delta t},\quad \Delta f=\fr... | 07c24d3ae27da03a61f5c9414a3344b13009e817 | 146,024 | ipynb | Jupyter Notebook | python_ex1.ipynb | szazs89/jupyter_ex | 366079f54a8ac8f3d6e65d45ec79d4b2318bed40 | [
"MIT"
] | null | null | null | python_ex1.ipynb | szazs89/jupyter_ex | 366079f54a8ac8f3d6e65d45ec79d4b2318bed40 | [
"MIT"
] | null | null | null | python_ex1.ipynb | szazs89/jupyter_ex | 366079f54a8ac8f3d6e65d45ec79d4b2318bed40 | [
"MIT"
] | null | null | null | 568.18677 | 122,772 | 0.95046 | true | 814 | Qwen/Qwen-72B | 1. YES
2. YES | 0.927363 | 0.763484 | 0.708027 | __label__eng_Latn | 0.205814 | 0.483315 |
<a id='heavy-tails'></a>
<div id="qe-notebook-header" align="right" style="text-align:right;">
<a href="https://quantecon.org/" title="quantecon.org">
</a>
</div>
# Heavy-Tailed Distributions
<a id='index-0'></a>
## Contents
- [Heavy-Tailed Distributions](#Heavy-Tailed-Distribution... | 5c0a12343a1d2353c65f8c6a0b927aad8f590457 | 175,127 | ipynb | Jupyter Notebook | homework/HarveyT47/heavy_tails.ipynb | QuantEcon/summer_course_2019 | 8715ce171dbe371aac44bb7cda00c44aea8a8690 | [
"BSD-3-Clause"
] | 7 | 2019-11-01T06:33:00.000Z | 2020-03-20T10:28:26.000Z | homework/HarveyT47/heavy_tails.ipynb | QuantEcon/summer_course_2019 | 8715ce171dbe371aac44bb7cda00c44aea8a8690 | [
"BSD-3-Clause"
] | 4 | 2019-12-14T07:26:59.000Z | 2019-12-20T06:03:28.000Z | homework/HarveyT47/heavy_tails.ipynb | QuantEcon/summer_course_2019 | 8715ce171dbe371aac44bb7cda00c44aea8a8690 | [
"BSD-3-Clause"
] | 14 | 2019-12-14T07:08:04.000Z | 2021-11-17T13:48:56.000Z | 190.97819 | 50,972 | 0.897794 | true | 5,770 | Qwen/Qwen-72B | 1. YES
2. YES | 0.651355 | 0.7773 | 0.506298 | __label__eng_Latn | 0.980917 | 0.014629 |
# Task Three: Quantum Gates and Circuits
```python
from qiskit import *
from qiskit.visualization import plot_bloch_multivector
```
## Pauli Matrices
\begin{align}
I = \begin{pmatrix} 1&0 \\ 0&1 \end{pmatrix}, \quad
X = \begin{pmatrix} 0&1 \\ 1&0 \end{pmatrix}, \quad
Y = \begin{pmatrix} 0&i \\ -i&0 \end{pmatrix}, \... | f21e707097ed88f3b52fd3bce07b1d3b12d40d34 | 14,939 | ipynb | Jupyter Notebook | Guided Project - Programming a Quantum Computer with Qiskit - IBM SDK/Task 3/Task 3 - Quantum Gates and Circuits.ipynb | bobsub218/exercise-qubit | 32e1b851f65b98dcdf90ceaca1bd52ac6553e63a | [
"MIT"
] | 229 | 2020-11-13T07:11:20.000Z | 2022-03-06T02:27:45.000Z | Guided Project - Programming a Quantum Computer with Qiskit - IBM SDK/Task 3/Task 3 - Quantum Gates and Circuits.ipynb | bobsub218/Exercise-qubit | 32e1b851f65b98dcdf90ceaca1bd52ac6553e63a | [
"MIT"
] | 6 | 2020-12-25T17:25:14.000Z | 2021-04-26T07:56:06.000Z | Guided Project - Programming a Quantum Computer with Qiskit - IBM SDK/Task 3/Task 3 - Quantum Gates and Circuits.ipynb | trial1user/Quantum-Computing-Collection-Of-Resources | 6953854769adef05e705fa315017c459e5e581ce | [
"MIT"
] | 50 | 2020-11-13T08:55:28.000Z | 2022-03-14T21:16:07.000Z | 49.963211 | 2,876 | 0.762233 | true | 854 | Qwen/Qwen-72B | 1. YES
2. YES | 0.90053 | 0.835484 | 0.752378 | __label__eng_Latn | 0.468875 | 0.586358 |
# Robust Data-Driven Portfolio Diversification
### Francisco A. Ibanez
1. RPCA on the sample
2. Singular Value Hard Thresholding (SVHT)
3. Truncated SVD
4. Maximize portfolio effective bets - regualization, s.t.:
- Positivity constraint
- Leverage 1x
The combination of (1), (2), and (3) should limit the poss... | 78043cf83883c1f7b5ef0baa28b812b0c49cbcd2 | 50,612 | ipynb | Jupyter Notebook | notebooks/spectral_diversification.ipynb | fcoibanez/eigenportfolio | 6e0f6c0239448a191aecf9137d545abf12cb344e | [
"MIT"
] | null | null | null | notebooks/spectral_diversification.ipynb | fcoibanez/eigenportfolio | 6e0f6c0239448a191aecf9137d545abf12cb344e | [
"MIT"
] | null | null | null | notebooks/spectral_diversification.ipynb | fcoibanez/eigenportfolio | 6e0f6c0239448a191aecf9137d545abf12cb344e | [
"MIT"
] | null | null | null | 94.779026 | 9,902 | 0.833083 | true | 2,771 | Qwen/Qwen-72B | 1. YES
2. YES | 0.931463 | 0.884039 | 0.823449 | __label__eng_Latn | 0.603432 | 0.751482 |
# The Laplace Transform
*This Jupyter notebook is part of a [collection of notebooks](../index.ipynb) in the bachelors module Signals and Systems, Communications Engineering, Universität Rostock. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*
## Theorems... | 0bbfef3925f7053ab51bba7bf92a730d9da6ed2a | 169,627 | ipynb | Jupyter Notebook | laplace_transform/theorems.ipynb | swchao/signalsAndSystemsLecture | 7f135d091499e1d3d635bac6ddf22adee15454f8 | [
"MIT"
] | 3 | 2019-01-27T12:39:27.000Z | 2022-03-15T10:26:12.000Z | laplace_transform/theorems.ipynb | swchao/signalsAndSystemsLecture | 7f135d091499e1d3d635bac6ddf22adee15454f8 | [
"MIT"
] | null | null | null | laplace_transform/theorems.ipynb | swchao/signalsAndSystemsLecture | 7f135d091499e1d3d635bac6ddf22adee15454f8 | [
"MIT"
] | 2 | 2020-09-18T06:26:48.000Z | 2021-12-10T06:11:45.000Z | 276.265472 | 78,568 | 0.898807 | true | 5,208 | Qwen/Qwen-72B | 1. YES
2. YES | 0.695958 | 0.859664 | 0.59829 | __label__eng_Latn | 0.972015 | 0.228359 |
# Calculating Times of Rise, Set, and Culmination
Suppose we want to calculate when a given celestial object rises above the horizon, sets below the horizon, or reaches the highest point above the horizon (*culminates*), as seen by an observer at a given location on the surface of the Earth.
### Azimuth and altitude
... | 8623a62f24e213acf4ab2113f6c244d3718f4782 | 11,518 | ipynb | Jupyter Notebook | theory/rise_set_culm.ipynb | matheo/astronomy | 3a1d4ea47a0c04d83bd8ede43dc564e956e999fe | [
"MIT"
] | null | null | null | theory/rise_set_culm.ipynb | matheo/astronomy | 3a1d4ea47a0c04d83bd8ede43dc564e956e999fe | [
"MIT"
] | null | null | null | theory/rise_set_culm.ipynb | matheo/astronomy | 3a1d4ea47a0c04d83bd8ede43dc564e956e999fe | [
"MIT"
] | null | null | null | 67.752941 | 597 | 0.647074 | true | 2,498 | Qwen/Qwen-72B | 1. YES
2. YES | 0.921922 | 0.795658 | 0.733535 | __label__eng_Latn | 0.999135 | 0.542578 |
<table>
<tr>
<td>Auhor:</td>
<td>Zlatko Minev </td>
</tr>
<tr>
<td>Purpose:</td>
<td>Demonstrate some of the basic conversion and tools in toolbox_circuits <br>
These are just basic utility functions
</td>
</tr>
<td>File Status:</td>
... | 46adf3e5ce7ce5a05595cf0e158f433308dc59ae | 7,818 | ipynb | Jupyter Notebook | Updated pyEPR Files/pyEPR/_tutorial_notebooks/Tutorial 3. toolbox_circuits.ipynb | circuitqed/Automated-RF-Design-Demo-MASTER | d92bd57447ecce901dc5a4d527205acecb617268 | [
"BSD-3-Clause"
] | null | null | null | Updated pyEPR Files/pyEPR/_tutorial_notebooks/Tutorial 3. toolbox_circuits.ipynb | circuitqed/Automated-RF-Design-Demo-MASTER | d92bd57447ecce901dc5a4d527205acecb617268 | [
"BSD-3-Clause"
] | null | null | null | Updated pyEPR Files/pyEPR/_tutorial_notebooks/Tutorial 3. toolbox_circuits.ipynb | circuitqed/Automated-RF-Design-Demo-MASTER | d92bd57447ecce901dc5a4d527205acecb617268 | [
"BSD-3-Clause"
] | null | null | null | 23.835366 | 151 | 0.467639 | true | 1,317 | Qwen/Qwen-72B | 1. YES
2. YES | 0.83762 | 0.766294 | 0.641863 | __label__yue_Hant | 0.299086 | 0.329593 |
```python
import numpy as np
```
### Matrices
```python
matrix_01 = np.matrix("1, 2, 3; 4, 5, 6"); matrix_01
```
matrix([[1, 2, 3],
[4, 5, 6]])
```python
matrix_02 = np.matrix([[1, 2, 3], [4, 5, 6]]); matrix_02
```
matrix([[1, 2, 3],
[4, 5, 6]])
### Math Operations with... | b801552fa4e6a7665d76c23257067f5306883fc5 | 26,029 | ipynb | Jupyter Notebook | modules/02-data-organization-and-visualization/06-numpy-array-matrix-math-operations.ipynb | cfascina/rtaps | d54c83a2100ac3a300041e2d589c86e5ca0c4a8e | [
"MIT"
] | 2 | 2020-07-27T14:25:23.000Z | 2020-12-02T22:12:03.000Z | modules/02-data-organization-and-visualization/06-numpy-array-matrix-math-operations.ipynb | cfascina/rtaps | d54c83a2100ac3a300041e2d589c86e5ca0c4a8e | [
"MIT"
] | null | null | null | modules/02-data-organization-and-visualization/06-numpy-array-matrix-math-operations.ipynb | cfascina/rtaps | d54c83a2100ac3a300041e2d589c86e5ca0c4a8e | [
"MIT"
] | null | null | null | 68.6781 | 19,140 | 0.826732 | true | 677 | Qwen/Qwen-72B | 1. YES
2. YES | 0.941654 | 0.944177 | 0.889088 | __label__eng_Latn | 0.401105 | 0.903983 |
# The Winding Number and the SSH model
The Chern number isn't the only topological invariant. We have multiple invariants, each convenient in their own situations. The Chern number just happened to appear one of the biggest, early examples, the Integer Quantum Hall Effect, but the winding number actually occurs much... | 8f2b8a7792d0e1a93d5cf0d42963912504eec281 | 415,363 | ipynb | Jupyter Notebook | Graduate/Winding-Number.ipynb | IanHawke/M4 | 2d841d4eb38f3d09891ed3c84e49858d30f2d4d4 | [
"MIT"
] | null | null | null | Graduate/Winding-Number.ipynb | IanHawke/M4 | 2d841d4eb38f3d09891ed3c84e49858d30f2d4d4 | [
"MIT"
] | null | null | null | Graduate/Winding-Number.ipynb | IanHawke/M4 | 2d841d4eb38f3d09891ed3c84e49858d30f2d4d4 | [
"MIT"
] | null | null | null | 105.717231 | 564 | 0.64623 | true | 5,353 | Qwen/Qwen-72B | 1. YES
2. YES | 0.882428 | 0.749087 | 0.661015 | __label__eng_Latn | 0.990825 | 0.374091 |
# Fitting a Mixture Model with Gibbs Sampling
```python
%matplotlib inline
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
from scipy import stats
from collections import namedtuple, Counter
```
Suppose we receive some data that looks like the following:
```python
data = pd.Se... | c7a6bda22c51039fc6e1eace14a2f4f3b0786f43 | 71,843 | ipynb | Jupyter Notebook | pages/2015-09-02-fitting-a-mixture-model.ipynb | tdhopper/notes-on-dirichlet-processes | 6efb736ca7f65cb4a51f99494d6fcf6709395cd7 | [
"MIT"
] | 438 | 2015-08-06T13:32:35.000Z | 2022-03-05T03:20:44.000Z | pages/2015-09-02-fitting-a-mixture-model.ipynb | tdhopper/notes-on-dirichlet-processes | 6efb736ca7f65cb4a51f99494d6fcf6709395cd7 | [
"MIT"
] | 2 | 2015-10-13T17:10:18.000Z | 2018-07-18T14:37:21.000Z | pages/2015-09-02-fitting-a-mixture-model.ipynb | tdhopper/notes-on-dirichlet-processes | 6efb736ca7f65cb4a51f99494d6fcf6709395cd7 | [
"MIT"
] | 134 | 2015-08-26T03:59:12.000Z | 2021-09-10T02:45:44.000Z | 96.693136 | 10,094 | 0.800454 | true | 4,841 | Qwen/Qwen-72B | 1. YES
2. YES | 0.903294 | 0.914901 | 0.826425 | __label__eng_Latn | 0.930901 | 0.758394 |
$
\begin{align}
a_1&=b_1+c_1 \tag{1}\\
a_2&=b_2+c_2+d_2 \tag{2}\\
a_3&=b_3+c_3 \tag{3}
\end{align}
$
[Euler](https://krasjet.github.io/quaternion/bonus_gimbal_lock.pdf)
[Quaternion](https://krasjet.github.io/quaternion/bonus_gimbal_lock.pdf)
[Source](https://github.com/Krasjet/quaternion)
```python
```
```p... | ca1b0a8eb933221f411e2731de800fae2f930bd0 | 1,368 | ipynb | Jupyter Notebook | Doc/Jupyter Notebook/Math_2.ipynb | Alpha255/Rockcat | f04124b17911fb6148512dd8fb260bd84702ffc1 | [
"MIT"
] | null | null | null | Doc/Jupyter Notebook/Math_2.ipynb | Alpha255/Rockcat | f04124b17911fb6148512dd8fb260bd84702ffc1 | [
"MIT"
] | null | null | null | Doc/Jupyter Notebook/Math_2.ipynb | Alpha255/Rockcat | f04124b17911fb6148512dd8fb260bd84702ffc1 | [
"MIT"
] | null | null | null | 18.486486 | 81 | 0.505117 | true | 129 | Qwen/Qwen-72B | 1. YES
2. YES | 0.822189 | 0.689306 | 0.56674 | __label__yue_Hant | 0.298697 | 0.155056 |
# 14 Linear Algebra: Singular Value Decomposition
One can always decompose a matrix $\mathsf{A}$
\begin{gather}
\mathsf{A} = \mathsf{U}\,\text{diag}(w_j)\,\mathsf{V}^{T}\\
\mathsf{U}^T \mathsf{U} = \mathsf{U} \mathsf{U}^T = 1\\
\mathsf{V}^T \mathsf{V} = \mathsf{V} \mathsf{V}^T = 1
\end{gather}
where $\mathsf{U}$ an... | bbbe07ee5de06bd6ba2212b0e5977dac1b7a5df7 | 153,734 | ipynb | Jupyter Notebook | 14_linear_algebra/14_SVD.ipynb | nachrisman/PHY494 | bac0dd5a7fe6f59f9e2ccaee56ebafcb7d97e2e7 | [
"CC-BY-4.0"
] | null | null | null | 14_linear_algebra/14_SVD.ipynb | nachrisman/PHY494 | bac0dd5a7fe6f59f9e2ccaee56ebafcb7d97e2e7 | [
"CC-BY-4.0"
] | null | null | null | 14_linear_algebra/14_SVD.ipynb | nachrisman/PHY494 | bac0dd5a7fe6f59f9e2ccaee56ebafcb7d97e2e7 | [
"CC-BY-4.0"
] | null | null | null | 97.361621 | 66,442 | 0.844777 | true | 5,706 | Qwen/Qwen-72B | 1. YES
2. YES | 0.945801 | 0.803174 | 0.759643 | __label__eng_Latn | 0.761403 | 0.603237 |
```python
from IPython.core.display import HTML
css_file = './custom.css'
HTML(open(css_file, "r").read())
```
###### Content provided under a Creative Commons Attribution license, CC-BY 4.0; code under MIT License. (c)2015 [David I. Ketcheson](http://davidketcheson.info)
##### Version 0.2 - May 2021
```python
impo... | 9695a926b9101383fd9c5182f7cfbe495c40ab02 | 19,726 | ipynb | Jupyter Notebook | PSPython_03-FFT-aliasing-filtering.ipynb | ketch/PseudoSpectralPython | 382894906cfa3ded504f7f3393e139957c147022 | [
"MIT"
] | 20 | 2016-07-11T07:52:30.000Z | 2022-03-15T00:29:15.000Z | PSPython_03-FFT-aliasing-filtering.ipynb | chenjied/PseudoSpectralPython | 382894906cfa3ded504f7f3393e139957c147022 | [
"MIT"
] | null | null | null | PSPython_03-FFT-aliasing-filtering.ipynb | chenjied/PseudoSpectralPython | 382894906cfa3ded504f7f3393e139957c147022 | [
"MIT"
] | 13 | 2017-02-08T00:58:59.000Z | 2022-03-27T17:29:09.000Z | 40.01217 | 631 | 0.593886 | true | 4,057 | Qwen/Qwen-72B | 1. YES
2. YES | 0.727975 | 0.879147 | 0.639997 | __label__eng_Latn | 0.992469 | 0.325259 |
```python
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import numpy as np
np.set_printoptions(precision=3)
np.set_printoptions(suppress=True)
```
# Neural Networks
### Interpreting the linear function as a neural network
In the last example we tried to classify our data into two cat... | 35539646ec640bc1f70a5a1030e7e2c9ed65277a | 231,587 | ipynb | Jupyter Notebook | 06. Neural Networks.ipynb | Mistrymm7/machineintelligence | 7629d61d46dafa8e5f3013082b1403813d165375 | [
"Apache-2.0"
] | 82 | 2019-09-23T11:25:41.000Z | 2022-03-29T22:56:10.000Z | 06. Neural Networks.ipynb | Iason-Giraud/machineintelligence | b34a070208c7ac7d7b8a1e1ad02813b39274921c | [
"Apache-2.0"
] | null | null | null | 06. Neural Networks.ipynb | Iason-Giraud/machineintelligence | b34a070208c7ac7d7b8a1e1ad02813b39274921c | [
"Apache-2.0"
] | 31 | 2019-09-30T16:08:46.000Z | 2022-02-19T10:29:07.000Z | 187.216653 | 31,508 | 0.897883 | true | 6,768 | Qwen/Qwen-72B | 1. YES
2. YES | 0.865224 | 0.7773 | 0.672539 | __label__eng_Latn | 0.989551 | 0.400864 |
# Fixed Coefficients Random Utility (Demand) Estimation
This notebook reviews the estimation and inference of a **linear** random utility model when the agent is facing a finite number of alternatives.
## Introduction
Consider a set of $J+1$ alternatives $\{0,1,2,...,J\}$. The utility that decision maker (DM) $i$ rec... | 111668a6a6abfa59b86c87e406f630220d75907a | 97,716 | ipynb | Jupyter Notebook | Berry.ipynb | econjinkim/econjinkim.github.io | 745ca0eebc8e8e5271722d96deb11c829fce435d | [
"MIT"
] | 1 | 2021-02-18T15:44:42.000Z | 2021-02-18T15:44:42.000Z | Berry.ipynb | econgenekim/econjinkim.github.io | 669ee5038a4a138fc6c3ada9ce5e3cff8ea22136 | [
"MIT"
] | null | null | null | Berry.ipynb | econgenekim/econjinkim.github.io | 669ee5038a4a138fc6c3ada9ce5e3cff8ea22136 | [
"MIT"
] | null | null | null | 74.25228 | 7,637 | 0.491793 | true | 21,638 | Qwen/Qwen-72B | 1. YES
2. YES | 0.793106 | 0.715424 | 0.567407 | __label__kor_Hang | 0.162518 | 0.156606 |
## Tracking Error Minimization
---
### Passive Management Vs. Active Management
+ So far we have reviewed how to manage our portfolio in terms of the balance between the expected return and the risk (the variance or the expected shortfall). This style of portfolio management is called <font color=red>active managemen... | 79610ba76add7842e3815ae46ffb342d969d928c | 42,221 | ipynb | Jupyter Notebook | notebook/ges_tracking_error.ipynb | nakatsuma/GES-PEARL | 094c2f5f1f4045d60803db91159824a801ce5dcd | [
"MIT"
] | 4 | 2018-10-10T04:10:51.000Z | 2021-10-06T02:03:56.000Z | notebook/ges_tracking_error.ipynb | nakatsuma/GES-PEARL | 094c2f5f1f4045d60803db91159824a801ce5dcd | [
"MIT"
] | null | null | null | notebook/ges_tracking_error.ipynb | nakatsuma/GES-PEARL | 094c2f5f1f4045d60803db91159824a801ce5dcd | [
"MIT"
] | 5 | 2019-05-15T04:03:04.000Z | 2021-12-07T01:33:23.000Z | 121.674352 | 29,868 | 0.827313 | true | 2,475 | Qwen/Qwen-72B | 1. YES
2. YES | 0.774583 | 0.819893 | 0.635076 | __label__eng_Latn | 0.457474 | 0.313824 |
# <h1><center><span style="color: red;">$\textbf{Superposition}$</center></h1>
<font size = '4'> It’s only when you look at the tiniest quantum particles like atoms, electrons, photons, and the like that you see intriguing phenomena like $\textbf{superposition and entanglement}$.
$\textbf{Superposition}$ refers ... | 88729c515d7688ca42772bcd918fa67a8e542a72 | 156,908 | ipynb | Jupyter Notebook | day2/Superposition, Random circuit and Entanglement.ipynb | locus-ioe/Quantum-Computing-2021 | ba11d76be7d5bf36dbd1e4b92e7f9635f3237bbb | [
"MIT"
] | 12 | 2021-07-23T13:38:20.000Z | 2021-09-07T00:40:09.000Z | day2/Superposition, Random circuit and Entanglement.ipynb | Pratha-Me/Quantum-Computing-2021 | bd9cf9a1165a47c61f9277126f4df04ae5562d61 | [
"MIT"
] | 3 | 2021-07-31T08:43:38.000Z | 2021-07-31T08:43:38.000Z | day2/Superposition, Random circuit and Entanglement.ipynb | Pratha-Me/Quantum-Computing-2021 | bd9cf9a1165a47c61f9277126f4df04ae5562d61 | [
"MIT"
] | 7 | 2021-07-24T06:14:36.000Z | 2021-07-29T22:02:12.000Z | 361.539171 | 38,236 | 0.935759 | true | 1,925 | Qwen/Qwen-72B | 1. YES
2. YES | 0.868827 | 0.815232 | 0.708296 | __label__eng_Latn | 0.786542 | 0.48394 |
# Quantum Fourier Transforms
The **"QFT (Quantum Fourier Transform)"** quantum kata is a series of exercises designed
to teach you the basics of quantum Fourier transform (QFT). It covers implementing QFT and using
it to perform simple state transformations.
Each task is wrapped in one operation preceded by the descr... | 10cd8c9f4e203745cbb9089b233b996bdc363614 | 32,420 | ipynb | Jupyter Notebook | QFT/QFT.ipynb | cfhirsch/QuantumKatas | dd531c8c4b9034ef1dfbb303a6efe1d42fe7f2cb | [
"MIT"
] | null | null | null | QFT/QFT.ipynb | cfhirsch/QuantumKatas | dd531c8c4b9034ef1dfbb303a6efe1d42fe7f2cb | [
"MIT"
] | null | null | null | QFT/QFT.ipynb | cfhirsch/QuantumKatas | dd531c8c4b9034ef1dfbb303a6efe1d42fe7f2cb | [
"MIT"
] | null | null | null | 29.339367 | 203 | 0.509315 | true | 6,692 | Qwen/Qwen-72B | 1. YES
2. YES | 0.872347 | 0.737158 | 0.643058 | __label__eng_Latn | 0.772946 | 0.33237 |
### Calculations used for Tsmp
```
from sympy import *
init_printing()
```
#### Post synaptic dendritic current
Tfd (Tf difference) below represents the difference between $t$ (current time) and $Tf[i]$ (last pre synaptic spike time for dendrite i). E. g. result of $t - Tf[i]$.
```
Tfd = var('Tfd')
```
Dt = Del... | bdc7b805f754f046ee9982ba7b5c16d5de49b013 | 25,498 | ipynb | Jupyter Notebook | notebooks/TsmpCalcs.ipynb | Jbwasse2/snn-rl | 29b040655f432bd390bc9d835b86cbfdf1a622e4 | [
"MIT"
] | 68 | 2015-04-16T11:14:31.000Z | 2022-03-11T07:43:51.000Z | notebooks/TsmpCalcs.ipynb | Jbwasse2/snn-rl | 29b040655f432bd390bc9d835b86cbfdf1a622e4 | [
"MIT"
] | 6 | 2015-11-24T04:53:57.000Z | 2019-10-21T02:00:15.000Z | notebooks/TsmpCalcs.ipynb | Jbwasse2/snn-rl | 29b040655f432bd390bc9d835b86cbfdf1a622e4 | [
"MIT"
] | 25 | 2015-12-27T10:04:53.000Z | 2021-01-03T03:25:18.000Z | 55.794311 | 5,523 | 0.735783 | true | 936 | Qwen/Qwen-72B | 1. YES
2. YES | 0.942507 | 0.810479 | 0.763882 | __label__eng_Latn | 0.888958 | 0.613086 |
## Nonlinear Dimensionality Reduction
G. Richards (2016, 2018), based on materials from Ivezic, Connolly, Miller, Leighly, and VanderPlas.
Today we will talk about the concepts of
* manifold learning
* nonlinear dimensionality reduction
Specifically using the following algorithms
* local linear embedding (LLE)
* iso... | 30d6db95ab1ac28cc04763edc0d2a3c5ad10df58 | 142,816 | ipynb | Jupyter Notebook | notebooks/NonlinearDimensionReduction.ipynb | ejh92/PHYS_T480_F18 | 8aa8bdcb230ef36fe4fab3c8d689e59e4be59366 | [
"MIT"
] | null | null | null | notebooks/NonlinearDimensionReduction.ipynb | ejh92/PHYS_T480_F18 | 8aa8bdcb230ef36fe4fab3c8d689e59e4be59366 | [
"MIT"
] | null | null | null | notebooks/NonlinearDimensionReduction.ipynb | ejh92/PHYS_T480_F18 | 8aa8bdcb230ef36fe4fab3c8d689e59e4be59366 | [
"MIT"
] | null | null | null | 271.513308 | 54,628 | 0.920548 | true | 2,595 | Qwen/Qwen-72B | 1. YES
2. YES | 0.853913 | 0.810479 | 0.692078 | __label__eng_Latn | 0.959208 | 0.446261 |
# Neural Nets t2
```python
%matplotlib widget
#%matplotlib inline
%load_ext autoreload
%autoreload 2
```
```python
# import Importing_Notebooks
import numpy as np
from scipy import ndimage
import matplotlib.pyplot as plt
import dill
```
A network built of components which:
1. accept an ordered set of reals (we'll ... | 918e7bebfea9a42a73e5e9384a7f155e38b0042f | 44,882 | ipynb | Jupyter Notebook | nbs/OLD/nnt2.ipynb | pramasoul/aix | 98333b875f6c6cda6dee86e6eab02c5ddc622543 | [
"MIT"
] | null | null | null | nbs/OLD/nnt2.ipynb | pramasoul/aix | 98333b875f6c6cda6dee86e6eab02c5ddc622543 | [
"MIT"
] | 1 | 2021-11-29T03:44:00.000Z | 2021-12-19T05:34:04.000Z | nbs/OLD/nnt2.ipynb | pramasoul/aix | 98333b875f6c6cda6dee86e6eab02c5ddc622543 | [
"MIT"
] | null | null | null | 24.742007 | 298 | 0.441981 | true | 7,523 | Qwen/Qwen-72B | 1. YES
2. YES | 0.938124 | 0.805632 | 0.755783 | __label__eng_Latn | 0.317826 | 0.594269 |
<center>
<h1> TP-Projet d'optimisation numérique </h1>
<h1> Année 2020-2021 - 2e année département Sciences du Numérique </h1>
<h1> Mouddene Hamza </h1>
<h1> Tyoubi Anass </h1>
</center>
# Algorithme de Newton
## Implémentation
1. Coder l’algorithme de Newton local tel que décrit dans la section *Algorithme de ... | 0c8f6123fc3dfbbf9bc3ca56b7e2d067d1f92681 | 44,980 | ipynb | Jupyter Notebook | 2A/S7/Optimisation/TP-Projet/src/.ipynb_checkpoints/TP-Projet-Optinum-checkpoint.ipynb | MOUDDENEHamza/ENSEEIHT | a90b1dee0c8d18a9578153a357278d99405bb534 | [
"Apache-2.0"
] | 4 | 2020-05-02T12:32:32.000Z | 2022-01-12T20:20:35.000Z | 2A/S7/Optimisation/TP-Projet/src/.ipynb_checkpoints/TP-Projet-Optinum-checkpoint.ipynb | MOUDDENEHamza/ENSEEIHT | a90b1dee0c8d18a9578153a357278d99405bb534 | [
"Apache-2.0"
] | 2 | 2021-01-14T20:03:26.000Z | 2022-01-30T01:10:00.000Z | 2A/S7/Optimisation/TP-Projet/src/.ipynb_checkpoints/TP-Projet-Optinum-checkpoint.ipynb | MOUDDENEHamza/ENSEEIHT | a90b1dee0c8d18a9578153a357278d99405bb534 | [
"Apache-2.0"
] | 13 | 2020-11-11T21:28:11.000Z | 2022-02-19T13:54:22.000Z | 45.851172 | 567 | 0.520454 | true | 10,907 | Qwen/Qwen-72B | 1. YES
2. YES | 0.819893 | 0.851953 | 0.69851 | __label__fra_Latn | 0.645104 | 0.461205 |
# GPyTorch Regression Tutorial
## Introduction
In this notebook, we demonstrate many of the design features of GPyTorch using the simplest example, training an RBF kernel Gaussian process on a simple function. We'll be modeling the function
\begin{align}
y &= \sin(2\pi x) + \epsilon \\
\epsilon &\sim \mathcal{N}(0... | 849d0b24a2b65a0f984cc2d11417c10aff98ba33 | 35,633 | ipynb | Jupyter Notebook | examples/01_Exact_GPs/Simple_GP_Regression.ipynb | Mehdishishehbor/gpytorch | 432e537b3f6679ea4ab3acf33b14626b7e161c92 | [
"MIT"
] | null | null | null | examples/01_Exact_GPs/Simple_GP_Regression.ipynb | Mehdishishehbor/gpytorch | 432e537b3f6679ea4ab3acf33b14626b7e161c92 | [
"MIT"
] | null | null | null | examples/01_Exact_GPs/Simple_GP_Regression.ipynb | Mehdishishehbor/gpytorch | 432e537b3f6679ea4ab3acf33b14626b7e161c92 | [
"MIT"
] | null | null | null | 95.021333 | 18,714 | 0.800662 | true | 3,731 | Qwen/Qwen-72B | 1. YES
2. YES | 0.754915 | 0.815232 | 0.615431 | __label__eng_Latn | 0.964293 | 0.268183 |
# Algebra Lineal con Python
*Esta notebook fue creada originalmente como un blog post por [Raúl E. López Briega](http://relopezbriega.com.ar/) en [Mi blog sobre Python](http://relopezbriega.github.io). El contenido esta bajo la licencia BSD.*
## Introducción
Una de las herramientas matemáticas más utilizadas en [m... | 6d9414436822e9ffc101a7483a4551146646233e | 188,242 | ipynb | Jupyter Notebook | Precurso/03_Matematicas_estadistica_Git/Introduccion-matematicas.ipynb | Lawlesscodelen/Bootcamp-Data- | 17125432ff82dd9b6b8dd08e4b5f39e1d787ccde | [
"MIT"
] | null | null | null | Precurso/03_Matematicas_estadistica_Git/Introduccion-matematicas.ipynb | Lawlesscodelen/Bootcamp-Data- | 17125432ff82dd9b6b8dd08e4b5f39e1d787ccde | [
"MIT"
] | null | null | null | Precurso/03_Matematicas_estadistica_Git/Introduccion-matematicas.ipynb | Lawlesscodelen/Bootcamp-Data- | 17125432ff82dd9b6b8dd08e4b5f39e1d787ccde | [
"MIT"
] | 1 | 2020-04-21T19:01:34.000Z | 2020-04-21T19:01:34.000Z | 53.175706 | 11,970 | 0.700896 | true | 23,824 | Qwen/Qwen-72B | 1. YES
2. YES | 0.853913 | 0.808067 | 0.690019 | __label__spa_Latn | 0.806964 | 0.441476 |
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
```
### Goals of this Lesson
- Present the fundamentals of Linear Regression for Prediction
- Notation and Framework
- Gradient Descent for Linear Regression
- Advantages and Issues
- Closed for... | 20dd458bd91e953aecfac2ce86bbfb6d10943437 | 204,715 | ipynb | Jupyter Notebook | Session 1 - Linear_Regression.ipynb | dinrker/PredictiveModeling | af69864fbe506d095d15049ee2fea6ecd770af36 | [
"MIT"
] | null | null | null | Session 1 - Linear_Regression.ipynb | dinrker/PredictiveModeling | af69864fbe506d095d15049ee2fea6ecd770af36 | [
"MIT"
] | null | null | null | Session 1 - Linear_Regression.ipynb | dinrker/PredictiveModeling | af69864fbe506d095d15049ee2fea6ecd770af36 | [
"MIT"
] | null | null | null | 180.206866 | 38,724 | 0.870185 | true | 7,713 | Qwen/Qwen-72B | 1. YES
2. YES | 0.927363 | 0.903294 | 0.837682 | __label__eng_Latn | 0.928219 | 0.784549 |
```python
import numpy as np
binPow = 1.6
maxR = 8
kernelSize = 20
kernelDist = 10
def v(x):
return x**binPow/kernelSize**binPow * kernelDist
def bin(i):
return ( i * (kernelSize**binPow) / kernelDist ) ** (1/binPow)
for i in range(kernelSize):
print(i, v(i), v(i+1), v(i+1)-v(i))
for i in range(maxR):
... | 22167955fb76aa83caf4ad399f37cd449f118043 | 4,709 | ipynb | Jupyter Notebook | examples/MRA-Head/SupportingNotes.ipynb | kian-weimer/ITKTubeTK | 88da3195bfeca017745e7cddfe04f82571bd00ee | [
"Apache-2.0"
] | 27 | 2020-04-06T17:23:22.000Z | 2022-03-02T13:25:52.000Z | examples/MRA-Head/SupportingNotes.ipynb | kian-weimer/ITKTubeTK | 88da3195bfeca017745e7cddfe04f82571bd00ee | [
"Apache-2.0"
] | 14 | 2020-04-09T00:23:15.000Z | 2022-02-26T13:02:35.000Z | examples/MRA-Head/SupportingNotes.ipynb | kian-weimer/ITKTubeTK | 88da3195bfeca017745e7cddfe04f82571bd00ee | [
"Apache-2.0"
] | 14 | 2020-04-03T03:56:14.000Z | 2022-01-14T07:51:32.000Z | 28.539394 | 181 | 0.564239 | true | 1,288 | Qwen/Qwen-72B | 1. YES
2. YES | 0.760651 | 0.682574 | 0.5192 | __label__yue_Hant | 0.214736 | 0.044605 |
```python
import numpy as np
import sympy as sym
import numba
import pydae.build as db
```
```python
```
```python
S_b = 90e3
U_b = 400.0
Z_b = U_b**2/S_b
I_b = S_b/(np.sqrt(3)*U_b)
Omega_b = 2*np.pi*50
R_s = 0.023/Z_b
R_r = 0.024/Z_b
Ll_s = 0.086/Z_b
Ll_r = 0.196/Z_b
L_m = 3.7/Z_b
params = {'S_b':S_b,'U_b'... | f23c83bed287b5ca7e97435ba8bbe3467b268431 | 6,660 | ipynb | Jupyter Notebook | examples/machines/im_milano/imib_fisix_5ord_builder.ipynb | pydae/pydae | 8076bcfeb2cdc865a5fc58561ff8d246d0ed7d9d | [
"MIT"
] | 1 | 2020-12-20T03:45:26.000Z | 2020-12-20T03:45:26.000Z | examples/machines/im_milano/imib_fisix_5ord_builder.ipynb | pydae/pydae | 8076bcfeb2cdc865a5fc58561ff8d246d0ed7d9d | [
"MIT"
] | null | null | null | examples/machines/im_milano/imib_fisix_5ord_builder.ipynb | pydae/pydae | 8076bcfeb2cdc865a5fc58561ff8d246d0ed7d9d | [
"MIT"
] | null | null | null | 35.806452 | 740 | 0.515015 | true | 1,274 | Qwen/Qwen-72B | 1. YES
2. YES | 0.90053 | 0.743168 | 0.669245 | __label__kor_Hang | 0.129944 | 0.393211 |
<a href="https://colab.research.google.com/github/AnilZen/centpy/blob/master/notebooks/Scalar_2d.ipynb" target="_parent"></a>
# Quasilinear scalar equation with CentPy in 2d
### Import packages
```python
# Install the centpy package
!pip install centpy
```
Collecting centpy
Downloading https://files.pyth... | 31fcdf9ee0987e7e132118a9bf87a2c82ea32a53 | 368,597 | ipynb | Jupyter Notebook | notebooks/Scalar_2d.ipynb | olekravchenko/centpy | e10d1b92c0ee5520110496595b6875b749fa4451 | [
"MIT"
] | 2 | 2021-06-23T17:23:21.000Z | 2022-01-14T01:28:57.000Z | notebooks/Scalar_2d.ipynb | olekravchenko/centpy | e10d1b92c0ee5520110496595b6875b749fa4451 | [
"MIT"
] | null | null | null | notebooks/Scalar_2d.ipynb | olekravchenko/centpy | e10d1b92c0ee5520110496595b6875b749fa4451 | [
"MIT"
] | null | null | null | 90.519892 | 232 | 0.76853 | true | 1,017 | Qwen/Qwen-72B | 1. YES
2. YES | 0.874077 | 0.661923 | 0.578572 | __label__eng_Latn | 0.389761 | 0.182546 |
```python
import numpy as np
import math
from scipy import stats
import matplotlib as mpl
import matplotlib.pyplot as plt
import ipywidgets as widgets
from ipywidgets import interact, interact_manual
%matplotlib inline
plt.style.use('seaborn-whitegrid')
mpl.style.use('seaborn')
prop_cycle = plt.rcParams["axes.prop_cycl... | 6fc105e8dfd07de52c1490003c40be45a2b70046 | 34,619 | ipynb | Jupyter Notebook | Abatement_v1.ipynb | ChampionApe/Abatement_project | eeb1ebe3ed84a49521c18c0acf22314474fbfc2e | [
"MIT"
] | null | null | null | Abatement_v1.ipynb | ChampionApe/Abatement_project | eeb1ebe3ed84a49521c18c0acf22314474fbfc2e | [
"MIT"
] | null | null | null | Abatement_v1.ipynb | ChampionApe/Abatement_project | eeb1ebe3ed84a49521c18c0acf22314474fbfc2e | [
"MIT"
] | null | null | null | 63.521101 | 15,144 | 0.743205 | true | 3,951 | Qwen/Qwen-72B | 1. YES
2. YES | 0.849971 | 0.76908 | 0.653696 | __label__eng_Latn | 0.880639 | 0.357086 |
# Лаба Дмитро
## Лабораторна робота №2
## Варіант 2
```python
import numpy as np
import sympy as sp
from scipy.linalg import eig
from sympy.matrices import Matrix
from IPython.display import display, Math, Latex
def bmatrix(a):
lines = str(a).replace('[', '').replace(']', '').splitlines()
rv = [r'\begin{bma... | 8e29f38ba494a0eb80ca405b74a94376edd2d792 | 12,031 | ipynb | Jupyter Notebook | eco_systems/laba2.ipynb | pashchenkoromak/jParcs | 5d91ef6fdd983300e850599d04a469c17238fc65 | [
"MIT"
] | 2 | 2019-10-01T09:41:15.000Z | 2021-06-06T17:46:13.000Z | eco_systems/laba2.ipynb | pashchenkoromak/jParcs | 5d91ef6fdd983300e850599d04a469c17238fc65 | [
"MIT"
] | 1 | 2018-05-18T18:20:46.000Z | 2018-05-18T18:20:46.000Z | eco_systems/laba2.ipynb | pashchenkoromak/jParcs | 5d91ef6fdd983300e850599d04a469c17238fc65 | [
"MIT"
] | 8 | 2017-01-20T15:44:06.000Z | 2021-11-28T20:00:49.000Z | 21.369449 | 114 | 0.468872 | true | 1,885 | Qwen/Qwen-72B | 1. YES
2. YES | 0.926304 | 0.83762 | 0.77589 | __label__kor_Hang | 0.074921 | 0.640986 |
```python
### PREAMBLE
# Chapter 2 - linear models
# linear.svg
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
```
**any bullet points are comments I've made to help for understanding!**
## Chapter 2: Linear models
Before we dive ... | aac355f112bd64a04f338cbb01715889942f9549 | 236,819 | ipynb | Jupyter Notebook | misc/adversarial_robustness_neurips_tutorial/linear_models/linear_models.ipynb | kchare/advex_notbugs_features | 0ec0578a1aba2bdb86854676c005488091b64123 | [
"MIT"
] | 2 | 2022-02-08T11:51:12.000Z | 2022-02-23T00:30:07.000Z | misc/adversarial_robustness_neurips_tutorial/linear_models/linear_models.ipynb | kchare/advex_notbugs_features | 0ec0578a1aba2bdb86854676c005488091b64123 | [
"MIT"
] | null | null | null | misc/adversarial_robustness_neurips_tutorial/linear_models/linear_models.ipynb | kchare/advex_notbugs_features | 0ec0578a1aba2bdb86854676c005488091b64123 | [
"MIT"
] | 2 | 2021-12-21T20:31:28.000Z | 2022-01-21T17:06:34.000Z | 51.493586 | 2,441 | 0.535861 | true | 6,843 | Qwen/Qwen-72B | 1. YES
2. YES | 0.805632 | 0.808067 | 0.651005 | __label__eng_Latn | 0.986379 | 0.350833 |
```python
# goal: have sympy do the mechanical substitutions, to double-check the desired relations
# once this is done, this will also make it easier for a human to check (just double-check the definitions), and easier
# to check for arbitrary splittings
from sympy import *
from sympy import init_printing
init_printi... | a6ef330526a753e41d7d7a1c09c54974e28e80bc | 75,428 | ipynb | Jupyter Notebook | Using sympy to compute relative path action.ipynb | jchodera/maxentile-notebooks | 6e8ca4e3d9dbd1623ea926395d06740a30d9111d | [
"MIT"
] | null | null | null | Using sympy to compute relative path action.ipynb | jchodera/maxentile-notebooks | 6e8ca4e3d9dbd1623ea926395d06740a30d9111d | [
"MIT"
] | 2 | 2018-06-10T12:21:10.000Z | 2018-06-10T14:42:45.000Z | Using sympy to compute relative path action.ipynb | jchodera/maxentile-notebooks | 6e8ca4e3d9dbd1623ea926395d06740a30d9111d | [
"MIT"
] | 1 | 2018-06-10T12:14:55.000Z | 2018-06-10T12:14:55.000Z | 114.806697 | 13,472 | 0.736915 | true | 1,797 | Qwen/Qwen-72B | 1. YES
2. YES | 0.877477 | 0.831143 | 0.729309 | __label__eng_Latn | 0.350959 | 0.53276 |
```python
import numpy as np
import matplotlib.pyplot as plt
from sympy import S, solve
import plotutils as pu
%matplotlib inline
```
# numbers on a plane
Numbers can be a lot more interesting than just a value if you're just willing to shift your perspective a bit.
# integers
When we are dealing with integers we ar... | 2332e5032b2f7552cae859c9d3e2c176e205b711 | 20,341 | ipynb | Jupyter Notebook | squares_and_roots.ipynb | basp/notes | 8831f5f44fc675fbf1c3359a8743d2023312d5ca | [
"MIT"
] | 1 | 2016-12-09T13:58:13.000Z | 2016-12-09T13:58:13.000Z | squares_and_roots.ipynb | basp/notes | 8831f5f44fc675fbf1c3359a8743d2023312d5ca | [
"MIT"
] | null | null | null | squares_and_roots.ipynb | basp/notes | 8831f5f44fc675fbf1c3359a8743d2023312d5ca | [
"MIT"
] | null | null | null | 141.256944 | 8,106 | 0.871098 | true | 764 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.863392 | 0.766005 | __label__eng_Latn | 0.999698 | 0.618018 |
# Week 8 - Discrete Latent Variable Models and Hybrid Models Notebook
In this notebook, we will solve questions discrete latent variable models and hybrid generative models.
- This notebook is prepared using PyTorch. However, you can use any Python package you want to implement the necessary functions in questions.
... | 72be7d5a7f9ccbef59597468224933265a05e1e5 | 12,296 | ipynb | Jupyter Notebook | inzva x METU ImageLab Joint Program/Week 8 - Discrete Latent Variable Models and Hybrid Models/Week_8_Discrete_Latent_Variable_Models_and_Hybrid_Models.ipynb | inzva/-AI-Labs-Joint-Program | 45d776000f5d6671c7dbd98bb86ad3ceae6e4b2c | [
"MIT"
] | 12 | 2021-07-31T11:14:41.000Z | 2022-02-26T14:28:59.000Z | inzva x METU ImageLab Joint Program/Week 8 - Discrete Latent Variable Models and Hybrid Models/Week_8_Discrete_Latent_Variable_Models_and_Hybrid_Models.ipynb | inzva/-AI-Labs-Joint-Program | 45d776000f5d6671c7dbd98bb86ad3ceae6e4b2c | [
"MIT"
] | null | null | null | inzva x METU ImageLab Joint Program/Week 8 - Discrete Latent Variable Models and Hybrid Models/Week_8_Discrete_Latent_Variable_Models_and_Hybrid_Models.ipynb | inzva/-AI-Labs-Joint-Program | 45d776000f5d6671c7dbd98bb86ad3ceae6e4b2c | [
"MIT"
] | 1 | 2021-08-16T20:50:44.000Z | 2021-08-16T20:50:44.000Z | 34.63662 | 391 | 0.499919 | true | 1,873 | Qwen/Qwen-72B | 1. YES
2. YES | 0.709019 | 0.927363 | 0.657518 | __label__eng_Latn | 0.990794 | 0.365966 |
# Band Math and Indices
This section discusses band math and spectral indicies.
This notebook is derived from a [Digital Earth Africa](https://www.digitalearthafrica.org/) notebook: [here](https://github.com/digitalearthafrica/deafrica-training-workshop/blob/master/docs/session_4/01_band_indices.ipynb)
## Backgroun... | d5e62ed614258f121dd5b0df1d4af395ae9d7afb | 14,748 | ipynb | Jupyter Notebook | notebooks/day3/Band_Math_and_Indices.ipynb | jcrattz/odc_training_notebooks | 651a0028463633cf30b32ac16b4addb05d9f4e85 | [
"Apache-2.0"
] | null | null | null | notebooks/day3/Band_Math_and_Indices.ipynb | jcrattz/odc_training_notebooks | 651a0028463633cf30b32ac16b4addb05d9f4e85 | [
"Apache-2.0"
] | null | null | null | notebooks/day3/Band_Math_and_Indices.ipynb | jcrattz/odc_training_notebooks | 651a0028463633cf30b32ac16b4addb05d9f4e85 | [
"Apache-2.0"
] | 1 | 2021-08-18T16:24:48.000Z | 2021-08-18T16:24:48.000Z | 43.762611 | 467 | 0.657038 | true | 2,452 | Qwen/Qwen-72B | 1. YES
2. YES | 0.695958 | 0.72487 | 0.50448 | __label__eng_Latn | 0.997136 | 0.010404 |
# Tutorial: advection-diffusion kernels in Parcels
In Eulerian ocean models, sub-grid scale dispersion of tracers such as heat, salt, or nutrients is often parameterized as a diffusive process. In Lagrangian particle simulations, sub-grid scale effects can be parameterized as a stochastic process, randomly displacing a... | 6f5bf8dec24266ec64bac827062d4de69465d0d7 | 678,433 | ipynb | Jupyter Notebook | parcels/examples/tutorial_diffusion.ipynb | noemieplanat/Copy-parcels-master | 21f053b81a9ccdaa5d8ee4f7efd6f01639b83bfc | [
"MIT"
] | 202 | 2017-07-24T23:22:38.000Z | 2022-03-22T15:33:46.000Z | parcels/examples/tutorial_diffusion.ipynb | noemieplanat/Copy-parcels-master | 21f053b81a9ccdaa5d8ee4f7efd6f01639b83bfc | [
"MIT"
] | 538 | 2017-06-21T08:04:43.000Z | 2022-03-31T14:36:45.000Z | parcels/examples/tutorial_diffusion.ipynb | noemieplanat/Copy-parcels-master | 21f053b81a9ccdaa5d8ee4f7efd6f01639b83bfc | [
"MIT"
] | 94 | 2017-07-05T10:28:55.000Z | 2022-03-23T19:46:23.000Z | 1,600.07783 | 322,548 | 0.959405 | true | 4,621 | Qwen/Qwen-72B | 1. YES
2. YES | 0.904651 | 0.831143 | 0.751894 | __label__eng_Latn | 0.943369 | 0.585234 |
<a href="https://colab.research.google.com/github/lsantiago/PythonIntermedio/blob/master/Clases/Semana6_ALGEBRA/algebra_lineal_apuntes.ipynb" target="_parent"></a>
# Clase Nro. 6: Álgebra Lineal
> El álgebra lineal es una rama de las matemáticas que estudia conceptos tales como vectores, matrices, espacio dual, siste... | c63aa8f1b90c272d10b6acbda8444dd0060b0744 | 29,911 | ipynb | Jupyter Notebook | Clases/Semana6_ALGEBRA/algebra_lineal_apuntes.ipynb | CarlosLedesma/PythonIntermedio | 4e54817fe5c0f13e8152f1d752b02dfa55785e28 | [
"MIT"
] | null | null | null | Clases/Semana6_ALGEBRA/algebra_lineal_apuntes.ipynb | CarlosLedesma/PythonIntermedio | 4e54817fe5c0f13e8152f1d752b02dfa55785e28 | [
"MIT"
] | null | null | null | Clases/Semana6_ALGEBRA/algebra_lineal_apuntes.ipynb | CarlosLedesma/PythonIntermedio | 4e54817fe5c0f13e8152f1d752b02dfa55785e28 | [
"MIT"
] | null | null | null | 23.890575 | 495 | 0.461001 | true | 2,603 | Qwen/Qwen-72B | 1. YES
2. YES | 0.835484 | 0.815232 | 0.681113 | __label__spa_Latn | 0.983554 | 0.420786 |
# Hydrogen Wave Function
```python
#Import libraries
from numpy import *
import matplotlib.pyplot as plt
from sympy.physics.hydrogen import Psi_nlm
```
## Analytical Equation
$$
\psi_{n \ell m}(r, \theta, \varphi)=\sqrt{\left(\frac{2}{n a_{0}^{*}}\right)^{3} \frac{(n-\ell-1) !}{2 n(n+\ell) !}} e^{-\rho / 2} \rho^{... | 1c0219cd0030deedd6270184c1603444018b2c05 | 41,093 | ipynb | Jupyter Notebook | hydrogen-wave-function.ipynb | sinansevim/EBT617E | 0907846e09173b419dfb6c3a5eae20c3ef8548bb | [
"MIT"
] | 1 | 2021-03-12T13:16:39.000Z | 2021-03-12T13:16:39.000Z | hydrogen-wave-function.ipynb | sinansevim/EBT617E | 0907846e09173b419dfb6c3a5eae20c3ef8548bb | [
"MIT"
] | null | null | null | hydrogen-wave-function.ipynb | sinansevim/EBT617E | 0907846e09173b419dfb6c3a5eae20c3ef8548bb | [
"MIT"
] | null | null | null | 132.987055 | 34,692 | 0.893218 | true | 943 | Qwen/Qwen-72B | 1. YES
2. YES | 0.927363 | 0.819893 | 0.760339 | __label__eng_Latn | 0.438451 | 0.604854 |
```python
import pycalphad
from pycalphad.tests.datasets import ALFE_TDB
from pycalphad import Database, Model
import pycalphad.variables as v
from sympy import Piecewise, Function
dbf = Database(ALFE_TDB)
mod = Model(dbf, ['AL','FE', 'VA'], 'B2_BCC')
t = mod.ast.diff(v.Y('B2_BCC', 1, 'AL'), v.Y('B2_BCC', 0, 'FE'))
#p... | 99434542f5ac04ce671a0e1de074f800d784b53a | 80,268 | ipynb | Jupyter Notebook | SymEngineTest.ipynb | richardotis/pycalphad-sandbox | 43d8786eee8f279266497e9c5f4630d19c893092 | [
"MIT"
] | 1 | 2017-03-08T18:21:30.000Z | 2017-03-08T18:21:30.000Z | SymEngineTest.ipynb | richardotis/pycalphad-sandbox | 43d8786eee8f279266497e9c5f4630d19c893092 | [
"MIT"
] | null | null | null | SymEngineTest.ipynb | richardotis/pycalphad-sandbox | 43d8786eee8f279266497e9c5f4630d19c893092 | [
"MIT"
] | 1 | 2018-11-03T01:31:57.000Z | 2018-11-03T01:31:57.000Z | 399.343284 | 18,835 | 0.694623 | true | 610 | Qwen/Qwen-72B | 1. YES
2. YES | 0.798187 | 0.679179 | 0.542111 | __label__eng_Latn | 0.449568 | 0.097836 |
# The Space-Arrow of Space-time
People have claimed Nature does not have an arrow for time. I don't think the question, as stated, is well-formed. Any analysis of the arrow of time for one observer will look like the arrow of space-time to another one moving relative to the first.
Two different problems are often cit... | ef37442ff3d97dcf565edd3ec2424201dba4cc7b | 43,512 | ipynb | Jupyter Notebook | q_notebooks/space-time_reversal.ipynb | dougsweetser/ipq | 5505c8c9c6a6991e053dc9a3de3b5e3588805203 | [
"Apache-2.0"
] | 2 | 2017-01-19T18:43:20.000Z | 2017-02-21T16:23:07.000Z | q_notebooks/space-time_reversal.ipynb | dougsweetser/ipq | 5505c8c9c6a6991e053dc9a3de3b5e3588805203 | [
"Apache-2.0"
] | null | null | null | q_notebooks/space-time_reversal.ipynb | dougsweetser/ipq | 5505c8c9c6a6991e053dc9a3de3b5e3588805203 | [
"Apache-2.0"
] | null | null | null | 55.784615 | 2,692 | 0.727684 | true | 2,358 | Qwen/Qwen-72B | 1. YES
2. YES | 0.76908 | 0.66888 | 0.514423 | __label__eng_Latn | 0.984795 | 0.033505 |
# Allen Cahn equation
* Physical space
\begin{align}
u_{t} = \epsilon u_{xx} + u - u^{3}
\end{align}
* Discretized with Chebyshev differentiation matrix (D)
\begin{align}
u_t = (\epsilon D^2 + I)u - u^{3}
\end{align}
# Imports
```python
import numpy as np
import matplotlib.pyplot as plt
from rkstiff.grids import c... | 1da4080158afa4cfadbe169e6a22d972b25b7aee | 518,273 | ipynb | Jupyter Notebook | demos/allen_cahn.ipynb | whalenpt/rkstiff | 9fbec7ddd123cc644d392933b518d342751b4cd8 | [
"MIT"
] | 4 | 2021-11-05T15:35:21.000Z | 2022-01-17T10:20:57.000Z | demos/allen_cahn.ipynb | whalenpt/rkstiff | 9fbec7ddd123cc644d392933b518d342751b4cd8 | [
"MIT"
] | null | null | null | demos/allen_cahn.ipynb | whalenpt/rkstiff | 9fbec7ddd123cc644d392933b518d342751b4cd8 | [
"MIT"
] | null | null | null | 1,511 | 492,404 | 0.960833 | true | 1,545 | Qwen/Qwen-72B | 1. YES
2. YES | 0.785309 | 0.793106 | 0.622833 | __label__eng_Latn | 0.183555 | 0.28538 |
## 1 求解导数
给定输入的张量是$x$，这是一个 $N \times C_{i n} \times w \times h$ 的张量;
给定模板的张量是$h$，这是一个$C_{\text {out }} \times C_{\text {in }} \times 3 \times 3$的张量;
进行卷积运算的参数，采用Padding = 1，然后 Stride = 1
现在已知张量$y$是通过模板对输入进行模板运算的结果，如下:
$$y=x \otimes h$$
其中$\otimes$是模板运算，另外已知损失函数相对于$y$的偏导数为:
$$\frac{\partial L}{\partial y}$$
请尝试推... | 893b1d1d596d4a2f2735e5f21080fb2267c10a27 | 15,469 | ipynb | Jupyter Notebook | homeworks/ch_11.ipynb | magicwenli/morpher | 2f8e756d81f3fac59c948789e945a06a4d4adce3 | [
"MIT"
] | null | null | null | homeworks/ch_11.ipynb | magicwenli/morpher | 2f8e756d81f3fac59c948789e945a06a4d4adce3 | [
"MIT"
] | null | null | null | homeworks/ch_11.ipynb | magicwenli/morpher | 2f8e756d81f3fac59c948789e945a06a4d4adce3 | [
"MIT"
] | null | null | null | 29.408745 | 279 | 0.40662 | true | 4,712 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91848 | 0.79053 | 0.726086 | __label__yue_Hant | 0.14423 | 0.525274 |
```python
# HIDDEN
from datascience import *
from prob140 import *
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
%matplotlib inline
import math
from scipy import stats
from sympy import *
init_printing()
```
### Probabilities and Expectations ###
A function $f$ on the plane is cal... | 77bc35e3a40636f4c12143d8dfc200f083583c51 | 217,358 | ipynb | Jupyter Notebook | miscellaneous_notebooks/Joint_Densities/Probabilities_and_Expectations.ipynb | dcroce/jupyter-book | 9ac4b502af8e8c5c3b96f5ec138602a0d3d8a624 | [
"MIT"
] | null | null | null | miscellaneous_notebooks/Joint_Densities/Probabilities_and_Expectations.ipynb | dcroce/jupyter-book | 9ac4b502af8e8c5c3b96f5ec138602a0d3d8a624 | [
"MIT"
] | null | null | null | miscellaneous_notebooks/Joint_Densities/Probabilities_and_Expectations.ipynb | dcroce/jupyter-book | 9ac4b502af8e8c5c3b96f5ec138602a0d3d8a624 | [
"MIT"
] | null | null | null | 343.920886 | 164,082 | 0.915204 | true | 2,687 | Qwen/Qwen-72B | 1. YES
2. YES | 0.874077 | 0.865224 | 0.756273 | __label__eng_Latn | 0.99257 | 0.595407 |
For the Ronbrock method, we need to solve a linear system of the form
$$
M_{ij}x_{j}=b_{i} \;,
$$
with M a square matrix (repeated indecies imply summation).
Such systems are soved by (among other methods) the so-called LU factorization (or decomposition),
where you decompose $M_{ij}=L_{ik}U_{kj}$ with $L_{i, j>i}=0$... | 0472e0c62c167525f49b4df1c9a6a111f8fbd84f | 14,542 | ipynb | Jupyter Notebook | Differential_Equations/python/0-useful/LU_decomposition-first.ipynb | dkaramit/ASAP | afade2737b332e7dbf0ea06eb4f31564a478ee40 | [
"MIT"
] | null | null | null | Differential_Equations/python/0-useful/LU_decomposition-first.ipynb | dkaramit/ASAP | afade2737b332e7dbf0ea06eb4f31564a478ee40 | [
"MIT"
] | null | null | null | Differential_Equations/python/0-useful/LU_decomposition-first.ipynb | dkaramit/ASAP | afade2737b332e7dbf0ea06eb4f31564a478ee40 | [
"MIT"
] | 1 | 2021-12-15T02:03:01.000Z | 2021-12-15T02:03:01.000Z | 31.681917 | 180 | 0.422019 | true | 3,408 | Qwen/Qwen-72B | 1. YES
2. YES | 0.92079 | 0.849971 | 0.782645 | __label__eng_Latn | 0.876823 | 0.656678 |
# Multi-Armed bandit UCB
Thompson sampling is an ingenious algorithm that implicitly balances exploration and exploitation based on quality and uncertainty. Let's say we sample a 3-armed bandit and model the probability that each arm gives us a positive reward. The goal is of course to maximize our rewards by pulling ... | 8ac723037bcbf2b6bd7e588d08605b48db9b2a57 | 41,382 | ipynb | Jupyter Notebook | bandits/03-mab-ucb.ipynb | martin-fabbri/colab-notebooks | 03658a7772fbe71612e584bbc767009f78246b6b | [
"Apache-2.0"
] | 8 | 2020-01-18T18:39:49.000Z | 2022-02-17T19:32:26.000Z | bandits/03-mab-ucb.ipynb | martin-fabbri/colab-notebooks | 03658a7772fbe71612e584bbc767009f78246b6b | [
"Apache-2.0"
] | null | null | null | bandits/03-mab-ucb.ipynb | martin-fabbri/colab-notebooks | 03658a7772fbe71612e584bbc767009f78246b6b | [
"Apache-2.0"
] | 6 | 2020-01-18T18:40:02.000Z | 2020-09-27T09:26:38.000Z | 79.275862 | 21,978 | 0.777729 | true | 3,436 | Qwen/Qwen-72B | 1. YES
2. YES | 0.863392 | 0.863392 | 0.745445 | __label__eng_Latn | 0.98176 | 0.570251 |
```python
%matplotlib inline
import sys, platform, os
import matplotlib
from matplotlib import pyplot as plt
import numpy as np
import scipy as sci
import camb as camb
```
```python
from camb import model, initialpower
print('Using CAMB %s installed at %s'%(camb.__version__,os.path.dirname(camb.__file__)))
```
U... | 2e02de11f777325daf1885b787e3d9b63ccb6ae5 | 120,092 | ipynb | Jupyter Notebook | IMP**.ipynb | DhruvKumarPHY/solutions | 83bced0692c78399cea906e8ba4ebb2a17b57d31 | [
"MIT"
] | null | null | null | IMP**.ipynb | DhruvKumarPHY/solutions | 83bced0692c78399cea906e8ba4ebb2a17b57d31 | [
"MIT"
] | null | null | null | IMP**.ipynb | DhruvKumarPHY/solutions | 83bced0692c78399cea906e8ba4ebb2a17b57d31 | [
"MIT"
] | null | null | null | 145.742718 | 57,056 | 0.882315 | true | 3,770 | Qwen/Qwen-72B | 1. YES
2. YES | 0.771843 | 0.793106 | 0.612154 | __label__eng_Latn | 0.267281 | 0.260568 |
# Density Estimation
### Preliminaries
- Goal
- Simple maximum likelihood estimates for Gaussian and categorical distributions
- Materials
- Mandatory
- These lecture notes
- Optional
- Bishop pp. 67-70, 74-76, 93-94
### Why Density Estimation?
Density estimation relates to building a m... | 27a4361e7ed08e2530451f2dc5cf8b943014f225 | 109,784 | ipynb | Jupyter Notebook | lessons/notebooks/05_Density-Estimation.ipynb | spsbrats/AIP-5SSB0 | c518274fdaed9fc55423ae4dd216be4218238d9d | [
"CC-BY-3.0"
] | 8 | 2018-06-14T20:45:55.000Z | 2021-10-05T09:46:25.000Z | lessons/notebooks/05_Density-Estimation.ipynb | bertdv/AIP-5SSB0 | c518274fdaed9fc55423ae4dd216be4218238d9d | [
"CC-BY-3.0"
] | 59 | 2015-08-18T11:30:12.000Z | 2019-07-03T15:17:33.000Z | lessons/notebooks/05_Density-Estimation.ipynb | bertdv/AIP-5SSB0 | c518274fdaed9fc55423ae4dd216be4218238d9d | [
"CC-BY-3.0"
] | 5 | 2015-12-30T07:39:57.000Z | 2019-03-09T10:42:21.000Z | 124.471655 | 64,630 | 0.849085 | true | 5,151 | Qwen/Qwen-72B | 1. YES
2. YES | 0.849971 | 0.743168 | 0.631671 | __label__eng_Latn | 0.718947 | 0.305915 |
# Modelagem SEIR
Neste notebook está implementado o modelo SEIR de Zhilan Feng. Que inclui quarentena, e hospitalizações
\begin{align}
\frac{dS}{dt}&=-\beta S (I+(1-\rho)H)\\
\frac{dE}{dt}&= \beta S (I+(1-\rho)H)-(\chi+\alpha)E\\
\frac{dQ}{dt}&=\chi E -\alpha Q\\
\frac{dI}{dt}&= \alpha E - (\phi+\delta)I\\
\frac{dH}{d... | 48a66629d29f134bf7b9d8fd9a00c21b6a087ade | 521,611 | ipynb | Jupyter Notebook | notebooks/Modelo SEIR.ipynb | nahumsa/covidash | 24f3fdabb41ceeaadc4582ed2820f6f7f1a392a1 | [
"MIT"
] | null | null | null | notebooks/Modelo SEIR.ipynb | nahumsa/covidash | 24f3fdabb41ceeaadc4582ed2820f6f7f1a392a1 | [
"MIT"
] | null | null | null | notebooks/Modelo SEIR.ipynb | nahumsa/covidash | 24f3fdabb41ceeaadc4582ed2820f6f7f1a392a1 | [
"MIT"
] | null | null | null | 200.157713 | 84,204 | 0.870871 | true | 14,738 | Qwen/Qwen-72B | 1. YES
2. YES | 0.888759 | 0.812867 | 0.722443 | __label__kor_Hang | 0.151401 | 0.516809 |
```python
# Método para resolver las energías y eigenfunciones de un sistema cuántico numéricamente
# Modelado Molecular 2
# By: José Manuel Casillas Martín
import numpy as np
from sympy import *
from sympy import init_printing; init_printing(use_latex = 'mathjax')
import matplotlib.pyplot as plt
```
```python
# Vari... | 1287416ede47536380ee6ce74793cb7cb90d9618 | 108,746 | ipynb | Jupyter Notebook | Huckel_M0/Chema/Teorema_de_variaciones(1).ipynb | lazarusA/Density-functional-theory | c74fd44a66f857de570dc50471b24391e3fa901f | [
"MIT"
] | null | null | null | Huckel_M0/Chema/Teorema_de_variaciones(1).ipynb | lazarusA/Density-functional-theory | c74fd44a66f857de570dc50471b24391e3fa901f | [
"MIT"
] | null | null | null | Huckel_M0/Chema/Teorema_de_variaciones(1).ipynb | lazarusA/Density-functional-theory | c74fd44a66f857de570dc50471b24391e3fa901f | [
"MIT"
] | null | null | null | 269.173267 | 26,888 | 0.887251 | true | 2,133 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.782662 | 0.694382 | __label__spa_Latn | 0.542886 | 0.451613 |
```python
%matplotlib inline
```
```python
import numpy as np
import matplotlib.pyplot as plt
```
# SciPy
SciPy is a collection of numerical algorithms with python interfaces. In many cases, these interfaces are wrappers around standard numerical libraries that have been developed in the community and are used wit... | 7c6a96796a7a11eed3ca53bba860960ebc5e6505 | 1,015,999 | ipynb | Jupyter Notebook | Other/scipy-basics.ipynb | xiaozhouli/Jupyter | 68d5a384dd939b3e8079da4470d6401d11b63a4c | [
"MIT"
] | 6 | 2020-02-27T13:09:06.000Z | 2021-11-14T09:50:30.000Z | Other/scipy-basics.ipynb | xiaozhouli/Jupyter | 68d5a384dd939b3e8079da4470d6401d11b63a4c | [
"MIT"
] | null | null | null | Other/scipy-basics.ipynb | xiaozhouli/Jupyter | 68d5a384dd939b3e8079da4470d6401d11b63a4c | [
"MIT"
] | 8 | 2018-10-18T10:20:56.000Z | 2021-09-24T08:09:27.000Z | 170.813551 | 133,884 | 0.841931 | true | 48,413 | Qwen/Qwen-72B | 1. YES
2. YES | 0.787931 | 0.90053 | 0.709556 | __label__eng_Latn | 0.987179 | 0.486867 |
# Almgren and Chriss Model For Optimal Execution of Portfolio Transactions
### Introduction
We consider the execution of portfolio transactions with the aim of minimizing a combination of risk and transaction costs arising from permanent and temporary market impact. As an example, assume that you have a certain numbe... | 2578ff711f7d9e3033591f7f282ca4dc74f257b5 | 292,856 | ipynb | Jupyter Notebook | finance/Almgren and Chriss Model.ipynb | reinaldomaslim/deep-reinforcement-learning | 231a58718922788d892fab7a2a2156ffdfff53c2 | [
"MIT"
] | null | null | null | finance/Almgren and Chriss Model.ipynb | reinaldomaslim/deep-reinforcement-learning | 231a58718922788d892fab7a2a2156ffdfff53c2 | [
"MIT"
] | null | null | null | finance/Almgren and Chriss Model.ipynb | reinaldomaslim/deep-reinforcement-learning | 231a58718922788d892fab7a2a2156ffdfff53c2 | [
"MIT"
] | null | null | null | 293.442886 | 62,628 | 0.886395 | true | 6,139 | Qwen/Qwen-72B | 1. YES
2. YES | 0.877477 | 0.861538 | 0.75598 | __label__eng_Latn | 0.994404 | 0.594726 |
```python
from sympy import *
```
# Доказать (или опровергнуть), что для известного трёхмерного вектора v единичной длины матрица является матрицей поворота.
$R = \begin{bmatrix}
\vec{k}\times(\vec{k}\times\vec{v}) & \vec{k}\times\vec{v} & \vec{k}
\end{bmatrix}^T$
$|\vec{v}|=1$
$R = \begin{bmatrix}
& \vec{k}\time... | 14fa19c7b573047c735aa97ab88293314879538c | 16,101 | ipynb | Jupyter Notebook | lab3/rotation.ipynb | ArcaneStudent/stereolabs | 730c64bd3b71809cf0dd36c69748bdf032e5265a | [
"MIT"
] | null | null | null | lab3/rotation.ipynb | ArcaneStudent/stereolabs | 730c64bd3b71809cf0dd36c69748bdf032e5265a | [
"MIT"
] | null | null | null | lab3/rotation.ipynb | ArcaneStudent/stereolabs | 730c64bd3b71809cf0dd36c69748bdf032e5265a | [
"MIT"
] | null | null | null | 21.787551 | 294 | 0.413453 | true | 2,327 | Qwen/Qwen-72B | 1. YES
2. YES | 0.904651 | 0.7773 | 0.703185 | __label__yue_Hant | 0.147802 | 0.472065 |
# Análisis de Estado Estable
## Probabilidades de estado estable
Podemos utilizar la ecuación de Chapman-Kolgomorov para analizar la evolución de las probabilidades de transición de $n$-pasos. Utilicemos los datos de la parte anterior:
```python
import numpy as np
p = np.array([[0.7, 0.1, 0.2], [0.2, 0.7, 0.1], [0.... | 01498f54cf09941d5d12e992c39a26ccf2df3c9e | 38,980 | ipynb | Jupyter Notebook | docs/02cm_estado_estable.ipynb | map0logo/tci-2019 | 64b83aadf88bf1d666dee6b94eb698a8b6125c14 | [
"Unlicense"
] | 1 | 2022-03-27T04:04:33.000Z | 2022-03-27T04:04:33.000Z | docs/02cm_estado_estable.ipynb | map0logo/tci-2019 | 64b83aadf88bf1d666dee6b94eb698a8b6125c14 | [
"Unlicense"
] | null | null | null | docs/02cm_estado_estable.ipynb | map0logo/tci-2019 | 64b83aadf88bf1d666dee6b94eb698a8b6125c14 | [
"Unlicense"
] | null | null | null | 114.985251 | 30,260 | 0.864802 | true | 1,511 | Qwen/Qwen-72B | 1. YES
2. YES | 0.874077 | 0.903294 | 0.789549 | __label__spa_Latn | 0.939343 | 0.672719 |
# Ritz method for a beam
**November, 2018**
We want to find a Ritz approximation of the deflection $w$ of a beam under applied
transverse uniform load of intensity $f$ per unit lenght and an end moment $M$.
This is described by the following boundary value problem.
$$
\frac{\mathrm{d}^2}{\mathrm{d}x^2}\left(EI \frac... | 6f94e283d8ca8953f203c50e4e73c0a752401461 | 938,712 | ipynb | Jupyter Notebook | variational/ritz_beam.ipynb | nicoguaro/FEM_resources | 32f032a4e096fdfd2870e0e9b5269046dd555aee | [
"MIT"
] | 28 | 2015-11-06T16:59:39.000Z | 2022-02-25T18:18:49.000Z | variational/ritz_beam.ipynb | oldninja/FEM_resources | e44f315be217fd78ba95c09e3c94b1693773c047 | [
"MIT"
] | null | null | null | variational/ritz_beam.ipynb | oldninja/FEM_resources | e44f315be217fd78ba95c09e3c94b1693773c047 | [
"MIT"
] | 9 | 2018-06-24T22:12:00.000Z | 2022-01-12T15:57:37.000Z | 86.621021 | 40,053 | 0.716947 | true | 3,926 | Qwen/Qwen-72B | 1. YES
2. YES | 0.868827 | 0.826712 | 0.718269 | __label__eng_Latn | 0.413475 | 0.507112 |
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