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# HEWL S-SAD Merging Statistics
Merging statistics are a useful means to assess data quality in crystallography. However, each statistic has inherent shortcomings. For example, R-merge will appear inflated if the multiplicity is high, and the Pearson correlation coefficients used for $CC_{1/2}$ are very sensitive to o... | a7ff14f3914eb539613cfe1ba2d4530884b070f7 | 220,557 | ipynb | Jupyter Notebook | docs/examples/2_mergingstats.ipynb | kmdalton/reciprocalspaceship | 50655f077cb670ee86e88480f54621780c8e9f0d | [
"MIT"
] | 22 | 2020-07-10T18:13:10.000Z | 2022-03-04T16:51:00.000Z | docs/examples/2_mergingstats.ipynb | kmdalton/reciprocalspaceship | 50655f077cb670ee86e88480f54621780c8e9f0d | [
"MIT"
] | 109 | 2020-07-03T10:07:18.000Z | 2022-03-28T20:49:48.000Z | docs/examples/2_mergingstats.ipynb | JBGreisman/reciprocalspaceship | cf936cca64c5c387ace505416a047318efa9375f | [
"MIT"
] | 10 | 2020-07-03T10:51:21.000Z | 2021-08-23T19:05:24.000Z | 142.294839 | 61,692 | 0.836405 | true | 9,291 | Qwen/Qwen-72B | 1. YES
2. YES | 0.903294 | 0.867036 | 0.783188 | __label__eng_Latn | 0.635856 | 0.657941 |
## System dynamics
CartPole environment consists of a cart on a trail and a pole hinge fixed on it.
### States
$$X = \left[\array{x \\ \dot{x} \\ \theta \\ \dot{\theta} }\right]$$
### Parameters:
$l = 0.5 m$: half length of pole (homogenuous pole)
$M = 1 kg$: mass of cart
$m = 0.1 kg$: mass of pole
$\tau = ... | 70141ed15fe94468e86ceee78accc08e488496e9 | 10,406 | ipynb | Jupyter Notebook | Proposal.ipynb | caseypen/MAE_298_Final_Project | 66c96aabc7bbcfe070c5170c03d5f6b5196b31bb | [
"MIT"
] | null | null | null | Proposal.ipynb | caseypen/MAE_298_Final_Project | 66c96aabc7bbcfe070c5170c03d5f6b5196b31bb | [
"MIT"
] | null | null | null | Proposal.ipynb | caseypen/MAE_298_Final_Project | 66c96aabc7bbcfe070c5170c03d5f6b5196b31bb | [
"MIT"
] | 1 | 2020-11-08T14:38:42.000Z | 2020-11-08T14:38:42.000Z | 26.145729 | 285 | 0.468672 | true | 2,055 | Qwen/Qwen-72B | 1. YES
2. YES | 0.934395 | 0.815232 | 0.761749 | __label__kor_Hang | 0.151337 | 0.608131 |
平面ロボットアームの運動学導出
```python
import sympy as sy
from sympy import pi, cos, sin, tan
from IPython.display import display
from sympy.printing.pycode import pycode
#q1, q2, q3, q4 = sy.symbols("q1, q2, q3, q4") # 関節角度
t = sy.Symbol("t")
q1 = sy.Function("q1")
q2 = sy.Function("q2")
q3 = sy.Function("q3")
q4 = sy.Function... | c984d200a9dfd5c1c8d5d1c4cf2b4bd6ed344c98 | 6,796 | ipynb | Jupyter Notebook | misc/sice_arm_kinematics.ipynb | YoshimitsuMatsutaIe/manipulator_dynamics | 587b3cedddd07c2aa09d1195289b0c312e0fc749 | [
"MIT"
] | null | null | null | misc/sice_arm_kinematics.ipynb | YoshimitsuMatsutaIe/manipulator_dynamics | 587b3cedddd07c2aa09d1195289b0c312e0fc749 | [
"MIT"
] | null | null | null | misc/sice_arm_kinematics.ipynb | YoshimitsuMatsutaIe/manipulator_dynamics | 587b3cedddd07c2aa09d1195289b0c312e0fc749 | [
"MIT"
] | null | null | null | 32.830918 | 778 | 0.424956 | true | 2,136 | Qwen/Qwen-72B | 1. YES
2. YES | 0.938124 | 0.782662 | 0.734234 | __label__kor_Hang | 0.06295 | 0.544205 |
# 11 Ordinary Differential Equations (ODEs)
[ODE](http://mathworld.wolfram.com/OrdinaryDifferentialEquation.html)s describe many phenomena in physics. They describe the changes of a **dependent variable** $y(t)$ as a function of a **single independent variable** (e.g. $t$ or $x$).
An ODE of **order** $n$
$$
F(t, y^{... | b969f6e98be1f8a06e51c610aef1730ca8b5d502 | 125,997 | ipynb | Jupyter Notebook | 11_ODEs/.ipynb_checkpoints/11-ODEs-checkpoint.ipynb | nachrisman/PHY494 | bac0dd5a7fe6f59f9e2ccaee56ebafcb7d97e2e7 | [
"CC-BY-4.0"
] | null | null | null | 11_ODEs/.ipynb_checkpoints/11-ODEs-checkpoint.ipynb | nachrisman/PHY494 | bac0dd5a7fe6f59f9e2ccaee56ebafcb7d97e2e7 | [
"CC-BY-4.0"
] | null | null | null | 11_ODEs/.ipynb_checkpoints/11-ODEs-checkpoint.ipynb | nachrisman/PHY494 | bac0dd5a7fe6f59f9e2ccaee56ebafcb7d97e2e7 | [
"CC-BY-4.0"
] | null | null | null | 145.157834 | 28,476 | 0.874576 | true | 3,818 | Qwen/Qwen-72B | 1. YES
2. YES | 0.779993 | 0.843895 | 0.658232 | __label__eng_Latn | 0.773268 | 0.367625 |
# 15 PDEs: Solution with Time Stepping
## Heat Equation
The **heat equation** can be derived from Fourier's law and energy conservation (see the [lecture notes on the heat equation (PDF)](15_PDEs_LectureNotes_HeatEquation.pdf))
$$
\frac{\partial T(\mathbf{x}, t)}{\partial t} = \frac{K}{C\rho} \nabla^2 T(\mathbf{x}, t... | dbeee048f81ca92544519ab22fb3ff43504c4e84 | 488,829 | ipynb | Jupyter Notebook | 15_PDEs/15_PDEs.ipynb | Py4Phy/PHY432-resources | c26d95eaf5c28e25da682a61190e12ad6758a938 | [
"CC-BY-4.0"
] | null | null | null | 15_PDEs/15_PDEs.ipynb | Py4Phy/PHY432-resources | c26d95eaf5c28e25da682a61190e12ad6758a938 | [
"CC-BY-4.0"
] | 1 | 2022-03-03T21:47:56.000Z | 2022-03-03T21:47:56.000Z | 15_PDEs/15_PDEs.ipynb | Py4Phy/PHY432-resources | c26d95eaf5c28e25da682a61190e12ad6758a938 | [
"CC-BY-4.0"
] | null | null | null | 677.048476 | 174,744 | 0.948424 | true | 3,668 | Qwen/Qwen-72B | 1. YES
2. YES | 0.90053 | 0.880797 | 0.793184 | __label__eng_Latn | 0.866937 | 0.681165 |
```julia
using Pkg
Pkg.activate(@__DIR__)
Pkg.instantiate()
using LinearAlgebra, Symbolics, DifferentialEquations, JLD2
```
[32m[1m Activating[22m[39m environment at `~/Research/symbolics_double_pendulum/Project.toml`
┌ Info: Precompiling Symbolics [0c5d862f-8b57-4792-8d23-62f2024744c7]
└ @ Base load... | 828d158277d8b02d1471296941c66b04dad3dfce | 14,101 | ipynb | Jupyter Notebook | double pendulum.ipynb | thowell/symbolics_double_pendulum | 1d8541f491fd61f7862ff2885d1f2c7cf30a4544 | [
"MIT"
] | 4 | 2021-04-21T20:56:02.000Z | 2021-11-22T14:19:25.000Z | double pendulum.ipynb | thowell/symbolics_double_pendulum | 1d8541f491fd61f7862ff2885d1f2c7cf30a4544 | [
"MIT"
] | null | null | null | double pendulum.ipynb | thowell/symbolics_double_pendulum | 1d8541f491fd61f7862ff2885d1f2c7cf30a4544 | [
"MIT"
] | null | null | null | 38.527322 | 2,243 | 0.491667 | true | 3,472 | Qwen/Qwen-72B | 1. YES
2. YES | 0.94079 | 0.826712 | 0.777762 | __label__eng_Latn | 0.0903 | 0.645334 |
```python
# Notebook imports and packages
import numpy as np
from sympy import symbols, diff
# symbols is for "turning variables into math symbols"
# diff differentiates functions (when using symbols).
# Go through the rest of the code to understand better.
```
# Partial Derivatives and Symbolic Computation
$$f(x, y... | 713aaf57f175751aea4b05c8fd71b405497e826c | 6,840 | ipynb | Jupyter Notebook | Section_04/Example_04_(05-08)/06-SymPy_derivatives.ipynb | ArielMAJ/Data-Science-and-Machine-Learning_Bootcamp | afae685c96d9fc8af0b2ee1be4d817df505c6c8d | [
"MIT"
] | null | null | null | Section_04/Example_04_(05-08)/06-SymPy_derivatives.ipynb | ArielMAJ/Data-Science-and-Machine-Learning_Bootcamp | afae685c96d9fc8af0b2ee1be4d817df505c6c8d | [
"MIT"
] | null | null | null | Section_04/Example_04_(05-08)/06-SymPy_derivatives.ipynb | ArielMAJ/Data-Science-and-Machine-Learning_Bootcamp | afae685c96d9fc8af0b2ee1be4d817df505c6c8d | [
"MIT"
] | null | null | null | 6,840 | 6,840 | 0.650292 | true | 1,070 | Qwen/Qwen-72B | 1. YES
2. YES | 0.946597 | 0.863392 | 0.817284 | __label__eng_Latn | 0.684796 | 0.737157 |
### Code setup
```python
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
sns.set_context("talk", font_scale=1.5, rc={"lines.linewidth": 2.5})
sns.set_style("whitegrid")
from IPython.display import HTML
from matplotlib import animation
%matplotlib inline
# Don't tinker, or do
#%matplotli... | c305f16a7b4d9c7e0b4a0e5290a4920f1602d73d | 44,297 | ipynb | Jupyter Notebook | lectures/05_timeintegration/code/time_integrators.ipynb | tp5uiuc/soft_systems_course | c9585c8fdc7fbc2fd539b4a1ed5e3b43a889a1ce | [
"MIT"
] | 3 | 2022-01-12T21:54:46.000Z | 2022-01-15T09:31:40.000Z | lectures/05_timeintegration/code/time_integrators.ipynb | tp5uiuc/soft_systems_course | c9585c8fdc7fbc2fd539b4a1ed5e3b43a889a1ce | [
"MIT"
] | null | null | null | lectures/05_timeintegration/code/time_integrators.ipynb | tp5uiuc/soft_systems_course | c9585c8fdc7fbc2fd539b4a1ed5e3b43a889a1ce | [
"MIT"
] | null | null | null | 34.418803 | 556 | 0.503872 | true | 8,679 | Qwen/Qwen-72B | 1. YES
2. YES | 0.899121 | 0.810479 | 0.728719 | __label__eng_Latn | 0.8782 | 0.53139 |
# Polynomial Optimization
## Technical note
The section "Sum-of-Squares approach" of notebook uses features of SumOfSquares.jl and PolyJuMP.jl that are not yet released.
Please do the following to use the "master" branch
```julia
Pkg.checkout("SumOfSquares")
Pkg.checkout("PolyJuMP")
```
You can undo these with the... | 27904efc04f64857560dbd0034b2ce3833646e24 | 14,510 | ipynb | Jupyter Notebook | examples/Polynomial_Optimization.ipynb | mforets/SumOfSquares.jl | d98ad9b6e5fef3bac2e4cbaf38faf599cabeada9 | [
"MIT"
] | null | null | null | examples/Polynomial_Optimization.ipynb | mforets/SumOfSquares.jl | d98ad9b6e5fef3bac2e4cbaf38faf599cabeada9 | [
"MIT"
] | null | null | null | examples/Polynomial_Optimization.ipynb | mforets/SumOfSquares.jl | d98ad9b6e5fef3bac2e4cbaf38faf599cabeada9 | [
"MIT"
] | null | null | null | 29.855967 | 262 | 0.573604 | true | 2,788 | Qwen/Qwen-72B | 1. YES
2. YES | 0.870597 | 0.867036 | 0.754839 | __label__eng_Latn | 0.983828 | 0.592076 |
# Frequentist Inference Case Study - Part A
## 1. Learning objectives
Welcome to part A of the Frequentist inference case study! The purpose of this case study is to help you apply the concepts associated with Frequentist inference in Python. Frequentist inference is the process of deriving conclusions about an unde... | cb62898eb8e3cddabe141738f4936ae2378999ce | 112,622 | ipynb | Jupyter Notebook | Frequentist Inference Case Study - Part A (3).ipynb | JasonCaldwellMBA/Frequentist_Case_Study | c8e89370f481985bfd366ccc76c6d00d640a88fd | [
"MIT"
] | null | null | null | Frequentist Inference Case Study - Part A (3).ipynb | JasonCaldwellMBA/Frequentist_Case_Study | c8e89370f481985bfd366ccc76c6d00d640a88fd | [
"MIT"
] | null | null | null | Frequentist Inference Case Study - Part A (3).ipynb | JasonCaldwellMBA/Frequentist_Case_Study | c8e89370f481985bfd366ccc76c6d00d640a88fd | [
"MIT"
] | null | null | null | 68.839853 | 14,387 | 0.77737 | true | 12,891 | Qwen/Qwen-72B | 1. YES
2. YES | 0.798187 | 0.727975 | 0.58106 | __label__eng_Latn | 0.999061 | 0.188328 |
<h1 align=center> Home Quiz 1 - Logistic Regression</h1>
<br>
$$
\text{Chatziefraimidis Lefteris 2209}\\
$$
## Problem 1: Gradient Descent
We will estimate the parameters $w_{0},w_{1},w_{2}$ using gradient descent for the following prediction model:
<br>
<br>
$$ y = w_{0} + w_{1}x_{1} + w_{2}x_{2} + w_{3}x_{1}^2 + \e... | d1239f05d6223b09b8c49b317973aab9eb4814ed | 133,460 | ipynb | Jupyter Notebook | Logistic Regression - Quiz 1/Quiz_1.ipynb | echatzief/Machine-Learning-Projects | 900493e4f14ef4faf1a794d85d103d966dca5be5 | [
"MIT"
] | null | null | null | Logistic Regression - Quiz 1/Quiz_1.ipynb | echatzief/Machine-Learning-Projects | 900493e4f14ef4faf1a794d85d103d966dca5be5 | [
"MIT"
] | null | null | null | Logistic Regression - Quiz 1/Quiz_1.ipynb | echatzief/Machine-Learning-Projects | 900493e4f14ef4faf1a794d85d103d966dca5be5 | [
"MIT"
] | null | null | null | 179.623149 | 39,672 | 0.877342 | true | 5,973 | Qwen/Qwen-72B | 1. YES
2. YES | 0.94079 | 0.771843 | 0.726142 | __label__eng_Latn | 0.586601 | 0.525404 |
```
import scipy
import numpy as np
import sympy
from sympy import *
```
```
ix, iy, iz = symbols('ix iy iz',real=True, constant = True)
hx, hy, hz = symbols('hx hy hz',real = True, constant = False)
```
```
h = Matrix([hx, hy, hz])
i = Matrix([ix, iy, iz])
tmp = 2.*(h.T*i)[0,0]
f = tmp*h - i
f = f.subs(ix,0).subs(... | 16c34562bb970b86c6a10ecc7bb09ea15baa25ea | 13,207 | ipynb | Jupyter Notebook | misc/TheJacobianOfTheSpecialReflection.ipynb | DaWelter/NaiveTrace | a904785a0e13c394b2c221bc918cddb41bc8b175 | [
"FSFAP"
] | 16 | 2018-04-25T08:14:14.000Z | 2022-01-29T06:19:16.000Z | misc/TheJacobianOfTheSpecialReflection.ipynb | DaWelter/NaiveTrace | a904785a0e13c394b2c221bc918cddb41bc8b175 | [
"FSFAP"
] | null | null | null | misc/TheJacobianOfTheSpecialReflection.ipynb | DaWelter/NaiveTrace | a904785a0e13c394b2c221bc918cddb41bc8b175 | [
"FSFAP"
] | null | null | null | 64.740196 | 2,855 | 0.447187 | true | 5,776 | Qwen/Qwen-72B | 1. YES
2. YES | 0.947381 | 0.679179 | 0.643441 | __label__kor_Hang | 0.205682 | 0.33326 |
# 量子・古典ハイブリッドの量子機械学習アルゴリズムを使って、新しい素粒子現象の発見を目指す
この実習では、**量子・古典ハイブリッドアルゴリズム**の応用である**量子機械学習**の基本的な実装を学んだのち、その活用例として、**素粒子実験での新粒子探索**への応用を考えます。ここで学ぶ量子機械学習の手法は、量子コンピュータを応用することで古典機械学習の性能を向上するという観点から提案された、**変分量子回路**を使った学習手法 [[1]](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.98.032309)です。その手法の元になる変分法と、それに基づいた変分量子固有... | b22376ad8779c4a6f0db14c162f922b42e73d6f5 | 25,514 | ipynb | Jupyter Notebook | source/jp/vqc_machine_learning.ipynb | kterashi/qc-workbook | 07d8948f1e24e3b6862ab6fa307031cfd35f8b55 | [
"Apache-2.0"
] | null | null | null | source/jp/vqc_machine_learning.ipynb | kterashi/qc-workbook | 07d8948f1e24e3b6862ab6fa307031cfd35f8b55 | [
"Apache-2.0"
] | null | null | null | source/jp/vqc_machine_learning.ipynb | kterashi/qc-workbook | 07d8948f1e24e3b6862ab6fa307031cfd35f8b55 | [
"Apache-2.0"
] | null | null | null | 35.884669 | 438 | 0.591754 | true | 9,560 | Qwen/Qwen-72B | 1. YES
2. YES | 0.90599 | 0.581303 | 0.526655 | __label__yue_Hant | 0.31526 | 0.061925 |
```python
# Notebook imports and packages
import numpy as np
from sympy import symbols, diff, lambdify
```
# Please lambdify your derivatives
$$f(x, y)=\frac{1}{3^{-x^2-y^2}+1}$$
<hr color="lightblue">
$$\frac{\partial f(x, y)}{\partial x}=\frac{2x\ln \left(3\right)\cdot \:3^{-x^2-y^2}}{\left(3^{-x^2-y^2}+1\right)^2}... | 667150e54731e46049ef7d4906c6e19ad9fb93c0 | 5,185 | ipynb | Jupyter Notebook | Section_04/Example_04_(05-08)/07-GD_and_Lambdify.ipynb | ArielMAJ/Data-Science-and-Machine-Learning_Bootcamp | afae685c96d9fc8af0b2ee1be4d817df505c6c8d | [
"MIT"
] | null | null | null | Section_04/Example_04_(05-08)/07-GD_and_Lambdify.ipynb | ArielMAJ/Data-Science-and-Machine-Learning_Bootcamp | afae685c96d9fc8af0b2ee1be4d817df505c6c8d | [
"MIT"
] | null | null | null | Section_04/Example_04_(05-08)/07-GD_and_Lambdify.ipynb | ArielMAJ/Data-Science-and-Machine-Learning_Bootcamp | afae685c96d9fc8af0b2ee1be4d817df505c6c8d | [
"MIT"
] | null | null | null | 5,185 | 5,185 | 0.694889 | true | 647 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91611 | 0.833325 | 0.763417 | __label__eng_Latn | 0.673676 | 0.612005 |
# EPA-1316 Introduction to *Urban* Data Science
## Lab 6: plotting, Simple Linear Regression,K-NN Regression
**TU Delft**<br>
**Q1 2020**<br>
**Instructor:** Trivik Verma <br>
**TAs:** Aarthi Meenakshi Sundaram, Jelle Egbers, Tess Kim, Lotte Lourens, Amir Ebrahimi Fard, Giulia Reggiani, Bramka Jafino, Talia Kaufma... | 2d3260430637f709935bd8768edf1283c443e95c | 275,678 | ipynb | Jupyter Notebook | static/epa1316-2020/labs/lab-06/lab-06.ipynb | trivikverma/researchgroup | af14454b351c4b0f673c321f4eeba8bdeee2b05c | [
"MIT"
] | null | null | null | static/epa1316-2020/labs/lab-06/lab-06.ipynb | trivikverma/researchgroup | af14454b351c4b0f673c321f4eeba8bdeee2b05c | [
"MIT"
] | null | null | null | static/epa1316-2020/labs/lab-06/lab-06.ipynb | trivikverma/researchgroup | af14454b351c4b0f673c321f4eeba8bdeee2b05c | [
"MIT"
] | null | null | null | 91.923308 | 35,456 | 0.805371 | true | 14,974 | Qwen/Qwen-72B | 1. YES
2. YES | 0.877477 | 0.851953 | 0.747569 | __label__eng_Latn | 0.931206 | 0.575185 |
<h1 align=center style="color: #005496; font-size: 4.2em;">Machine Learning with Python</h1>
<h2 align=center>Laboratory on Numpy / Matplotlib / Scikit-learn</h2>
***
***
## Introduction
In the past few years, Python has become the de-facto standard programming language for data analytics. Python's success is due... | 57f0a1bc0aa5f4c932432cee7b690c1c55e67c61 | 153,576 | ipynb | Jupyter Notebook | sklearn-lab.ipynb | paolodragone/machine-learning-labs | 83a6fa410f86d45866379b80abe700ae26a52e9d | [
"MIT"
] | 1 | 2019-05-30T04:32:37.000Z | 2019-05-30T04:32:37.000Z | sklearn-lab.ipynb | paolodragone/machine-learning-labs | 83a6fa410f86d45866379b80abe700ae26a52e9d | [
"MIT"
] | null | null | null | sklearn-lab.ipynb | paolodragone/machine-learning-labs | 83a6fa410f86d45866379b80abe700ae26a52e9d | [
"MIT"
] | null | null | null | 91.632458 | 23,908 | 0.825708 | true | 8,068 | Qwen/Qwen-72B | 1. YES
2. YES | 0.787931 | 0.853913 | 0.672824 | __label__eng_Latn | 0.942275 | 0.401528 |
# KW-Distance: Two alternatives LP Models
In this notebook, we write a basic Linear Programming (LP) model to approximate the Kantorovich-Wasserstein distance of order 1 between a pair of discrete measures, such as, for instance, a pair of gray scale images.
In order to assess computationally the deviance of our model... | b9b97caba674281547e87cb52d74fbc9f3a4971d | 488,005 | ipynb | Jupyter Notebook | notebook/KW-Distance_LP_models.ipynb | stegua/dotlib | 754d93f16522714668e99a3c313a2acdc2cd0bd1 | [
"MIT"
] | 4 | 2018-02-21T20:19:36.000Z | 2021-05-07T03:23:38.000Z | notebook/KW-Distance_LP_models.ipynb | stegua/dotlib | 754d93f16522714668e99a3c313a2acdc2cd0bd1 | [
"MIT"
] | null | null | null | notebook/KW-Distance_LP_models.ipynb | stegua/dotlib | 754d93f16522714668e99a3c313a2acdc2cd0bd1 | [
"MIT"
] | null | null | null | 764.898119 | 283,456 | 0.950357 | true | 4,217 | Qwen/Qwen-72B | 1. YES
2. YES | 0.923039 | 0.870597 | 0.803595 | __label__eng_Latn | 0.983604 | 0.705354 |
```python
# This cell is for the Google Colaboratory
# https://stackoverflow.com/a/63519730
if 'google.colab' in str(get_ipython()):
# https://colab.research.google.com/notebooks/io.ipynb
import google.colab.drive as gcdrive
# may need to visit a link for the Google Colab authorization code
gcdrive.mount("/cont... | 95a981a598e75a7fea6852d705fb30a020fc4b67 | 26,044 | ipynb | Jupyter Notebook | 45_sympy/10_sympy.ipynb | kangwonlee/2109eca-nmisp-template | 2e078870757fa06222df62d0ff8f4f4f288af51a | [
"BSD-3-Clause"
] | null | null | null | 45_sympy/10_sympy.ipynb | kangwonlee/2109eca-nmisp-template | 2e078870757fa06222df62d0ff8f4f4f288af51a | [
"BSD-3-Clause"
] | null | null | null | 45_sympy/10_sympy.ipynb | kangwonlee/2109eca-nmisp-template | 2e078870757fa06222df62d0ff8f4f4f288af51a | [
"BSD-3-Clause"
] | null | null | null | 17.385848 | 190 | 0.453963 | true | 3,179 | Qwen/Qwen-72B | 1. YES
2. YES | 0.843895 | 0.800692 | 0.6757 | __label__kor_Hang | 0.367905 | 0.408209 |
Let's use SymPy to derive the relation between potential V and charge density R
```
%pylab inline
from sympy.interactive import init_printing
init_printing()
from sympy import pi, var, S, Piecewise, piecewise_fold
var("r R")
Vh = Piecewise((-S(2)/3 * pi * (3*R**2 - r**2), r <= R), (-S(4)/3 * pi * R**3 / r, True))
def... | 950795722b6067af6c17737b0fa712a6b64ebbe8 | 21,726 | ipynb | Jupyter Notebook | tutorial_exercises/FFT charged sphere.ipynb | certik/scipy-2013-tutorial | 26a1cab3a16402afdc20088cedf47acd9bc58483 | [
"BSD-3-Clause"
] | 23 | 2015-02-28T08:53:05.000Z | 2021-12-05T05:37:59.000Z | sympy/FFT charged sphere.ipynb | certik/scipy-in-13 | 418c139ab6e1b0c9acd53e7e1a02b8b930005096 | [
"BSD-3-Clause"
] | 1 | 2021-04-17T15:05:46.000Z | 2021-04-17T15:05:46.000Z | sympy/FFT charged sphere.ipynb | certik/scipy-in-13 | 418c139ab6e1b0c9acd53e7e1a02b8b930005096 | [
"BSD-3-Clause"
] | 14 | 2015-03-11T00:25:21.000Z | 2021-08-25T14:52:40.000Z | 110.846939 | 16,352 | 0.823115 | true | 847 | Qwen/Qwen-72B | 1. YES
2. YES | 0.924142 | 0.833325 | 0.77011 | __label__eng_Latn | 0.33916 | 0.627556 |
Copyright **Paolo Raiteri**, January 2022
# Langmuir isotherm virtual lab
The Langmuir isotherm is one of the simplest models that can be used to describe the adsorption of molecules on surfaces, either in the gas phase or in solutions.
It is based on 5 key assumptions:
1. The surface is flat
2. The adsorbate is imm... | 6de0722562fa7881bb45c75be214303dc81ee87a | 7,776 | ipynb | Jupyter Notebook | week_04_surfaceAdsorption/langmuir.ipynb | praiteri/TeachingNotebook | 75ee8baf8ef81154dffcac556d4739bf73eba712 | [
"MIT"
] | null | null | null | week_04_surfaceAdsorption/langmuir.ipynb | praiteri/TeachingNotebook | 75ee8baf8ef81154dffcac556d4739bf73eba712 | [
"MIT"
] | null | null | null | week_04_surfaceAdsorption/langmuir.ipynb | praiteri/TeachingNotebook | 75ee8baf8ef81154dffcac556d4739bf73eba712 | [
"MIT"
] | 1 | 2022-02-23T11:36:12.000Z | 2022-02-23T11:36:12.000Z | 49.528662 | 382 | 0.662551 | true | 1,455 | Qwen/Qwen-72B | 1. YES
2. YES | 0.76908 | 0.689306 | 0.530131 | __label__eng_Latn | 0.998747 | 0.070002 |
Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved BSD-3 license. (c) Lorena A. Barba, Gilbert F. Forsyth 2017. Thanks to NSF for support via CAREER award #1149784.
[@LorenaABarba](https://twitter.com/LorenaABarba)
12 steps to Navier–Stokes
=====
***
... | 0a72f4390a165553ea7654e80b5ac28d21c0ae89 | 98,619 | ipynb | Jupyter Notebook | lessons/05_Step_4.ipynb | XuesongDing/CFDPython | 36a2b7b7b9a562db509a38a7e95dbc190523ac29 | [
"CC-BY-3.0"
] | null | null | null | lessons/05_Step_4.ipynb | XuesongDing/CFDPython | 36a2b7b7b9a562db509a38a7e95dbc190523ac29 | [
"CC-BY-3.0"
] | null | null | null | lessons/05_Step_4.ipynb | XuesongDing/CFDPython | 36a2b7b7b9a562db509a38a7e95dbc190523ac29 | [
"CC-BY-3.0"
] | 1 | 2021-12-18T02:08:34.000Z | 2021-12-18T02:08:34.000Z | 157.287081 | 50,756 | 0.851428 | true | 4,938 | Qwen/Qwen-72B | 1. YES
2. YES | 0.805632 | 0.731059 | 0.588964 | __label__eng_Latn | 0.838245 | 0.206692 |
# piston example with Gauss-Legendre collocation
```python
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.animation as anim
import sympy
sympy.init_printing()
from IPython.display import display
import numpy
import sys
sys.path.insert(0, './code')
from gauss_legendre import gauss_legendre
fr... | ddcd7beb1b894a535ca4b14fc3adecbc074dd4da | 239,801 | ipynb | Jupyter Notebook | piston_animation.ipynb | MarkusLohmayer/master-thesis-code | b107d1b582064daf9ad4414e1c9f332ef0be8660 | [
"MIT"
] | 1 | 2020-11-14T15:56:07.000Z | 2020-11-14T15:56:07.000Z | piston_animation.ipynb | MarkusLohmayer/master-thesis-code | b107d1b582064daf9ad4414e1c9f332ef0be8660 | [
"MIT"
] | null | null | null | piston_animation.ipynb | MarkusLohmayer/master-thesis-code | b107d1b582064daf9ad4414e1c9f332ef0be8660 | [
"MIT"
] | null | null | null | 389.920325 | 132,088 | 0.931698 | true | 2,808 | Qwen/Qwen-72B | 1. YES
2. YES | 0.882428 | 0.727975 | 0.642386 | __label__yue_Hant | 0.208587 | 0.330808 |
```python
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
```
<style>.container { width:100% !important; }</style>
```python
import numpy as np
import matplotlib.pyplot as plt
```
# Funciones necesarias para que las demas funciones funcionen .
... | 10508c033b7e316ee63d3866f2b1c49e56c4e106 | 211,583 | ipynb | Jupyter Notebook | Curso_Metodos_Numericos_2020_I/codigos_antes_parcial/Minimos_Cuadrados.ipynb | alonso121198/Regresion-lineal-en-python | 9f39c1ddc33e68263fa81730e62efc28c051ec6e | [
"MIT"
] | null | null | null | Curso_Metodos_Numericos_2020_I/codigos_antes_parcial/Minimos_Cuadrados.ipynb | alonso121198/Regresion-lineal-en-python | 9f39c1ddc33e68263fa81730e62efc28c051ec6e | [
"MIT"
] | null | null | null | Curso_Metodos_Numericos_2020_I/codigos_antes_parcial/Minimos_Cuadrados.ipynb | alonso121198/Regresion-lineal-en-python | 9f39c1ddc33e68263fa81730e62efc28c051ec6e | [
"MIT"
] | null | null | null | 266.47733 | 69,004 | 0.91133 | true | 5,941 | Qwen/Qwen-72B | 1. YES
2. YES | 0.740174 | 0.782662 | 0.579307 | __label__spa_Latn | 0.710379 | 0.184254 |
# Computational Astrophysics
## Interpolation 01
---
## Eduard Larrañaga
Observatorio Astronómico Nacional\
Facultad de Ciencias\
Universidad Nacional de Colombia
---
### About this notebook
In this notebook we present some of the interpolation techniques.
---
## Interpolation
Experimental astrophysical data us... | 2f8b64bca86c0f2e8d2cea76f8b56b39a25f7879 | 154,982 | ipynb | Jupyter Notebook | 05._Interpolation/presentation/Interpolation01.ipynb | ashcat2005/ComputationalAstrophysics | edda507d0d0a433dfd674a2451d750cf6ad3f1b7 | [
"MIT"
] | 2 | 2020-09-23T02:49:10.000Z | 2021-08-21T06:04:39.000Z | 05._Interpolation/presentation/Interpolation01.ipynb | ashcat2005/ComputationalAstrophysics | edda507d0d0a433dfd674a2451d750cf6ad3f1b7 | [
"MIT"
] | null | null | null | 05._Interpolation/presentation/Interpolation01.ipynb | ashcat2005/ComputationalAstrophysics | edda507d0d0a433dfd674a2451d750cf6ad3f1b7 | [
"MIT"
] | 2 | 2020-12-05T14:06:28.000Z | 2022-01-25T04:51:58.000Z | 234.111782 | 23,588 | 0.908796 | true | 4,182 | Qwen/Qwen-72B | 1. YES
2. YES | 0.938124 | 0.841826 | 0.789737 | __label__eng_Latn | 0.952581 | 0.673156 |
# Matrices Solutions
```
from sympy import *
init_printing()
```
Use `row_del` and `row_insert` to go from one Matrix to the other.
```
def matrix1(M):
"""
>>> M = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> M
[1, 2, 3]
[4, 5, 6]
[7, 8, 9]
>>> matrix1(M)
[4, 5, 6]
[0, 0, 0]
... | 144fbd18f86ca153d1425446a07ebd01ff7cea1d | 70,162 | ipynb | Jupyter Notebook | tutorial_exercises/Advanced-Matrices Solutions.ipynb | gvvynplaine/scipy-2016-tutorial | aa417427a1de2dcab2a9640b631b809d525d7929 | [
"BSD-3-Clause"
] | 53 | 2016-06-21T21:11:02.000Z | 2021-02-04T07:51:03.000Z | tutorial_exercises/Advanced-Matrices Solutions.ipynb | gvvynplaine/scipy-2016-tutorial | aa417427a1de2dcab2a9640b631b809d525d7929 | [
"BSD-3-Clause"
] | 11 | 2016-07-02T20:24:06.000Z | 2016-07-11T11:31:44.000Z | tutorial_exercises/Advanced-Matrices Solutions.ipynb | gvvynplaine/scipy-2016-tutorial | aa417427a1de2dcab2a9640b631b809d525d7929 | [
"BSD-3-Clause"
] | 36 | 2016-06-25T09:04:24.000Z | 2021-08-09T06:46:01.000Z | 71.887295 | 11,276 | 0.743608 | true | 3,778 | Qwen/Qwen-72B | 1. YES
2. YES | 0.849971 | 0.880797 | 0.748652 | __label__eng_Latn | 0.826184 | 0.577702 |
# Analytic approx. for filters
The aim here is to derive analytic formulae for products of the filtering, given $W(kR)$ models and (very) simple $P(k)$. These will be useful for basic testing (against known analytic solution), but also, if $P(k)$ can be set close enough to reasonable models, for checking appropriate r... | 0459f65557f92b9374d417fde77fa9aa6864e317 | 20,420 | ipynb | Jupyter Notebook | development/analytic_filter.ipynb | liuxx479/hmf-1 | 8b24f5df42cdf73d507ffc4a7c6138573769bb2c | [
"MIT"
] | 45 | 2015-01-06T06:13:54.000Z | 2021-01-08T04:31:19.000Z | development/analytic_filter.ipynb | liuxx479/hmf-1 | 8b24f5df42cdf73d507ffc4a7c6138573769bb2c | [
"MIT"
] | 113 | 2015-03-12T13:31:41.000Z | 2021-01-21T22:28:14.000Z | development/analytic_filter.ipynb | liuxx479/hmf-1 | 8b24f5df42cdf73d507ffc4a7c6138573769bb2c | [
"MIT"
] | 28 | 2015-03-14T05:56:51.000Z | 2020-12-14T20:16:15.000Z | 50.544554 | 5,044 | 0.685553 | true | 966 | Qwen/Qwen-72B | 1. YES
2. YES | 0.932453 | 0.865224 | 0.806781 | __label__eng_Latn | 0.658195 | 0.712755 |
# Example #1: Neural Network for $y = \sin(x)$
Same example as yesterday, a sine-curve with 10 points as training values:
```
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0,6.6, 0.6)
y = np.sin(x)
xplot = np.arange(0, 6.6, 0.01)
yplot = np.sin(xplot)
plt.scatter(x,y, color="b", label="Training... | b276771ba95a51c67f9e41b7538da84b8eddf1b0 | 10,841 | ipynb | Jupyter Notebook | machine_learning_example_sinx.ipynb | andersx/python-intro | 8409c89da7dd9cea21e3702a0f0f47aae816eb58 | [
"CC0-1.0"
] | 11 | 2020-05-03T11:59:01.000Z | 2021-11-15T12:33:39.000Z | machine_learning_example_sinx.ipynb | andersx/python-intro | 8409c89da7dd9cea21e3702a0f0f47aae816eb58 | [
"CC0-1.0"
] | null | null | null | machine_learning_example_sinx.ipynb | andersx/python-intro | 8409c89da7dd9cea21e3702a0f0f47aae816eb58 | [
"CC0-1.0"
] | 7 | 2020-05-10T21:15:15.000Z | 2021-12-05T15:13:54.000Z | 27.726343 | 187 | 0.463241 | true | 1,328 | Qwen/Qwen-72B | 1. YES
2. YES | 0.945801 | 0.882428 | 0.834601 | __label__eng_Latn | 0.77758 | 0.777392 |
(Other_Activation_Functions)=
# Chapter 16 -- Other Activation Functions
The other solution for the vanishing gradient is to use other activation functions. We like the old activation function sigmoid $\sigma(h)$ because first, it returns $0.5$ when $h=0$ (i.e. $\sigma(0)$) and second, it gives a higher probability w... | 7d8e36ce108625ca1f3b777d1aea4ac18a6a3aa5 | 32,946 | ipynb | Jupyter Notebook | notebooks/e_extra/pytorch_image_filtering_ml/Chapter 16 -- Other Activation Functions.ipynb | primer-computational-mathematics/book | 305941b4f1fc4f15d472fd11f2c6e90741fb8b64 | [
"MIT"
] | 3 | 2020-08-02T07:32:14.000Z | 2021-11-16T16:40:43.000Z | notebooks/e_extra/pytorch_image_filtering_ml/Chapter 16 -- Other Activation Functions.ipynb | primer-computational-mathematics/book | 305941b4f1fc4f15d472fd11f2c6e90741fb8b64 | [
"MIT"
] | 5 | 2020-07-27T10:45:26.000Z | 2020-08-12T15:09:14.000Z | notebooks/e_extra/pytorch_image_filtering_ml/Chapter 16 -- Other Activation Functions.ipynb | primer-computational-mathematics/book | 305941b4f1fc4f15d472fd11f2c6e90741fb8b64 | [
"MIT"
] | 4 | 2020-08-05T13:57:32.000Z | 2022-02-02T19:03:57.000Z | 151.12844 | 14,764 | 0.891884 | true | 892 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91611 | 0.882428 | 0.808401 | __label__eng_Latn | 0.998262 | 0.716518 |
```python
%%time
import time
for _ in range(1000):
time.sleep(0.01)# sleep for 0.01 seconds
from sympy import *
from sympy import init_printing; init_printing(use_latex = 'mathjax')
from sympy.plotting import plot
n = int(input('Qué número de valores de energía desea aproximar?'))
l, m, hbar, k = symbols('l m hb... | 05de0a76f7f40cb00eec222ba1f437609e07c451 | 89,164 | ipynb | Jupyter Notebook | Huckel_M0/Variational+Theory+beta.ipynb | lazarusA/Density-functional-theory | c74fd44a66f857de570dc50471b24391e3fa901f | [
"MIT"
] | null | null | null | Huckel_M0/Variational+Theory+beta.ipynb | lazarusA/Density-functional-theory | c74fd44a66f857de570dc50471b24391e3fa901f | [
"MIT"
] | null | null | null | Huckel_M0/Variational+Theory+beta.ipynb | lazarusA/Density-functional-theory | c74fd44a66f857de570dc50471b24391e3fa901f | [
"MIT"
] | null | null | null | 443.60199 | 22,450 | 0.923635 | true | 982 | Qwen/Qwen-72B | 1. YES
2. YES | 0.884039 | 0.581303 | 0.513895 | __label__eng_Latn | 0.190077 | 0.032279 |
```python
from sympy import *
x, y, z, t = symbols('x y z t')
```
## Mechanics
The module called [`sympy.physics.mechanics`](http://pyvideo.org/video/2653/dynamics-and-control-with-python)
contains elaborate tools for describing mechanical systems,
manipulating reference frames, forces, and torques.
These specialize... | 837a66368ba3abcecf839362518797a1c60b708f | 70,039 | ipynb | Jupyter Notebook | notebooks/Mechanics.ipynb | minireference/sympytut_notebooks | 6669e7bfccef9e70ae029ac5cbb54cb6cbc31652 | [
"BSD-3-Clause"
] | 4 | 2016-08-29T12:04:19.000Z | 2020-02-23T05:14:52.000Z | notebooks/Mechanics.ipynb | minireference/sympytut_notebooks | 6669e7bfccef9e70ae029ac5cbb54cb6cbc31652 | [
"BSD-3-Clause"
] | null | null | null | notebooks/Mechanics.ipynb | minireference/sympytut_notebooks | 6669e7bfccef9e70ae029ac5cbb54cb6cbc31652 | [
"BSD-3-Clause"
] | null | null | null | 68.936024 | 4,280 | 0.795428 | true | 2,859 | Qwen/Qwen-72B | 1. YES
2. YES | 0.909907 | 0.839734 | 0.76408 | __label__eng_Latn | 0.987631 | 0.613546 |
```python
import numpy as np
from sympy import *
init_printing(use_latex='mathjax')
```
```python
x = symbols('x')
f = x ** 6 / 6 - 3 * x ** 4 - 2 * x ** 3 / 3 + 27 * x ** 2 / 2 + 18 * x - 30
f
```
$$\frac{x^{6}}{6} - 3 x^{4} - \frac{2 x^{3}}{3} + \frac{27 x^{2}}{2} + 18 x - 30$$
```python
df = diff(f, x)
df
... | 9729b823f8d2194511cbe0e42bfa223528155b3b | 2,342 | ipynb | Jupyter Notebook | Certification 2/Week5.1 - Newton-Raphson method.ipynb | The-Brains/MathForMachineLearning | 5cbd9006f166059efaa2f312b741e64ce584aa1f | [
"MIT"
] | 6 | 2018-04-16T02:53:59.000Z | 2021-05-16T06:51:57.000Z | Certification 2/Week5.1 - Newton-Raphson method.ipynb | The-Brains/MathForMachineLearning | 5cbd9006f166059efaa2f312b741e64ce584aa1f | [
"MIT"
] | null | null | null | Certification 2/Week5.1 - Newton-Raphson method.ipynb | The-Brains/MathForMachineLearning | 5cbd9006f166059efaa2f312b741e64ce584aa1f | [
"MIT"
] | 4 | 2019-05-20T02:06:55.000Z | 2020-05-18T06:21:41.000Z | 19.516667 | 94 | 0.401793 | true | 217 | Qwen/Qwen-72B | 1. YES
2. YES | 0.960361 | 0.882428 | 0.847449 | __label__yue_Hant | 0.561749 | 0.807242 |
```python
# zero divisor (영인자)
# AB = 0 A \neq 0, B \neq 0
import sympy as sm
M1 = sm.Matrix([[1,1],[2,2]])
M2 = sm.Matrix([[1,1],[-1,-1]])
M1*M2
```
$\displaystyle \left[\begin{matrix}0 & 0\\0 & 0\end{matrix}\right]$
### 행고정: 행벡터, 열고정: 열벡터
> ### $
\left [
\begin{array}{}
a_{11} & a_{12} & a_{13} & a_{14} & a_... | 61c94f768a62fe136555a3b232019610325cdfec | 212,846 | ipynb | Jupyter Notebook | python/Vectors/Matrix.ipynb | karng87/nasm_game | a97fdb09459efffc561d2122058c348c93f1dc87 | [
"MIT"
] | null | null | null | python/Vectors/Matrix.ipynb | karng87/nasm_game | a97fdb09459efffc561d2122058c348c93f1dc87 | [
"MIT"
] | null | null | null | python/Vectors/Matrix.ipynb | karng87/nasm_game | a97fdb09459efffc561d2122058c348c93f1dc87 | [
"MIT"
] | null | null | null | 209.082515 | 161,651 | 0.890033 | true | 8,562 | Qwen/Qwen-72B | 1. YES
2. YES | 0.782662 | 0.654895 | 0.512562 | __label__kor_Hang | 0.181598 | 0.029181 |
```python
# This cell is added by sphinx-gallery
# It can be customized to whatever you like
%matplotlib inline
```
Noisy circuits
==============
.. meta::
:property="og:description": Learn how to simulate noisy quantum circuits
:property="og:image": https://pennylane.ai/qml/_images/N-Nisq.png
.. related::
... | 5890b361c3a2c89961812c83e7933d87cf0b4bc7 | 14,261 | ipynb | Jupyter Notebook | 98_quantum/99_tutorial_noisy_circuits.ipynb | dpai/workshop | d4936da77dac759ba2bac95a9584fde8e86c6b2b | [
"Apache-2.0"
] | 2,327 | 2020-03-01T09:47:34.000Z | 2021-11-25T12:38:42.000Z | 98_quantum/99_tutorial_noisy_circuits.ipynb | trideau/Data-Science-with-AWS-Workshop | 7dbe7989fa99e88544da8bf262beec907c536093 | [
"Apache-2.0"
] | 209 | 2020-03-01T17:14:12.000Z | 2021-11-08T20:35:42.000Z | 98_quantum/99_tutorial_noisy_circuits.ipynb | trideau/Data-Science-with-AWS-Workshop | 7dbe7989fa99e88544da8bf262beec907c536093 | [
"Apache-2.0"
] | 686 | 2020-03-03T17:24:51.000Z | 2021-11-25T23:39:12.000Z | 73.133333 | 2,169 | 0.693289 | true | 3,050 | Qwen/Qwen-72B | 1. YES
2. YES | 0.831143 | 0.746139 | 0.620148 | __label__eng_Latn | 0.98676 | 0.279143 |
# Function Representation and Manipulation
```
%matplotlib inline
```
```
import numpy as np
import matplotlib
matplotlib.rcParams.update({'font.size': 14})
import matplotlib.pyplot as plt
```
From a mathematical point of view, a central point in numerical methods is how we represent a general function $f(x)$. As ... | 86e908a6ef7593ac55c818a6431eb890449e4a6d | 36,170 | ipynb | Jupyter Notebook | Lectures/Function Representation and Manipulation.ipynb | alistairwalsh/NumericalMethods | fa10f9dfc4512ea3a8b54287be82f9511858bd22 | [
"MIT"
] | 1 | 2021-12-01T09:15:04.000Z | 2021-12-01T09:15:04.000Z | Lectures/Function Representation and Manipulation.ipynb | indranilsinharoy/NumericalMethods | 989e0205565131057c9807ed9d55b6c1a5a38d42 | [
"MIT"
] | null | null | null | Lectures/Function Representation and Manipulation.ipynb | indranilsinharoy/NumericalMethods | 989e0205565131057c9807ed9d55b6c1a5a38d42 | [
"MIT"
] | 1 | 2021-04-13T02:58:54.000Z | 2021-04-13T02:58:54.000Z | 198.736264 | 28,969 | 0.880398 | true | 1,258 | Qwen/Qwen-72B | 1. YES
2. YES | 0.851953 | 0.855851 | 0.729145 | __label__eng_Latn | 0.998205 | 0.532379 |
# Random Signals and LTI-Systems
*This jupyter notebook is part of a [collection of notebooks](../index.ipynb) on various topics of Digital Signal Processing. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*
## Power Spectral Densitity
For a wide-sense st... | 08c6421f93e6c749fa5a05b2e8af0df1b858c873 | 183,023 | ipynb | Jupyter Notebook | random_signals_LTI_systems/power_spectral_densities.ipynb | ZeroCommits/digital-signal-processing-lecture | e1e65432a5617a309ec02327a14962e37a0f7ec5 | [
"MIT"
] | 630 | 2016-01-05T17:11:43.000Z | 2022-03-30T07:48:27.000Z | random_signals_LTI_systems/power_spectral_densities.ipynb | alirezaopmc/digital-signal-processing-lecture | e1e65432a5617a309ec02327a14962e37a0f7ec5 | [
"MIT"
] | 12 | 2016-11-07T15:49:55.000Z | 2022-03-10T13:05:50.000Z | random_signals_LTI_systems/power_spectral_densities.ipynb | alirezaopmc/digital-signal-processing-lecture | e1e65432a5617a309ec02327a14962e37a0f7ec5 | [
"MIT"
] | 172 | 2015-12-26T21:05:40.000Z | 2022-03-10T23:13:30.000Z | 61.936717 | 25,378 | 0.629762 | true | 2,932 | Qwen/Qwen-72B | 1. YES
2. YES | 0.835484 | 0.824462 | 0.688824 | __label__eng_Latn | 0.767944 | 0.438701 |
```python
%load_ext rpy2.ipython
%matplotlib inline
```
```python
import matplotlib.pyplot as plt
import numpy as np
import numpy.random as rnd
from scipy import stats
import sympy as sym
from IPython.display import Image
plt.rcParams['figure.figsize'] = (20, 7)
```
# Rare-event simulation
## Lecture 3
### Patrick... | 49164ffaa506e41229c50bc52536683fb5e1be16 | 214,142 | ipynb | Jupyter Notebook | 2019/slides/l3.ipynb | Pat-Laub/RareEvents | 19e4f6bda4213dcd4a903bc3f1cde8cedd0dfca6 | [
"CC0-1.0"
] | 4 | 2019-04-10T23:24:56.000Z | 2020-06-09T12:41:20.000Z | 2019/slides/l3.ipynb | Pat-Laub/RareEvents | 19e4f6bda4213dcd4a903bc3f1cde8cedd0dfca6 | [
"CC0-1.0"
] | null | null | null | 2019/slides/l3.ipynb | Pat-Laub/RareEvents | 19e4f6bda4213dcd4a903bc3f1cde8cedd0dfca6 | [
"CC0-1.0"
] | 1 | 2020-04-19T07:08:31.000Z | 2020-04-19T07:08:31.000Z | 210.769685 | 46,951 | 0.9097 | true | 4,459 | Qwen/Qwen-72B | 1. YES
2. YES | 0.815232 | 0.743168 | 0.605855 | __label__eng_Latn | 0.509851 | 0.245934 |
<center></center>
## Машинное обучение
### Семинар 13. ЕМ-алгоритм
<br />
<br />
9 декабря 2021
Будем решать задачу восставновления картинки лица по набору зашумленных картинок (взято с курса deep bayes 2018 https://github.com/bayesgroup/deepbayes-2018).
У вас есть $K$ фотографий, поврежденных электромагнитным шум... | d0d4b116cceef3bcd1fae78ac7f3c15eec0ebfb3 | 632,475 | ipynb | Jupyter Notebook | 2021-fall-part-1/seminars/13_em_algo/13_em_algo_practice.ipynb | bagrorg/ml-course | 9a2aa7379ea0dee6968eef3a4ae5926e83c391ca | [
"MIT"
] | 4 | 2021-09-16T07:03:16.000Z | 2021-12-13T10:33:51.000Z | 2021-fall-part-1/seminars/13_em_algo/13_em_algo_practice.ipynb | bagrorg/ml-course | 9a2aa7379ea0dee6968eef3a4ae5926e83c391ca | [
"MIT"
] | null | null | null | 2021-fall-part-1/seminars/13_em_algo/13_em_algo_practice.ipynb | bagrorg/ml-course | 9a2aa7379ea0dee6968eef3a4ae5926e83c391ca | [
"MIT"
] | 13 | 2021-09-02T07:29:24.000Z | 2021-12-13T15:26:00.000Z | 612.863372 | 358,160 | 0.943418 | true | 4,453 | Qwen/Qwen-72B | 1. YES
2. YES | 0.771843 | 0.754915 | 0.582676 | __label__eng_Latn | 0.310221 | 0.192082 |
# Sparse-Group Lasso Inductive Matrix Completion via ADMM
```python
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
```
Fix the random state
```python
random_state = np.random.RandomState(0x0BADCAFE)
```
## Problem?
```python
PROBLEM = "classification" if True else "regression"
```
### S... | 1c1d0af6afb28a4056d794d859b247b5e497157e | 667,094 | ipynb | Jupyter Notebook | experiments/sgimc_by_qaadmm_prototype.ipynb | ivannz/SGIMC | cde56459d1d49576a5a6979a353ac27253233f3d | [
"MIT"
] | 11 | 2018-05-03T14:29:01.000Z | 2018-12-11T11:15:53.000Z | experiments/sgimc_by_qaadmm_prototype.ipynb | ivannz/SGIMC | cde56459d1d49576a5a6979a353ac27253233f3d | [
"MIT"
] | null | null | null | experiments/sgimc_by_qaadmm_prototype.ipynb | ivannz/SGIMC | cde56459d1d49576a5a6979a353ac27253233f3d | [
"MIT"
] | 1 | 2019-09-03T08:40:06.000Z | 2019-09-03T08:40:06.000Z | 408.258262 | 211,504 | 0.933885 | true | 8,307 | Qwen/Qwen-72B | 1. YES
2. YES | 0.851953 | 0.740174 | 0.630594 | __label__eng_Latn | 0.462704 | 0.303411 |
```python
import sys
sys.path.append('..')
import torch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sympy import simplify_logic
from lens.utils.base import validate_network
from lens.utils.relu_nn import get_reduced_model, prune_features
from lens import logic
import lens
torch.manual_... | 4da43cf1bf0c656ad402102961acad6c9a70c187 | 23,777 | ipynb | Jupyter Notebook | examples/example_pruning_02_dsprites.ipynb | pietrobarbiero/logic_explained_networks | 238f2a220ae8fc4f31ab0cf12649603aba0285d5 | [
"Apache-2.0"
] | 18 | 2021-05-24T07:47:57.000Z | 2022-01-05T14:48:39.000Z | examples/example_pruning_02_dsprites.ipynb | pietrobarbiero/logic_explained_networks | 238f2a220ae8fc4f31ab0cf12649603aba0285d5 | [
"Apache-2.0"
] | 1 | 2021-08-25T16:33:10.000Z | 2021-08-25T16:33:10.000Z | examples/example_pruning_02_dsprites.ipynb | pietrobarbiero/deep-logic | 238f2a220ae8fc4f31ab0cf12649603aba0285d5 | [
"Apache-2.0"
] | 2 | 2021-05-26T08:15:14.000Z | 2021-08-23T18:58:16.000Z | 32.437926 | 226 | 0.368549 | true | 5,700 | Qwen/Qwen-72B | 1. YES
2. YES | 0.721743 | 0.709019 | 0.51173 | __label__kor_Hang | 0.20741 | 0.027249 |
<a href="https://colab.research.google.com/github/SzymonSkrobiszewski/ON2022/blob/main/Untitled4.ipynb" target="_parent"></a>
```python
from sympy import*
def lagrange(X, Y, x):
y = 0; lenght = len(X)
for i in range(lenght):
result = 1
for j in range(lenght):
if j != i:
result *= (x - X[j])... | 96d4c1075f26b7ecbb2a4910a6f1ac00aee5a593 | 2,000 | ipynb | Jupyter Notebook | Untitled4.ipynb | SzymonSkrobiszewski/ON2022 | e71f77001e6cd0739a051423c3b7b36ccdf0dbb5 | [
"MIT"
] | null | null | null | Untitled4.ipynb | SzymonSkrobiszewski/ON2022 | e71f77001e6cd0739a051423c3b7b36ccdf0dbb5 | [
"MIT"
] | null | null | null | Untitled4.ipynb | SzymonSkrobiszewski/ON2022 | e71f77001e6cd0739a051423c3b7b36ccdf0dbb5 | [
"MIT"
] | null | null | null | 24.096386 | 232 | 0.4225 | true | 184 | Qwen/Qwen-72B | 1. YES
2. YES | 0.932453 | 0.76908 | 0.717131 | __label__eng_Latn | 0.158533 | 0.504468 |
# Combinando Modelos e dados: COVID-19
Ao se modelar uma epidemia real, a concordância do modelo e os dados observados é de extrema importância.
Neste Notebook vamos estudar o Modelo SEIAHR proposto para a COVID-19 por [Coelho et al](https://www.medrxiv.org/content/10.1101/2020.06.15.20132050v1). Neste modelo, temos ... | 59355bf8f553a4f4a659fc9d97c16d6751208ad7 | 175,304 | ipynb | Jupyter Notebook | Planilhas Sage/Suplemento 2 - o Modelo SEIAHR.ipynb | fccoelho/Modelagem-Matematica-IV | f0ff2824a564183a7c972988b32b487fa7fa1942 | [
"BSD-Source-Code"
] | 23 | 2019-04-15T16:51:02.000Z | 2021-08-25T01:22:03.000Z | Planilhas Sage/Suplemento 2 - o Modelo SEIAHR.ipynb | fccoelho/Modelagem-Matematica-IV | f0ff2824a564183a7c972988b32b487fa7fa1942 | [
"BSD-Source-Code"
] | 11 | 2021-08-04T12:25:24.000Z | 2021-11-26T13:57:28.000Z | Planilhas Sage/Suplemento 2 - o Modelo SEIAHR.ipynb | fccoelho/Modelagem-Matematica-IV | f0ff2824a564183a7c972988b32b487fa7fa1942 | [
"BSD-Source-Code"
] | 10 | 2020-08-03T12:24:13.000Z | 2021-12-08T12:51:02.000Z | 176.008032 | 48,768 | 0.885131 | true | 6,057 | Qwen/Qwen-72B | 1. YES
2. YES | 0.824462 | 0.712232 | 0.587208 | __label__kor_Hang | 0.143609 | 0.202612 |
Programming exercise 1: Manuel, Niclas, Veli <br>
This code approximates the solution to the two dimensional Poisson problem by discretizing:
\begin{align}
-\Delta u & = f \; in \; \Omega = (0,1)^2\\
u & = 0 \; on \; \partial \Omega
\end{align}
First, we write a function that gives back the sparse matrix of size $(n-1)... | c3fbdeface516d068887f69651094e3bcb320735 | 150,224 | ipynb | Jupyter Notebook | Programming_Exercise_Manuel_Niclas_Veli.ipynb | Veli-hub/Scientific_Computing | 942e0adf28913231b21396109c6c893b6dce2279 | [
"MIT"
] | null | null | null | Programming_Exercise_Manuel_Niclas_Veli.ipynb | Veli-hub/Scientific_Computing | 942e0adf28913231b21396109c6c893b6dce2279 | [
"MIT"
] | null | null | null | Programming_Exercise_Manuel_Niclas_Veli.ipynb | Veli-hub/Scientific_Computing | 942e0adf28913231b21396109c6c893b6dce2279 | [
"MIT"
] | null | null | null | 134.008921 | 74,224 | 0.771228 | true | 1,726 | Qwen/Qwen-72B | 1. YES
2. YES | 0.947381 | 0.853913 | 0.808981 | __label__eng_Latn | 0.806809 | 0.717866 |
<a href="https://colab.research.google.com/github/kalz2q/mycolabnotebooks/blob/master/chartmathc01matrix.ipynb" target="_parent"></a>
# メモ
手元にある
基礎からのチャート式数学C
の
第1章行列
を読む。
いくつかの数や文字を長方形状に並べ、両側を括弧で囲んだものを行列といい、そのおのおの数や文字を成分という。 横の並びを行 row といい、縦の並びを列 column という。
```latex
%%latex
\begin{pmatrix}
a... | df8a649a84f50589c923916760ba4381a11e62cc | 27,408 | ipynb | Jupyter Notebook | chartmathc01matrix.ipynb | kalz2q/-yjupyternotebooks | ba37ac7822543b830fe8602b3f611bb617943463 | [
"MIT"
] | 1 | 2021-09-16T03:45:19.000Z | 2021-09-16T03:45:19.000Z | chartmathc01matrix.ipynb | kalz2q/-yjupyternotebooks | ba37ac7822543b830fe8602b3f611bb617943463 | [
"MIT"
] | null | null | null | chartmathc01matrix.ipynb | kalz2q/-yjupyternotebooks | ba37ac7822543b830fe8602b3f611bb617943463 | [
"MIT"
] | null | null | null | 26.818004 | 462 | 0.387004 | true | 3,641 | Qwen/Qwen-72B | 1. YES
2. YES | 0.812867 | 0.766294 | 0.622895 | __label__yue_Hant | 0.109993 | 0.285524 |
# Lecture 6: Monty Hall, Simpson's Paradox
## The Monty Hall Problem
You know this problem.
* There are three doors.
* A car is behind one of the doors.
* The other two doors have goats behind them.
* You choose a door, but before you see what's behind your choice, Monty opens one of the other doors to reveal a goat... | 1101208cb1edba4a90f77869b0b671b471c81161 | 8,543 | ipynb | Jupyter Notebook | Lecture_06.ipynb | dirtScrapper/Stats-110-master | a123692d039193a048ff92f5a7389e97e479eb7e | [
"BSD-3-Clause"
] | null | null | null | Lecture_06.ipynb | dirtScrapper/Stats-110-master | a123692d039193a048ff92f5a7389e97e479eb7e | [
"BSD-3-Clause"
] | null | null | null | Lecture_06.ipynb | dirtScrapper/Stats-110-master | a123692d039193a048ff92f5a7389e97e479eb7e | [
"BSD-3-Clause"
] | null | null | null | 36.353191 | 248 | 0.52347 | true | 1,994 | Qwen/Qwen-72B | 1. YES
2. YES | 0.891811 | 0.936285 | 0.834989 | __label__eng_Latn | 0.991095 | 0.778293 |
```python
import numpy as np
from sympy import divisors, divisor_count, sieve
from tqdm import tqdm
import multiprocessing as mp
import pickle
```
```python
N = 1e8
try:
prime_set = pickle.load(open('data/prime_set_1e8.pkl', 'rb'))
except FileNotFoundError:
sieve._reset()
sieve.extend(N)
prime_set = ... | 7df5b072d1066fdc17e7685b71f31b1f34dc66b4 | 2,145 | ipynb | Jupyter Notebook | src/p357.ipynb | alexandru-dinu/project-euler | 10afd9e204203dd8d5c827b33659a5a2b3090532 | [
"MIT"
] | null | null | null | src/p357.ipynb | alexandru-dinu/project-euler | 10afd9e204203dd8d5c827b33659a5a2b3090532 | [
"MIT"
] | 3 | 2021-10-13T19:26:01.000Z | 2021-10-13T22:18:23.000Z | src/p357.ipynb | alexandru-dinu/project-euler | 10afd9e204203dd8d5c827b33659a5a2b3090532 | [
"MIT"
] | null | null | null | 21.237624 | 74 | 0.475058 | true | 260 | Qwen/Qwen-72B | 1. YES
2. YES | 0.896251 | 0.689306 | 0.617791 | __label__eng_Latn | 0.347874 | 0.273666 |
# **Propagação de erros em Python3: Jupyter Notebook - Data analytics**
## *Subprojeto "Protótipo de Magnetômetro Portátil com Internet das Coisas" (Computação Física) da UFES/Alegre*
### Eduardo Destefani Stefanato, IC FAPES
### Professor : Roberto Colistete Jr., em 05/02/2021.
__________________________________
#... | df6fc10f76b7b0158308f7a0d20152f3e829210e | 5,881 | ipynb | Jupyter Notebook | uncertz/source-code/uncertz_v0.1.ipynb | EduardoDestefani/python-samples | 91affdafe61bd1f5d55cb801a18969657e73177f | [
"MIT"
] | null | null | null | uncertz/source-code/uncertz_v0.1.ipynb | EduardoDestefani/python-samples | 91affdafe61bd1f5d55cb801a18969657e73177f | [
"MIT"
] | null | null | null | uncertz/source-code/uncertz_v0.1.ipynb | EduardoDestefani/python-samples | 91affdafe61bd1f5d55cb801a18969657e73177f | [
"MIT"
] | null | null | null | 26.490991 | 399 | 0.516579 | true | 977 | Qwen/Qwen-72B | 1. YES
2. YES | 0.877477 | 0.800692 | 0.702589 | __label__por_Latn | 0.987835 | 0.47068 |
# Learning Disentangled Representations using sequential images of a teapot
```python
from mpl_toolkits import mplot3d
import matplotlib.pyplot as plt
import random
import numpy as np
from PIL import Image
from tqdm import tqdm
import os
```
### Create dataset
Code to generate this dataset borrows from
https://medi... | 3c9e0c400840f467ccaeecdcd48fa576fa74483d | 94,939 | ipynb | Jupyter Notebook | fig4_teapot.ipynb | luis-armando-perez-rey/learning-group-structure | e238308de73a29506d9281e1b55cdd2de2795ebb | [
"MIT"
] | 12 | 2020-02-16T10:34:27.000Z | 2022-02-20T00:27:19.000Z | fig4_teapot.ipynb | luis-armando-perez-rey/learning-group-structure | e238308de73a29506d9281e1b55cdd2de2795ebb | [
"MIT"
] | 4 | 2021-06-08T22:32:50.000Z | 2022-03-12T00:49:42.000Z | fig4_teapot.ipynb | luis-armando-perez-rey/learning-group-structure | e238308de73a29506d9281e1b55cdd2de2795ebb | [
"MIT"
] | 3 | 2020-04-03T08:24:19.000Z | 2022-01-16T02:02:10.000Z | 76.873684 | 29,760 | 0.755211 | true | 9,386 | Qwen/Qwen-72B | 1. YES
2. YES | 0.803174 | 0.637031 | 0.511646 | __label__eng_Latn | 0.317221 | 0.027055 |
```python
%run base.py
```
```python
from sympy import init_printing
init_printing()
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
```
# 构建Waston函数
```python
m=31
n=5
xvec2 = symbols(f'x1:{n+1}')
xvec2 #向量符号
rlist = []
tlist = [Rational(i+1, 29) for i i... | d91791b99d02bce093f7fa16470caeb2a2cbd18f | 5,982 | ipynb | Jupyter Notebook | waston.ipynb | LingrenKong/Numerical-Optimization-Code | 598e2b5099e2ba57ea0aa7ff4a5f5547889828b2 | [
"MIT"
] | 2 | 2021-11-10T09:06:03.000Z | 2021-12-07T06:43:45.000Z | waston.ipynb | LingrenKong/Numerical-Optimization-Code | 598e2b5099e2ba57ea0aa7ff4a5f5547889828b2 | [
"MIT"
] | null | null | null | waston.ipynb | LingrenKong/Numerical-Optimization-Code | 598e2b5099e2ba57ea0aa7ff4a5f5547889828b2 | [
"MIT"
] | null | null | null | 20.209459 | 77 | 0.490137 | true | 1,055 | Qwen/Qwen-72B | 1. YES
2. YES | 0.833325 | 0.743168 | 0.6193 | __label__eng_Latn | 0.117456 | 0.277172 |
# Tarea 2
Daniela Paz Díaz Mora
201710003-6
```python
import numpy as np
from sympy import Matrix
from sympy.abc import x, y
from numpy import linalg
```
### Problema 1
# 2.4 Lutkepohl
#### Determine the autocovariances $\Gamma_y(0)$, $\Gamma_y(1)$, $\Gamma_y(2)$, $\Gamma_y(3)$ of the process (2.4.1). Compute an... | 2261a07f75bb9266fc5d834bace899f15627a0a0 | 136,342 | ipynb | Jupyter Notebook | Tareas/Tarea2.ipynb | pazDaniela/MAT287-Series-de-Tiempo-2020-2 | e7ac2bf9b474311556d8a345d567c54aa55788fd | [
"MIT"
] | 2 | 2020-10-30T00:47:43.000Z | 2020-12-05T14:11:58.000Z | Tareas/Tarea2.ipynb | pazDaniela/MAT287-Series-de-Tiempo-2020-2 | e7ac2bf9b474311556d8a345d567c54aa55788fd | [
"MIT"
] | null | null | null | Tareas/Tarea2.ipynb | pazDaniela/MAT287-Series-de-Tiempo-2020-2 | e7ac2bf9b474311556d8a345d567c54aa55788fd | [
"MIT"
] | null | null | null | 143.820675 | 109,288 | 0.876348 | true | 5,038 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.805632 | 0.714761 | __label__yue_Hant | 0.089188 | 0.49896 |
```python
from epipack import SymbolicEpiModel
from epipack.interactive import InteractiveIntegrator, Range, LogRange
import sympy
import numpy as np
%matplotlib widget
S, I, R, R0, tau, omega = sympy.symbols("S I R R_0 tau omega")
I0 = 0.01
model = SymbolicEpiModel([S,I,R])\
.set_processes([
... | 4593c100d1981b17253a6047054ed202d0182741 | 2,106 | ipynb | Jupyter Notebook | interactive.ipynb | benmaier/networks2021-hons-softwaredemo | f50112bb785123e4beed7077ef75283d72579a20 | [
"MIT"
] | 1 | 2021-07-02T18:22:08.000Z | 2021-07-02T18:22:08.000Z | interactive.ipynb | benmaier/networks2021-hons-softwaredemo | f50112bb785123e4beed7077ef75283d72579a20 | [
"MIT"
] | null | null | null | interactive.ipynb | benmaier/networks2021-hons-softwaredemo | f50112bb785123e4beed7077ef75283d72579a20 | [
"MIT"
] | null | null | null | 23.4 | 120 | 0.506648 | true | 266 | Qwen/Qwen-72B | 1. YES
2. YES | 0.92523 | 0.699254 | 0.646971 | __label__kor_Hang | 0.181389 | 0.341461 |
# Batch Normalization
One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train.
One idea along these lines is batch normalization which was proposed... | 2927f5541ad8aec21fb08e129ad5c1811d4f1a61 | 460,304 | ipynb | Jupyter Notebook | assignments/2019/assignment2/BatchNormalization.ipynb | comratvlad/cs231n.github.io | 63c72c3e8e88a6edfea7db7df604d715416ba15b | [
"MIT"
] | null | null | null | assignments/2019/assignment2/BatchNormalization.ipynb | comratvlad/cs231n.github.io | 63c72c3e8e88a6edfea7db7df604d715416ba15b | [
"MIT"
] | null | null | null | assignments/2019/assignment2/BatchNormalization.ipynb | comratvlad/cs231n.github.io | 63c72c3e8e88a6edfea7db7df604d715416ba15b | [
"MIT"
] | null | null | null | 402.715661 | 119,564 | 0.929466 | true | 9,035 | Qwen/Qwen-72B | 1. YES
2. YES | 0.731059 | 0.822189 | 0.601068 | __label__eng_Latn | 0.921406 | 0.234814 |
# A/B Testing from Scratch: Bayesian Approach
We reuse the simple problem of comparing two online ads campaigns (or teatments, user interfaces or slot machines). We details how Bayesian A/B test is conducted and highlights the differences between it and the frequentist approaches. Readers are encouraged to tinker with... | d932b249e6f5a2817422fad209315de964ad9a31 | 224,258 | ipynb | Jupyter Notebook | notebooks/bayesian.ipynb | TeamTamoad/abtestoo | 90e903ddbe945034b8226aad05a74fb46efb5326 | [
"Apache-2.0"
] | 12 | 2019-04-23T03:12:39.000Z | 2020-09-16T06:00:44.000Z | notebooks/bayesian.ipynb | TeamTamoad/abtestoo | 90e903ddbe945034b8226aad05a74fb46efb5326 | [
"Apache-2.0"
] | null | null | null | notebooks/bayesian.ipynb | TeamTamoad/abtestoo | 90e903ddbe945034b8226aad05a74fb46efb5326 | [
"Apache-2.0"
] | 27 | 2020-10-08T19:22:58.000Z | 2021-11-29T11:09:45.000Z | 236.559072 | 54,636 | 0.891915 | true | 6,765 | Qwen/Qwen-72B | 1. YES
2. YES | 0.861538 | 0.766294 | 0.660191 | __label__eng_Latn | 0.948949 | 0.372176 |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content-dl/blob/main/tutorials/W1D2_LinearDeepLearning/student/W1D2_Tutorial3.ipynb" target="_parent"></a>
# DL Neuromatch Academy: Week 1, Day 2, Tutorial 3
# Deep Linear Neural Networks
__Content creators:__ Andrew Saxe, Saeed Salehi, Vladi... | ebe3adf65c48f44dc3e946552c67b8618a74b333 | 70,972 | ipynb | Jupyter Notebook | tutorials/W1D2_LinearDeepLearning/student/W1D2_Tutorial3.ipynb | MeRajat/course-content-dl | cb659b29a4b0acd4bd0fb2705dd28b304c0a71cd | [
"CC-BY-4.0",
"BSD-3-Clause"
] | 1 | 2021-07-04T21:41:03.000Z | 2021-07-04T21:41:03.000Z | tutorials/W1D2_LinearDeepLearning/student/W1D2_Tutorial3.ipynb | MeRajat/course-content-dl | cb659b29a4b0acd4bd0fb2705dd28b304c0a71cd | [
"CC-BY-4.0",
"BSD-3-Clause"
] | null | null | null | tutorials/W1D2_LinearDeepLearning/student/W1D2_Tutorial3.ipynb | MeRajat/course-content-dl | cb659b29a4b0acd4bd0fb2705dd28b304c0a71cd | [
"CC-BY-4.0",
"BSD-3-Clause"
] | null | null | null | 39.82716 | 805 | 0.577157 | true | 12,597 | Qwen/Qwen-72B | 1. YES
2. YES | 0.672332 | 0.787931 | 0.529751 | __label__eng_Latn | 0.919626 | 0.069119 |
## Introduction
This tutorial illustrates the spectra computation for standard and pure B modes. We will only use
the `HEALPIX` pixellisation to pass through the different steps of generation.
The `HEALPIX` survey mask is a disk centered on longitude 30° and latitude 50° with a radius of 25
radians. The `nside` val... | d1f3e76e604304ed2e469b57d14edf0178a2fece | 298,164 | ipynb | Jupyter Notebook | notebooks/tutorial_purebb.ipynb | xgarrido/pspy | 8c1c13828ca982a1747ddeed2ee9c35b09fd9f0b | [
"BSD-3-Clause"
] | 6 | 2020-01-26T22:00:31.000Z | 2021-05-04T08:13:44.000Z | notebooks/tutorial_purebb.ipynb | simonsobs/pspy | b1faf15eb7c9f4c2bee80fe5cfafaab1d4bc6470 | [
"BSD-3-Clause"
] | 5 | 2021-02-12T13:04:08.000Z | 2022-01-24T18:57:34.000Z | notebooks/tutorial_purebb.ipynb | xgarrido/pspy | 8c1c13828ca982a1747ddeed2ee9c35b09fd9f0b | [
"BSD-3-Clause"
] | 1 | 2021-11-02T11:01:58.000Z | 2021-11-02T11:01:58.000Z | 440.419498 | 239,124 | 0.941009 | true | 2,888 | Qwen/Qwen-72B | 1. YES
2. YES | 0.72487 | 0.70253 | 0.509243 | __label__eng_Latn | 0.843819 | 0.021472 |
$\newcommand{\ve}[1]{\mathbf{#1}}$
$\newcommand{\ovo}{\overline{O}}$
$\def\Brack#1{\left[ #1 \right]}$
$\def\bra#1{\mathinner{\langle{#1}|}}$
$\def\ket#1{\mathinner{|{#1}\rangle}}$
$\def\braket#1{\mathinner{\langle{#1}\rangle}}$
$\def\Bra#1{\left<#1\right|}$
$\def\Ket#1{\left|#1\right>}$
$\def\KetC#1{\left|\left\{ #1 \... | 9dfc0160f4afb1ca0b17a11725e968ca07490efe | 108,405 | ipynb | Jupyter Notebook | lectures/Notebooks/Dia_4_QNDs.ipynb | ChekHub/ENS3 | 245685f7de4c18a6323fcccbcefd869b2b102513 | [
"MIT"
] | 2 | 2019-05-12T00:05:52.000Z | 2019-05-12T00:13:59.000Z | lectures/Notebooks/Dia_4_QNDs.ipynb | ChekHub/ENS3 | 245685f7de4c18a6323fcccbcefd869b2b102513 | [
"MIT"
] | null | null | null | lectures/Notebooks/Dia_4_QNDs.ipynb | ChekHub/ENS3 | 245685f7de4c18a6323fcccbcefd869b2b102513 | [
"MIT"
] | null | null | null | 171.255924 | 84,764 | 0.896739 | true | 3,175 | Qwen/Qwen-72B | 1. YES
2. YES | 0.805632 | 0.76908 | 0.619596 | __label__spa_Latn | 0.861091 | 0.277859 |
<b>Traçar um esboço do gráfico e obter uma equação da parábola que satisfaça as condições dadas.</b>
<b>21. Vértice: $V(0,-2)$; diretriz: $2x-3=0$</b>
<b>Arrumando a equação da diretriz</b><br><br>
$d: x = \frac{3}{2}$<br><br><br>
<b>Fazendo um esboço é possivel perceber que a parábola é paralela ao eixo $x$, logo su... | 716ea3bb386ea627f429a81a7583dbd1ba045414 | 14,204 | ipynb | Jupyter Notebook | Problemas Propostos. Pag. 172 - 175/21.ipynb | mateuschaves/GEOMETRIA-ANALITICA | bc47ece7ebab154e2894226c6d939b7e7f332878 | [
"MIT"
] | 1 | 2020-02-03T16:40:45.000Z | 2020-02-03T16:40:45.000Z | Problemas Propostos. Pag. 172 - 175/21.ipynb | mateuschaves/GEOMETRIA-ANALITICA | bc47ece7ebab154e2894226c6d939b7e7f332878 | [
"MIT"
] | null | null | null | Problemas Propostos. Pag. 172 - 175/21.ipynb | mateuschaves/GEOMETRIA-ANALITICA | bc47ece7ebab154e2894226c6d939b7e7f332878 | [
"MIT"
] | null | null | null | 161.409091 | 11,812 | 0.881794 | true | 580 | Qwen/Qwen-72B | 1. YES
2. YES | 0.917303 | 0.882428 | 0.809453 | __label__por_Latn | 0.906719 | 0.718964 |
# Семинар 5
```python
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
%matplotlib inline
```
```python
plt.rcParams['figure.figsize'] = (15, 7)
```
## Линейная классификация
### Постановка задачи классификации
Пусть задана обучающая выб... | dbbadc67d5df561a37e1a89133ab0a5a2607779b | 300,304 | ipynb | Jupyter Notebook | 04 ML/05 svm/sem05_logreg_svm.ipynb | ksetdekov/HSE_DS | 619d5b84f9d9e97b58ca1f12c5914ec65456c2c8 | [
"MIT"
] | 1 | 2020-09-26T18:48:11.000Z | 2020-09-26T18:48:11.000Z | 04 ML/05 svm/sem05_logreg_svm.ipynb | ksetdekov/HSE_DS | 619d5b84f9d9e97b58ca1f12c5914ec65456c2c8 | [
"MIT"
] | null | null | null | 04 ML/05 svm/sem05_logreg_svm.ipynb | ksetdekov/HSE_DS | 619d5b84f9d9e97b58ca1f12c5914ec65456c2c8 | [
"MIT"
] | null | null | null | 151.975709 | 102,896 | 0.858846 | true | 13,032 | Qwen/Qwen-72B | 1. YES
2. YES | 0.810479 | 0.771843 | 0.625563 | __label__rus_Cyrl | 0.078429 | 0.291723 |
### Exercises of Optimization
```python
# import Python libraries
import numpy as np
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import sympy as sym
from sympy.plotting import plot
import pandas as pd
from IPython.display import display
from IPython.core.display import Math
```
**1.) Find th... | d268d67c94bf1b442d4c31353fb536cacb75a458 | 4,415 | ipynb | Jupyter Notebook | courses/modsim2018/tasks/Task_ForLecture19.ipynb | raissabthibes/bmc | 840800fb94ea3bf188847d0771ca7197dfec68e3 | [
"MIT"
] | null | null | null | courses/modsim2018/tasks/Task_ForLecture19.ipynb | raissabthibes/bmc | 840800fb94ea3bf188847d0771ca7197dfec68e3 | [
"MIT"
] | null | null | null | courses/modsim2018/tasks/Task_ForLecture19.ipynb | raissabthibes/bmc | 840800fb94ea3bf188847d0771ca7197dfec68e3 | [
"MIT"
] | null | null | null | 25.818713 | 132 | 0.516648 | true | 784 | Qwen/Qwen-72B | 1. YES
2. YES | 0.969785 | 0.888759 | 0.861905 | __label__eng_Latn | 0.915101 | 0.840828 |
# Redes Neuronales
Inicialmente, las redes neuronales fueron inspiradas en el cerebro humano. Sin embargo, después de cierto tiempo se ha dejado de tratar de emular cómo funciona el cerebro y se ha tomado un enfoque en encontrar las configuraciones más apropiadas de las redes neuronales para desarrollar diferentes tar... | 7fba0ed6bf74292628f87845be7ab372cebb6127 | 802,034 | ipynb | Jupyter Notebook | 12NNs/1_IntroNNs.ipynb | sergiogaitan/Study_Guides | 083acd23f5faa6c6bc404d4d53df562096478e7c | [
"MIT"
] | 5 | 2020-09-12T17:16:12.000Z | 2021-02-03T01:37:02.000Z | 12NNs/1_IntroNNs.ipynb | sergiogaitan/Study_Guides | 083acd23f5faa6c6bc404d4d53df562096478e7c | [
"MIT"
] | null | null | null | 12NNs/1_IntroNNs.ipynb | sergiogaitan/Study_Guides | 083acd23f5faa6c6bc404d4d53df562096478e7c | [
"MIT"
] | 4 | 2020-05-22T12:57:49.000Z | 2021-02-03T01:37:07.000Z | 652.060163 | 195,288 | 0.939263 | true | 12,657 | Qwen/Qwen-72B | 1. YES
2. YES | 0.912436 | 0.839734 | 0.766204 | __label__spa_Latn | 0.47447 | 0.61848 |
<p align="center">
</p>
## Data Analytics
### Distribution Transformations in Python
#### Michael Pyrcz, Associate Professor, The University of Texas at Austin
##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar]... | 8750353f73349102e74439126629e57f93491182 | 229,895 | ipynb | Jupyter Notebook | PythonDataBasics_DistributionsTransformations.ipynb | caf3676/PythonNumericalDemos | 206a3d876f79e137af88b85ba98aff171e8d8e06 | [
"MIT"
] | null | null | null | PythonDataBasics_DistributionsTransformations.ipynb | caf3676/PythonNumericalDemos | 206a3d876f79e137af88b85ba98aff171e8d8e06 | [
"MIT"
] | null | null | null | PythonDataBasics_DistributionsTransformations.ipynb | caf3676/PythonNumericalDemos | 206a3d876f79e137af88b85ba98aff171e8d8e06 | [
"MIT"
] | 1 | 2022-03-14T03:28:32.000Z | 2022-03-14T03:28:32.000Z | 182.311657 | 50,724 | 0.863394 | true | 10,587 | Qwen/Qwen-72B | 1. YES
2. YES | 0.72487 | 0.70253 | 0.509243 | __label__eng_Latn | 0.583197 | 0.021472 |
# Programación lineal
### Anuncios varios
+ Encuesta
+ Clase 18 de Febrero (Martes 19 de feb 9-11 salón por definir)
+ Exámen 1 (28 de Febrero)
+ Proyecto (7 de Marzo)
> La programación lineal es el campo de la optimización matemática dedicado a maximizar o minimizar (optimizar) funciones lineales, denominada func... | 3915df958c7879d44bfddc9e8d1f26533bf7a075 | 96,426 | ipynb | Jupyter Notebook | Modulo1/Clase5_ProgramacionLineal.ipynb | ArellanoMCarlos/SimMat2019-1 | c84b92a581916572352615806d31961a468da3d9 | [
"MIT"
] | null | null | null | Modulo1/Clase5_ProgramacionLineal.ipynb | ArellanoMCarlos/SimMat2019-1 | c84b92a581916572352615806d31961a468da3d9 | [
"MIT"
] | null | null | null | Modulo1/Clase5_ProgramacionLineal.ipynb | ArellanoMCarlos/SimMat2019-1 | c84b92a581916572352615806d31961a468da3d9 | [
"MIT"
] | null | null | null | 83.630529 | 27,729 | 0.776761 | true | 9,280 | Qwen/Qwen-72B | 1. YES
2. YES | 0.76908 | 0.851953 | 0.65522 | __label__spa_Latn | 0.859449 | 0.360627 |
| | Pierre Proulx, ing, professeur|
|:---|:---|
|Département de génie chimique et de génie biotechnologique |** GCH200-Phénomènes d'échanges I **|
### Section 2.2, écoulement d'un film de fluide Newtonien sur un plan incliné.
> Dans cette section il est important de revoir les concepts de flux de quantité de mouvemen... | b56fe9a98e0a1994bade82c8025563b8440d2fbd | 75,446 | ipynb | Jupyter Notebook | Chap-2-Section-2-2.ipynb | Spationaute/GCH200 | 55144f5b2a59a7240d36c985997387f5036149f7 | [
"MIT"
] | null | null | null | Chap-2-Section-2-2.ipynb | Spationaute/GCH200 | 55144f5b2a59a7240d36c985997387f5036149f7 | [
"MIT"
] | null | null | null | Chap-2-Section-2-2.ipynb | Spationaute/GCH200 | 55144f5b2a59a7240d36c985997387f5036149f7 | [
"MIT"
] | null | null | null | 97.475452 | 21,380 | 0.825199 | true | 2,229 | Qwen/Qwen-72B | 1. YES
2. YES | 0.810479 | 0.798187 | 0.646914 | __label__fra_Latn | 0.888736 | 0.341328 |
[Principal component analysis (PCA)](https://en.wikipedia.org/wiki/Principal_component_analysis) is one of the most used techniques for exploratory data analysis and preprocessing.
There are different formulations of PCA. A fundamental concept that occurs in several formulations are covariance matrices. In this lab, w... | 4d61edcddbf5b5408b5d3dcf9aaba6b7aed56187 | 320,652 | ipynb | Jupyter Notebook | w2/02_Lab_2_Principal_component_analysis.ipynb | c-abbott/dme-2021 | 09bc0499c6e68864292e574485e11729449e7aa0 | [
"MIT"
] | null | null | null | w2/02_Lab_2_Principal_component_analysis.ipynb | c-abbott/dme-2021 | 09bc0499c6e68864292e574485e11729449e7aa0 | [
"MIT"
] | null | null | null | w2/02_Lab_2_Principal_component_analysis.ipynb | c-abbott/dme-2021 | 09bc0499c6e68864292e574485e11729449e7aa0 | [
"MIT"
] | null | null | null | 278.34375 | 147,700 | 0.915394 | true | 7,707 | Qwen/Qwen-72B | 1. YES
2. YES | 0.870597 | 0.763484 | 0.664687 | __label__eng_Latn | 0.979897 | 0.382621 |
## Confidence Intervals and Hypothesis Testing in Python for Engineers and Geoscientists
### Michael Pyrcz, Associate Professor, University of Texas at Austin
#### Contacts: [Twitter/@GeostatsGuy](https://twitter.com/geostatsguy) | [GitHub/GeostatsGuy](https://github.com/GeostatsGuy) | [www.michaelpyrcz.com](http://... | b1ecf2330375dd1194a5d7a16ed6e1b521863e5f | 18,328 | ipynb | Jupyter Notebook | PythonDataBasics_Hypothesis.ipynb | caf3676/PythonNumericalDemos | 206a3d876f79e137af88b85ba98aff171e8d8e06 | [
"MIT"
] | 403 | 2017-10-15T02:07:38.000Z | 2022-03-30T15:27:14.000Z | PythonDataBasics_Hypothesis.ipynb | caf3676/PythonNumericalDemos | 206a3d876f79e137af88b85ba98aff171e8d8e06 | [
"MIT"
] | 4 | 2019-08-21T10:35:09.000Z | 2021-02-04T04:57:13.000Z | PythonDataBasics_Hypothesis.ipynb | caf3676/PythonNumericalDemos | 206a3d876f79e137af88b85ba98aff171e8d8e06 | [
"MIT"
] | 276 | 2018-06-27T11:20:30.000Z | 2022-03-25T16:04:24.000Z | 35.937255 | 667 | 0.553525 | true | 3,237 | Qwen/Qwen-72B | 1. YES
2. YES | 0.893309 | 0.833325 | 0.744417 | __label__eng_Latn | 0.988788 | 0.567861 |
<center>
<h1> INF285 - Computación Científica </h1>
<h2> Gradient Descent and Nonlinear Least-Square </h2>
<h2> <a href="#acknowledgements"> [S]cientific [C]omputing [T]eam </a> </h2>
<h2> Version: 1.02</h2>
</center>
<div id='toc' />
## Table of Contents
* [Introduction](#intro)
* [Gradient Desc... | 30c3ecd488809a2bae7c32f908352c0a2644ed6c | 55,581 | ipynb | Jupyter Notebook | SC1v2/Bonus - 07-08 - Gradient Descent and Nonlinear Least-Square.ipynb | xavierutox/Scientific-Computing | bb5dd02362a7c2cdcaf2d24c348ab16c8533482c | [
"BSD-3-Clause"
] | 37 | 2017-06-05T21:01:15.000Z | 2022-03-17T12:51:55.000Z | SC1v2/Bonus - 07-08 - Gradient Descent and Nonlinear Least-Square.ipynb | xavierutox/Scientific-Computing | bb5dd02362a7c2cdcaf2d24c348ab16c8533482c | [
"BSD-3-Clause"
] | null | null | null | SC1v2/Bonus - 07-08 - Gradient Descent and Nonlinear Least-Square.ipynb | xavierutox/Scientific-Computing | bb5dd02362a7c2cdcaf2d24c348ab16c8533482c | [
"BSD-3-Clause"
] | 63 | 2017-10-02T21:21:30.000Z | 2022-03-23T02:23:22.000Z | 80.90393 | 26,888 | 0.753765 | true | 6,450 | Qwen/Qwen-72B | 1. YES
2. YES | 0.805632 | 0.727975 | 0.58648 | __label__eng_Latn | 0.905975 | 0.200921 |
Blankenbach Benchmark Case 1
======
Steady isoviscous thermal convection
----
Two-dimensional, incompressible, bottom heated, steady isoviscous thermal convection in a 1 x 1 box, see case 1 of Blankenbach *et al.* 1989 for details.
**This example introduces:**
1. Loading/Saving variables to disk.
2. Defining analy... | 868c778c68586755e0ae35337d6803b0ae810532 | 32,337 | ipynb | Jupyter Notebook | Notebooks/Underworld/03_BlankenbachBenchmark.ipynb | underworld-geodynamics-cloud/underworld-cloud-droplet | 5f786ae88cf42ecac980ad8fdc1c69bb389f948e | [
"MIT"
] | null | null | null | Notebooks/Underworld/03_BlankenbachBenchmark.ipynb | underworld-geodynamics-cloud/underworld-cloud-droplet | 5f786ae88cf42ecac980ad8fdc1c69bb389f948e | [
"MIT"
] | null | null | null | Notebooks/Underworld/03_BlankenbachBenchmark.ipynb | underworld-geodynamics-cloud/underworld-cloud-droplet | 5f786ae88cf42ecac980ad8fdc1c69bb389f948e | [
"MIT"
] | null | null | null | 32.272455 | 428 | 0.540372 | true | 5,713 | Qwen/Qwen-72B | 1. YES
2. YES | 0.763484 | 0.715424 | 0.546215 | __label__eng_Latn | 0.841927 | 0.107369 |
# Time Evolution: Split Operator Method
### Category: Prerequisites
### Prerequisites: Quantum Mechanics
When cleaning my apartment, sometimes I just grab the nearest dirty thing to me and try to do something to it. But that is not the most efficient way to get things done. If I'm planning, I'll first dedicate my ... | 7868672a59b98c652320e359b47d3d681feba9ad | 55,970 | ipynb | Jupyter Notebook | Prerequisites/Time-Evolution.ipynb | IanHawke/M4 | 2d841d4eb38f3d09891ed3c84e49858d30f2d4d4 | [
"MIT"
] | null | null | null | Prerequisites/Time-Evolution.ipynb | IanHawke/M4 | 2d841d4eb38f3d09891ed3c84e49858d30f2d4d4 | [
"MIT"
] | null | null | null | Prerequisites/Time-Evolution.ipynb | IanHawke/M4 | 2d841d4eb38f3d09891ed3c84e49858d30f2d4d4 | [
"MIT"
] | null | null | null | 96.666667 | 19,138 | 0.704217 | true | 3,937 | Qwen/Qwen-72B | 1. YES
2. YES | 0.863392 | 0.843895 | 0.728612 | __label__eng_Latn | 0.969918 | 0.531141 |
```python
from IPython.display import HTML, display
```
# Simulating Planetary Orbits with a Symplectic Integrator
The name of this library in Fluxions in homage to Isaac Newton, whose early name for differential calculus was "the method of fluxions." (For an entertaining work of fiction that places the invention o... | 37a7cc634682b9e51aefb654ccb063d1452c46a0 | 878,145 | ipynb | Jupyter Notebook | presentation/presentation_solar_system.ipynb | CS207-Final-Project-Group-10/cs207-FinalProject | 842e9c2d3ca1490cef18c086dfde81856d8d3a82 | [
"MIT"
] | 1 | 2021-03-21T04:50:31.000Z | 2021-03-21T04:50:31.000Z | presentation/presentation_solar_system.ipynb | CS207-Final-Project-Group-10/cs207-FinalProject | 842e9c2d3ca1490cef18c086dfde81856d8d3a82 | [
"MIT"
] | 6 | 2018-11-04T20:49:41.000Z | 2021-06-01T23:09:43.000Z | presentation/presentation_solar_system.ipynb | CS207-Final-Project-Group-10/cs207-FinalProject | 842e9c2d3ca1490cef18c086dfde81856d8d3a82 | [
"MIT"
] | 1 | 2020-10-22T13:59:47.000Z | 2020-10-22T13:59:47.000Z | 2,286.835938 | 857,140 | 0.96301 | true | 3,940 | Qwen/Qwen-72B | 1. YES
2. YES | 0.857768 | 0.867036 | 0.743716 | __label__eng_Latn | 0.997106 | 0.566233 |
This notebook is part of https://github.com/AudioSceneDescriptionFormat/splines, see also http://splines.readthedocs.io/.
# Derivation of Non-Uniform Catmull--Rom Splines
Recursive algorithm developed by
<cite data-cite="barry1988recursive">Barry and Goldman (1988)</cite>,
according to
<cite data-cite="yuksel2011para... | c9ca7563c421f0e3d40ffc577b12b7d0150afb4f | 7,439 | ipynb | Jupyter Notebook | doc/catmull-rom-non-uniform.ipynb | mgeier/splines | f54b09479d98bf13f00a183fd9d664b5783e3864 | [
"MIT"
] | null | null | null | doc/catmull-rom-non-uniform.ipynb | mgeier/splines | f54b09479d98bf13f00a183fd9d664b5783e3864 | [
"MIT"
] | null | null | null | doc/catmull-rom-non-uniform.ipynb | mgeier/splines | f54b09479d98bf13f00a183fd9d664b5783e3864 | [
"MIT"
] | null | null | null | 21.193732 | 144 | 0.488641 | true | 1,245 | Qwen/Qwen-72B | 1. YES
2. YES | 0.919643 | 0.90053 | 0.828165 | __label__eng_Latn | 0.323131 | 0.762439 |
# Lecture 4: Conditional Probability
## Stat 110, Prof. Joe Blitzstein, Harvard University
----
## Definitions
We continue with some basic definitions of _independence_ and _disjointness_:
#### Definition: independence & disjointness
> Events A and B are __independent__ if $P(A \cap B) = P(A)P(B)$. Knowing ... | e9653b76771fa270fee8316c296372201afd9f55 | 7,291 | ipynb | Jupyter Notebook | Lecture_04.ipynb | abhra-nilIITKgp/stats-110 | 258461cdfbdcf99de5b96bcf5b4af0dd98d48f85 | [
"BSD-3-Clause"
] | 113 | 2016-04-29T07:27:33.000Z | 2022-02-27T18:32:47.000Z | Lecture_04.ipynb | snoop2head/stats-110 | 88d0cc56ede406a584f6ba46368e548010f2b14a | [
"BSD-3-Clause"
] | null | null | null | Lecture_04.ipynb | snoop2head/stats-110 | 88d0cc56ede406a584f6ba46368e548010f2b14a | [
"BSD-3-Clause"
] | 65 | 2016-12-24T02:02:25.000Z | 2022-02-13T13:20:02.000Z | 34.230047 | 275 | 0.522562 | true | 1,631 | Qwen/Qwen-72B | 1. YES
2. YES | 0.828939 | 0.92079 | 0.763278 | __label__eng_Latn | 0.939413 | 0.611683 |
# Chapter 3.2 Calculus - Review
Here, we provide some examples of calculus.
More examples: https://scipy-lectures.org/packages/sympy.html
Copyright:
## 1 Calculate limits using Sympy
```python
# import library
import sympy as sym
# pythonic math expressions: add spaces, use single quotes and lowercases
# decla... | 517e24e89034e6af97ca9cae0c8caf609a1308f8 | 31,643 | ipynb | Jupyter Notebook | 3.2 Calculus.ipynb | NilaBlueshirt/MAT494TeachingMaterial | 87f89d627345eef254ebe3f6f658ab181f791984 | [
"MIT"
] | null | null | null | 3.2 Calculus.ipynb | NilaBlueshirt/MAT494TeachingMaterial | 87f89d627345eef254ebe3f6f658ab181f791984 | [
"MIT"
] | null | null | null | 3.2 Calculus.ipynb | NilaBlueshirt/MAT494TeachingMaterial | 87f89d627345eef254ebe3f6f658ab181f791984 | [
"MIT"
] | 1 | 2022-01-11T23:20:15.000Z | 2022-01-11T23:20:15.000Z | 31,643 | 31,643 | 0.818222 | true | 2,507 | Qwen/Qwen-72B | 1. YES
2. YES | 0.939025 | 0.66888 | 0.628095 | __label__yue_Hant | 0.145377 | 0.297606 |
# Frequentist Inference Case Study - Part B
## Learning objectives
Welcome to Part B of the Frequentist inference case study! The purpose of this case study is to help you apply the concepts associated with Frequentist inference in Python. In particular, you'll practice writing Python code to apply the following stat... | e39d49f7feaa01f268ad9f135650b1b62f7775b2 | 38,900 | ipynb | Jupyter Notebook | frequentist-case-study/frequentist-case-study-part-B.ipynb | reppertj/Data-Science-Examples | ee2690f07a9f606ecdb47cf1f3538641ade24312 | [
"MIT"
] | null | null | null | frequentist-case-study/frequentist-case-study-part-B.ipynb | reppertj/Data-Science-Examples | ee2690f07a9f606ecdb47cf1f3538641ade24312 | [
"MIT"
] | null | null | null | frequentist-case-study/frequentist-case-study-part-B.ipynb | reppertj/Data-Science-Examples | ee2690f07a9f606ecdb47cf1f3538641ade24312 | [
"MIT"
] | null | null | null | 74.095238 | 20,700 | 0.78144 | true | 2,987 | Qwen/Qwen-72B | 1. YES
2. YES | 0.83762 | 0.782662 | 0.655574 | __label__eng_Latn | 0.996646 | 0.361448 |
# Notebook 03: Inverse design parameterization
This notebook will introduce a few basic parameterization concepts for inverse design. The same mode converter device concept as in the previous notebook will be used here as an example.
*Parameterization* refers to how we are representing our device. In the previous not... | e41605cafc168bfa6a48ebfeacfad8a8ee0d112d | 378,110 | ipynb | Jupyter Notebook | 03_Invdes_parameterization.ipynb | fancompute/workshop-invdesign | 200eaa0abc3f691137e228e98ebb62446015ec38 | [
"MIT"
] | 57 | 2019-11-22T18:22:21.000Z | 2022-03-15T15:38:08.000Z | 03_Invdes_parameterization.ipynb | Ydeh22/workshop-invdesign | 200eaa0abc3f691137e228e98ebb62446015ec38 | [
"MIT"
] | 4 | 2019-12-14T16:57:42.000Z | 2021-04-01T05:41:30.000Z | 03_Invdes_parameterization.ipynb | Ydeh22/workshop-invdesign | 200eaa0abc3f691137e228e98ebb62446015ec38 | [
"MIT"
] | 20 | 2019-11-23T19:37:37.000Z | 2022-03-22T22:30:20.000Z | 421.057906 | 107,924 | 0.930462 | true | 5,093 | Qwen/Qwen-72B | 1. YES
2. YES | 0.924142 | 0.826712 | 0.763999 | __label__eng_Latn | 0.87413 | 0.613358 |
# Multi-trait LMMs
### Set up the environment
```python
%matplotlib inline
from warnings import simplefilter
simplefilter(action='ignore', category=FutureWarning)
import sys
import scipy as sp
import numpy as np
import scipy.stats as st
import pylab as pl
import pandas as pd
import h5py
sp.random.seed(0)
import l... | dab88598499ec3ba8527880789d274207fdd214d | 521,538 | ipynb | Jupyter Notebook | limix1/Lecture-12-Multi-Trait-Linear-Mixed-Model.ipynb | mahort/gwas-lecture | 59613e19a49d4cb1b4b446b077c2b30949f27347 | [
"CC-BY-3.0"
] | 17 | 2018-11-26T10:09:26.000Z | 2022-01-05T14:08:06.000Z | limix1/Lecture-12-Multi-Trait-Linear-Mixed-Model.ipynb | mahort/gwas-lecture | 59613e19a49d4cb1b4b446b077c2b30949f27347 | [
"CC-BY-3.0"
] | 1 | 2020-11-20T17:26:13.000Z | 2020-11-20T18:02:46.000Z | limix1/Lecture-12-Multi-Trait-Linear-Mixed-Model.ipynb | mahort/gwas-lecture | 59613e19a49d4cb1b4b446b077c2b30949f27347 | [
"CC-BY-3.0"
] | 14 | 2018-11-30T17:42:19.000Z | 2021-10-09T09:40:29.000Z | 353.824966 | 112,868 | 0.925125 | true | 6,450 | Qwen/Qwen-72B | 1. YES
2. YES | 0.810479 | 0.718594 | 0.582406 | __label__eng_Latn | 0.334571 | 0.191453 |
<a href="https://hub.callysto.ca/jupyter/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fcallysto%2Fcahiers-de-programmes&branch=master&subPath=Tutoriels/LaTeX.ipynb&depth=1" target="_parent"></a>
# Composition mathématique avec LaTeX
Cela ne servira que de brève introduction à la composition mathématiqu... | a89b50dbf0b49ca72c8815a7e6d77325d6324a40 | 10,921 | ipynb | Jupyter Notebook | Tutoriels/LaTeX.ipynb | callysto/cahiers-de-programmes | 456045bfe7d28395ff31a4b0b89fe86260765605 | [
"CC-BY-3.0"
] | null | null | null | Tutoriels/LaTeX.ipynb | callysto/cahiers-de-programmes | 456045bfe7d28395ff31a4b0b89fe86260765605 | [
"CC-BY-3.0"
] | null | null | null | Tutoriels/LaTeX.ipynb | callysto/cahiers-de-programmes | 456045bfe7d28395ff31a4b0b89fe86260765605 | [
"CC-BY-3.0"
] | null | null | null | 31.025568 | 497 | 0.595641 | true | 1,892 | Qwen/Qwen-72B | 1. YES
2. YES | 0.705785 | 0.901921 | 0.636562 | __label__fra_Latn | 0.993005 | 0.317278 |
```{warning}
This book is a work in progress and should be considered currently to be in a
**pre**draft state. Work is actively taking place in preparation for October
2020.
If you happen to find this and notice any typos and/or have any suggestions
please open an issue on the github repo: <https://github.com/drvincek... | decf84ac289ab9eb0f5ef7a40bb7625b46a2a775 | 7,495 | ipynb | Jupyter Notebook | book/.intro.md.bcp.ipynb | daffidwilde/pfm | dcf38faccee3c212c8394c36f4c093a2916d283e | [
"MIT"
] | 8 | 2020-09-24T21:02:41.000Z | 2020-10-14T08:37:21.000Z | book/.intro.md.bcp.ipynb | daffidwilde/pfm | dcf38faccee3c212c8394c36f4c093a2916d283e | [
"MIT"
] | 87 | 2020-09-21T15:54:23.000Z | 2021-12-19T23:26:15.000Z | book/.intro.md.bcp.ipynb | daffidwilde/pfm | dcf38faccee3c212c8394c36f4c093a2916d283e | [
"MIT"
] | 3 | 2020-10-02T09:21:27.000Z | 2021-07-08T14:46:27.000Z | 29.163424 | 91 | 0.563442 | true | 1,154 | Qwen/Qwen-72B | 1. YES
2. YES | 0.689306 | 0.793106 | 0.546692 | __label__eng_Latn | 0.999225 | 0.108479 |
Code for HW 2
```python
import numpy as np
```
Q4
```python
v0 = np.array([0.5, 0.5, 0.5, 0.5]).T
v1 = np.array([0.5, 0.5, -0.5, -0.5]).T
v2 = np.array([0.5, -0.5, 0.5, -0.5]).T
```
```python
A = 0.5*np.matrix([[1, 1, 1, 1],[1, 1, -1 ,-1],[1, -1, 1, -1]])
```
```python
y = np.matrix([-0.5, 0.5, 0.5, 1.5]).T... | f999c0314a91230a9b6432dead96a5f13dbf8751 | 6,753 | ipynb | Jupyter Notebook | HaarBasis/HW 2.ipynb | AkshayPR244/Coursera-EPFL-Digital-Signal-Processing | bdf9c65e2c02f0a99336cbe60ebac919891e05e3 | [
"MIT"
] | 2 | 2020-07-24T03:16:36.000Z | 2020-09-25T10:21:00.000Z | HaarBasis/HW 2.ipynb | AkshayPR244/Coursera-EPFL-Digital-Signal-Processing | bdf9c65e2c02f0a99336cbe60ebac919891e05e3 | [
"MIT"
] | null | null | null | HaarBasis/HW 2.ipynb | AkshayPR244/Coursera-EPFL-Digital-Signal-Processing | bdf9c65e2c02f0a99336cbe60ebac919891e05e3 | [
"MIT"
] | 1 | 2021-03-23T19:37:53.000Z | 2021-03-23T19:37:53.000Z | 17.494819 | 86 | 0.423071 | true | 1,003 | Qwen/Qwen-72B | 1. YES
2. YES | 0.847968 | 0.835484 | 0.708463 | __label__eng_Latn | 0.109798 | 0.484329 |
# Vibration modes of a membrane in parabolic coordinates
```python
%matplotlib notebook
```
```python
import numpy as np
from scipy.linalg import eigh
from sympy import (symbols, lambdify, init_printing,
expand, Matrix, diff, integrate)
from sympy.utilities.lambdify import lambdify
import matplot... | a5d20712f2212fc754d2fde3a28cde8dda6e7b61 | 297,282 | ipynb | Jupyter Notebook | variational/parabolic_membrane.ipynb | nicoguaro/FEM_resources | 32f032a4e096fdfd2870e0e9b5269046dd555aee | [
"MIT"
] | 28 | 2015-11-06T16:59:39.000Z | 2022-02-25T18:18:49.000Z | variational/parabolic_membrane.ipynb | oldninja/FEM_resources | e44f315be217fd78ba95c09e3c94b1693773c047 | [
"MIT"
] | null | null | null | variational/parabolic_membrane.ipynb | oldninja/FEM_resources | e44f315be217fd78ba95c09e3c94b1693773c047 | [
"MIT"
] | 9 | 2018-06-24T22:12:00.000Z | 2022-01-12T15:57:37.000Z | 153.317174 | 167,179 | 0.83741 | true | 1,489 | Qwen/Qwen-72B | 1. YES
2. YES | 0.868827 | 0.79053 | 0.686834 | __label__eng_Latn | 0.382669 | 0.434077 |
```julia
using Distributions
using Plots
using WebIO
WebIO.install_jupyter_nbextension()
using Interact
```
<p
id="webio-warning-9096107456339008615"
class="output_text output_stderr"
style="padding: 1em; font-weight: bold;"
>
Unable to load WebIO. Please make sure WebIO works for your Jupyter clie... | c4e73eece48ce7ce5f32af500e6e4fa6552c5876 | 480,478 | ipynb | Jupyter Notebook | notebooks/Weber-ProbModels.ipynb | dominikstrb/Fechner.jl | 8761b988db4e153c6ebd3f115a91d07aad99a5db | [
"MIT"
] | 1 | 2021-11-22T19:49:54.000Z | 2021-11-22T19:49:54.000Z | notebooks/Weber-ProbModels.ipynb | dominikstrb/Fechner.jl | 8761b988db4e153c6ebd3f115a91d07aad99a5db | [
"MIT"
] | null | null | null | notebooks/Weber-ProbModels.ipynb | dominikstrb/Fechner.jl | 8761b988db4e153c6ebd3f115a91d07aad99a5db | [
"MIT"
] | null | null | null | 250.509906 | 63,324 | 0.642912 | true | 3,153 | Qwen/Qwen-72B | 1. YES
2. YES | 0.855851 | 0.817574 | 0.699722 | __label__eng_Latn | 0.953595 | 0.46402 |
```python
from thewalrus import hafnian, tor, quantum, samples, reduction, symplectic, threshold_detection_prob
import strawberryfields as sf
from strawberryfields.ops import *
import numpy as np
from sympy.utilities.iterables import multiset_permutations
import matplotlib.pyplot as plt
%matplotlib inline
%config Inlin... | e95ac8c071c1a59129187b2a6822cb6616563813 | 241,339 | ipynb | Jupyter Notebook | demo.ipynb | stacy8popova/PyGBSThr | d0a6ba4ad99dc23d1cfad581926b098b6a9222ed | [
"MIT"
] | 1 | 2021-11-29T08:59:18.000Z | 2021-11-29T08:59:18.000Z | demo.ipynb | stacy8popova/PyGBSThr | d0a6ba4ad99dc23d1cfad581926b098b6a9222ed | [
"MIT"
] | null | null | null | demo.ipynb | stacy8popova/PyGBSThr | d0a6ba4ad99dc23d1cfad581926b098b6a9222ed | [
"MIT"
] | null | null | null | 39.492554 | 305 | 0.431381 | true | 23,322 | Qwen/Qwen-72B | 1. YES
2. YES | 0.817574 | 0.795658 | 0.65051 | __label__krc_Cyrl | 0.274976 | 0.349683 |
# What's up with polynomial regression?
Why do we have to use this `PolynomialFeatures` thing from scikit? What does it do?
Let's imagine we have some data from which we know the true function we want our model to learn. This function is:
\begin{align}
y = 3x + 1x^2 -2
\end{align}
```python
import numpy
from skle... | 44b4249dfb1cb69b0751cbd16b3b094d7f0e845b | 5,803 | ipynb | Jupyter Notebook | cmsc_210/examples/lecture_25/notebooks/PolynomialFeatureTransforms.ipynb | mazelife/cmsc-210 | dbaa1604ef49bcfe5a70e09c17fbd243a8b80220 | [
"MIT"
] | null | null | null | cmsc_210/examples/lecture_25/notebooks/PolynomialFeatureTransforms.ipynb | mazelife/cmsc-210 | dbaa1604ef49bcfe5a70e09c17fbd243a8b80220 | [
"MIT"
] | 5 | 2022-01-16T23:30:12.000Z | 2022-01-30T23:03:21.000Z | cmsc_210/examples/lecture_25/notebooks/PolynomialFeatureTransforms.ipynb | mazelife/cmsc-210 | dbaa1604ef49bcfe5a70e09c17fbd243a8b80220 | [
"MIT"
] | null | null | null | 23.589431 | 211 | 0.513527 | true | 757 | Qwen/Qwen-72B | 1. YES
2. YES | 0.952574 | 0.879147 | 0.837452 | __label__eng_Latn | 0.988029 | 0.784016 |
<a href="https://colab.research.google.com/github/john-s-butler-dit/Numerical-Analysis-Python/blob/master/Chapter%2001%20-%20Euler%20Methods/102_Euler_method_with_Theorems_nonlinear_Growth_function.ipynb" target="_parent"></a>
# Euler Method with Theorems Applied to Non-Linear Population Equations
The more general ... | 405e935a2f5e3e8f618500e437847118f60406f4 | 68,768 | ipynb | Jupyter Notebook | Chapter 01 - Euler Methods/102_Euler_method_with_Theorems_nonlinear_Growth_function.ipynb | john-s-butler-dit/Numerical-Analysis-Python | edd89141efc6f46de303b7ccc6e78df68b528a91 | [
"MIT"
] | 69 | 2019-09-05T21:39:12.000Z | 2022-03-26T14:00:25.000Z | Chapter 01 - Euler Methods/102_Euler_method_with_Theorems_nonlinear_Growth_function.ipynb | Zak2020/Numerical-Analysis-Python | edd89141efc6f46de303b7ccc6e78df68b528a91 | [
"MIT"
] | null | null | null | Chapter 01 - Euler Methods/102_Euler_method_with_Theorems_nonlinear_Growth_function.ipynb | Zak2020/Numerical-Analysis-Python | edd89141efc6f46de303b7ccc6e78df68b528a91 | [
"MIT"
] | 13 | 2021-06-17T15:34:04.000Z | 2022-01-14T14:53:43.000Z | 90.010471 | 11,118 | 0.762113 | true | 4,206 | Qwen/Qwen-72B | 1. YES
2. YES | 0.810479 | 0.865224 | 0.701246 | __label__eng_Latn | 0.897841 | 0.467561 |
```python
%pylab inline
%config InlineBackend.figure_format = 'retina'
from ipywidgets import interact
```
# Question 1
The Lagrange interpolating polynomial is
$$ p(x) = \sum_{j=0}^{n}y_j L_j(x).$$
Show that the identity ,
$$ \sum_{j=0}^{n} L_j(x) = 1,$$
is true for all $x$.
**Hint: The answer requires no algebra. U... | 9111b6a787b67440bf9f48ffd9a64cf7a21794ab | 5,863 | ipynb | Jupyter Notebook | Homework 5 Problems.ipynb | newby-jay/MATH381-Fall2021-JupyterNotebooks | 9181fb6e154081de26fb267e0794a67f60ae11a0 | [
"Apache-2.0"
] | null | null | null | Homework 5 Problems.ipynb | newby-jay/MATH381-Fall2021-JupyterNotebooks | 9181fb6e154081de26fb267e0794a67f60ae11a0 | [
"Apache-2.0"
] | null | null | null | Homework 5 Problems.ipynb | newby-jay/MATH381-Fall2021-JupyterNotebooks | 9181fb6e154081de26fb267e0794a67f60ae11a0 | [
"Apache-2.0"
] | null | null | null | 41.288732 | 486 | 0.526181 | true | 1,211 | Qwen/Qwen-72B | 1. YES
2. YES | 0.800692 | 0.950411 | 0.760986 | __label__eng_Latn | 0.981189 | 0.606359 |
```
# default_exp oneDim
```
```
#hide
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.cm as cm
plt.rcParams['figure.figsize'] = (10,6)
import sympy; sympy.init_printing()
# code for displaying matrices nicely
def display_matrix(m):
display(sympy.Matrix(m))
```
# oneDim
> Code for a 1-D p... | 847b7cc9c66b56fc94363426d486b3e9f254fbc8 | 411,548 | ipynb | Jupyter Notebook | 00_oneDim.ipynb | YanniPapandreou/statFEM | 189ddbb9c2f5a363d6e7e2f62a893cb3706e45bb | [
"Apache-2.0"
] | 1 | 2022-02-04T09:26:33.000Z | 2022-02-04T09:26:33.000Z | 00_oneDim.ipynb | YanniPapandreou/statFEM | 189ddbb9c2f5a363d6e7e2f62a893cb3706e45bb | [
"Apache-2.0"
] | null | null | null | 00_oneDim.ipynb | YanniPapandreou/statFEM | 189ddbb9c2f5a363d6e7e2f62a893cb3706e45bb | [
"Apache-2.0"
] | null | null | null | 221.85876 | 218,180 | 0.902514 | true | 12,742 | Qwen/Qwen-72B | 1. YES
2. YES | 0.917303 | 0.822189 | 0.754196 | __label__eng_Latn | 0.968941 | 0.590583 |
# A study on spontaneous decay rate of an atom in presence of a square dielectric waveguide using BEM approach
In these notes, I calculate the Local Density of States (LDOS), or the imaginary part of the on-site Green's function and hence the modified spontaneous emission rate of an atom in presence of a square dielec... | e818d84159ecec7d1ac061f8f432dc633cbfd2a2 | 477,016 | ipynb | Jupyter Notebook | sqwg_BEM.ipynb | i2000s/simwaveguide | e74233ee5d108c57d50818dc8172cb716c0185b0 | [
"MIT"
] | null | null | null | sqwg_BEM.ipynb | i2000s/simwaveguide | e74233ee5d108c57d50818dc8172cb716c0185b0 | [
"MIT"
] | null | null | null | sqwg_BEM.ipynb | i2000s/simwaveguide | e74233ee5d108c57d50818dc8172cb716c0185b0 | [
"MIT"
] | null | null | null | 503.712777 | 226,998 | 0.932749 | true | 11,596 | Qwen/Qwen-72B | 1. YES
2. YES | 0.785309 | 0.721743 | 0.566791 | __label__eng_Latn | 0.701871 | 0.155176 |
# Mousai: An Open-Source General Purpose Harmonic Balance Solver
Theory and Algorithm
November, 2019
## Overview
A wide array of contemporary problems can be represented by nonlinear ordinary differential equations with solutions that can be represented by Fourier Series:
* **Limit cycle oscillation of wings/bla... | 88cb40864953d52e1b4a2bc40da6292614e25e7b | 207,550 | ipynb | Jupyter Notebook | docs/algorithm/Algorithm.ipynb | CodingPenguin1/mousai | 0509d46f452a4baecb86822211f209b70a4a2522 | [
"BSD-3-Clause"
] | 19 | 2018-02-05T16:13:45.000Z | 2021-06-29T09:23:22.000Z | docs/algorithm/Algorithm.ipynb | josephcslater/mousai | 165ff167c3f8b092857586c5958e6469da78be96 | [
"BSD-3-Clause"
] | 15 | 2017-05-23T13:45:16.000Z | 2021-08-15T16:13:51.000Z | docs/algorithm/Algorithm.ipynb | CodingPenguin1/mousai | 0509d46f452a4baecb86822211f209b70a4a2522 | [
"BSD-3-Clause"
] | 15 | 2017-05-18T17:50:49.000Z | 2021-07-31T17:31:36.000Z | 186.310592 | 34,030 | 0.893891 | true | 4,757 | Qwen/Qwen-72B | 1. YES
2. YES | 0.859664 | 0.76908 | 0.66115 | __label__eng_Latn | 0.891001 | 0.374405 |
# The Jupyter notebook
[IPython](https://ipython.org) provides a **kernel** for [Jupyter](https://jupyter.org).
Jupyter is the name for this notebook interface,
and the document format.
Notebooks can contain [Markdown](https://help.github.com/articles/markdown-basics/) like this cell here,
as well as mathematics re... | fd8ee59931871ad9c3b753a1b6ccb72cab202d6c | 281,403 | ipynb | Jupyter Notebook | 2018-05-25-jupyter/Intro to IPython.ipynb | Anastasiia-Grishina/simula-tools-meetup | 2a1d661e818fb31750ced15170797d6ad47c7996 | [
"Unlicense"
] | 9 | 2018-04-20T13:12:08.000Z | 2021-11-08T09:28:22.000Z | 2018-05-25-jupyter/Intro to IPython.ipynb | Anastasiia-Grishina/simula-tools-meetup | 2a1d661e818fb31750ced15170797d6ad47c7996 | [
"Unlicense"
] | 1 | 2019-05-03T14:44:19.000Z | 2019-05-03T14:44:19.000Z | 2018-05-25-jupyter/Intro to IPython.ipynb | Anastasiia-Grishina/simula-tools-meetup | 2a1d661e818fb31750ced15170797d6ad47c7996 | [
"Unlicense"
] | 5 | 2018-04-20T13:13:49.000Z | 2021-10-31T07:55:35.000Z | 99.400565 | 145,036 | 0.779174 | true | 31,896 | Qwen/Qwen-72B | 1. YES
2. YES | 0.705785 | 0.83762 | 0.59118 | __label__eng_Latn | 0.58017 | 0.211839 |
## Rosenbrock
The definition ca be found in <cite data-cite="rosenbrock"></cite>. It is a non-convex function, introduced by Howard H. Rosenbrock in 1960 and also known as Rosenbrock's valley or Rosenbrock's banana function.
**Definition**
\begin{align}
\begin{split}
f(x) &=& \sum_{i=1}^{n-1} \bigg[100 (x_{i+1}-x_i... | 91c2bd9fd79054cad36dff668ceca6a0e581698a | 377,300 | ipynb | Jupyter Notebook | doc/source/problems/single/rosenbrock.ipynb | gabicavalcante/pymoo | 1711ce3a96e5ef622d0116d6c7ea4d26cbe2c846 | [
"Apache-2.0"
] | 11 | 2018-05-22T17:38:02.000Z | 2022-02-28T03:34:33.000Z | doc/source/problems/single/rosenbrock.ipynb | gabicavalcante/pymoo | 1711ce3a96e5ef622d0116d6c7ea4d26cbe2c846 | [
"Apache-2.0"
] | 15 | 2022-01-03T19:36:36.000Z | 2022-03-30T03:57:58.000Z | doc/source/problems/single/rosenbrock.ipynb | gabicavalcante/pymoo | 1711ce3a96e5ef622d0116d6c7ea4d26cbe2c846 | [
"Apache-2.0"
] | 3 | 2021-11-22T08:01:47.000Z | 2022-03-11T08:53:58.000Z | 3,042.741935 | 374,816 | 0.964551 | true | 267 | Qwen/Qwen-72B | 1. YES
2. YES | 0.904651 | 0.841826 | 0.761558 | __label__eng_Latn | 0.714133 | 0.607687 |
# Nonlinear vibrations<br>Part 2
## Van der Pol equation
### Limiting cycle
Method of averaging:
Van der Pol equation for autonomous system with negative damping
$$\ddot{x}-\epsilon(1-x^2)\dot{x}+x=0$$
Use method of averaging to estimate amplitude in case of small nonlinearity $\epsilon \ll 1$:
\begin{aligned}
x... | 01172105afa411f36bb4dc425c639143b29bd7a5 | 273,509 | ipynb | Jupyter Notebook | vdp-oscillator/vdp-1.ipynb | vr050714/nonlinear-vibration-seminar | 663584d46708857383b637610e54fafa753250e2 | [
"CC0-1.0"
] | 1 | 2021-05-26T05:38:38.000Z | 2021-05-26T05:38:38.000Z | vdp-oscillator/vdp-1.ipynb | vr050714/nonlinear-vibration-seminar | 663584d46708857383b637610e54fafa753250e2 | [
"CC0-1.0"
] | null | null | null | vdp-oscillator/vdp-1.ipynb | vr050714/nonlinear-vibration-seminar | 663584d46708857383b637610e54fafa753250e2 | [
"CC0-1.0"
] | null | null | null | 526.992293 | 98,828 | 0.944525 | true | 2,374 | Qwen/Qwen-72B | 1. YES
2. YES | 0.913677 | 0.863392 | 0.788861 | __label__eng_Latn | 0.253613 | 0.67112 |
# Quick Start: 単振り子の運動をシミュレーション
* Next >> None.
* Prev >> [1_modeling](https://github.com/yfur/basic-mechanics-python/blob/master/1_modeling/1_modeling.ipynb)
[単振り子](https://ja.wikipedia.org/wiki/%E6%8C%AF%E3%82%8A%E5%AD%90#.E5.8D.98.E6.8C.AF.E3.82.8A.E5.AD.90) の運動をシミュレーションする．
## 0. はじめに
一言に力学シミュレーションといっても，その方法は... | 68cfb6b89ec11d0ae62fa18b947ef18fb5d8896a | 10,381 | ipynb | Jupyter Notebook | 0_quickstart/.ipynb_checkpoints/0_quickstart-checkpoint.ipynb | yfur/basic-mechanics-python | fb313b01a116180a249a1f78e28aa5685030b2ea | [
"Apache-2.0"
] | 1 | 2021-09-17T11:34:59.000Z | 2021-09-17T11:34:59.000Z | 0_quickstart/0_quickstart.ipynb | yfur/basic-mechanics-python | fb313b01a116180a249a1f78e28aa5685030b2ea | [
"Apache-2.0"
] | null | null | null | 0_quickstart/0_quickstart.ipynb | yfur/basic-mechanics-python | fb313b01a116180a249a1f78e28aa5685030b2ea | [
"Apache-2.0"
] | null | null | null | 27.756684 | 216 | 0.51103 | true | 3,380 | Qwen/Qwen-72B | 1. YES
2. YES | 0.917303 | 0.737158 | 0.676197 | __label__yue_Hant | 0.546338 | 0.409364 |
# Description of the problem and solution
The task1 was to predict a person's age from the brain image data: a standard regression problem. The original dataset included 832 features as well as a lot of NaN values and a few outliers. A good preprocessing stage was necessary in order to have a well defined dataset that... | 3ed804506389baf26e7796d1971f4dc27cdc3fce | 67,892 | ipynb | Jupyter Notebook | Task 1/Task_1_AML.ipynb | KonstantinosBarmpas/Advanced-Machine-Learning-Projects | 61839d3933c3299666536b4daff53344af214b84 | [
"MIT"
] | 13 | 2020-10-15T19:45:05.000Z | 2022-01-15T19:38:29.000Z | Task 1/Task_1_AML.ipynb | KonstantinosBarmpas/Advanced-Machine-Learning-Projects | 61839d3933c3299666536b4daff53344af214b84 | [
"MIT"
] | null | null | null | Task 1/Task_1_AML.ipynb | KonstantinosBarmpas/Advanced-Machine-Learning-Projects | 61839d3933c3299666536b4daff53344af214b84 | [
"MIT"
] | 12 | 2020-09-27T13:15:00.000Z | 2021-11-22T17:29:55.000Z | 38.270575 | 1,795 | 0.350071 | true | 12,794 | Qwen/Qwen-72B | 1. YES
2. YES | 0.766294 | 0.705785 | 0.540839 | __label__yue_Hant | 0.182175 | 0.094879 |
# Single-qubit geometric gates
In this notebook we create a single-qubit geometric gate between the $|0\rangle$ and $|2\rangle$ state of the transmon. The ideal unitary operator describing the single-qubit geometric gate in the $\{|0\rangle, |2\rangle\}$ basis is
\begin{align}
U_g = \begin{pmatrix}
\cos\theta & e^{i... | 863dd42dccc98e75db35fc4c98597beb77eaa006 | 513,607 | ipynb | Jupyter Notebook | terra/qis_adv/single_qubit_geometric_gates.ipynb | YumaNK/qiskit-community-tutorials | 491fbb7ef1f99772d25eb6eacb4340ef1ac75253 | [
"Apache-2.0"
] | 293 | 2020-05-29T17:03:04.000Z | 2022-03-31T07:09:50.000Z | terra/qis_adv/single_qubit_geometric_gates.ipynb | YumaNK/qiskit-community-tutorials | 491fbb7ef1f99772d25eb6eacb4340ef1ac75253 | [
"Apache-2.0"
] | 30 | 2020-06-23T19:11:32.000Z | 2021-12-20T22:25:54.000Z | terra/qis_adv/single_qubit_geometric_gates.ipynb | YumaNK/qiskit-community-tutorials | 491fbb7ef1f99772d25eb6eacb4340ef1ac75253 | [
"Apache-2.0"
] | 204 | 2020-06-08T12:55:52.000Z | 2022-03-31T08:37:14.000Z | 469.047489 | 145,396 | 0.940762 | true | 5,995 | Qwen/Qwen-72B | 1. YES
2. YES | 0.771844 | 0.672332 | 0.518935 | __label__eng_Latn | 0.917112 | 0.043989 |
<!-- dom:TITLE: PHY321: Time-dependent Forces and Fourier Series, begin two-body problems -->
# PHY321: Time-dependent Forces and Fourier Series, begin two-body problems
<!-- dom:AUTHOR: [Morten Hjorth-Jensen](http://mhjgit.github.io/info/doc/web/) at Department of Physics and Astronomy and Facility for Rare Ion Beams ... | 48d72eec3c5bd01d06d8bd5a2389891d31293565 | 68,128 | ipynb | Jupyter Notebook | doc/pub/week10/ipynb/.ipynb_checkpoints/week10-checkpoint.ipynb | Shield94/Physics321 | 9875a3bf840b0fa164b865a3cb13073aff9094ca | [
"CC0-1.0"
] | 20 | 2020-01-09T17:41:16.000Z | 2022-03-09T00:48:58.000Z | doc/pub/week10/ipynb/.ipynb_checkpoints/week10-checkpoint.ipynb | Shield94/Physics321 | 9875a3bf840b0fa164b865a3cb13073aff9094ca | [
"CC0-1.0"
] | 6 | 2020-01-08T03:47:53.000Z | 2020-12-15T15:02:57.000Z | doc/pub/week10/ipynb/.ipynb_checkpoints/week10-checkpoint.ipynb | Shield94/Physics321 | 9875a3bf840b0fa164b865a3cb13073aff9094ca | [
"CC0-1.0"
] | 33 | 2020-01-10T20:40:55.000Z | 2022-02-11T20:28:41.000Z | 29.841437 | 1,111 | 0.525408 | true | 13,870 | Qwen/Qwen-72B | 1. YES
2. YES | 0.752013 | 0.819893 | 0.61657 | __label__eng_Latn | 0.960675 | 0.270829 |
# Perfect captive
# Purpose
If the matematical model is not correct or too little data is available this may lead to paramter drift, so that the parameters in the matematical model changes depending on how the fitted data has been sampled.
This notebooks showcases the perfect case when you have the correct model and ... | 5fb24f24754c7d298cead786b571a908bda50e93 | 8,869 | ipynb | Jupyter Notebook | notebooks/21.04_perfect_captive.ipynb | martinlarsalbert/wPCC | 16e0d4cc850d503247916c9f5bd9f0ddb07f8930 | [
"MIT"
] | null | null | null | notebooks/21.04_perfect_captive.ipynb | martinlarsalbert/wPCC | 16e0d4cc850d503247916c9f5bd9f0ddb07f8930 | [
"MIT"
] | null | null | null | notebooks/21.04_perfect_captive.ipynb | martinlarsalbert/wPCC | 16e0d4cc850d503247916c9f5bd9f0ddb07f8930 | [
"MIT"
] | null | null | null | 25.34 | 220 | 0.581464 | true | 1,148 | Qwen/Qwen-72B | 1. YES
2. YES | 0.760651 | 0.822189 | 0.625399 | __label__eng_Latn | 0.655172 | 0.291341 |
# Bracketing Methods (Bisection example)
Consider the refrigeration tank example from Belegundu and Chandrupatla [1].
We want to minimize the cost of a cylindrical refrigeration tank that must have
a volume of 50 m$^3$.
The costs of the tank are
- Circular ends cost \$10 per m$^2$
- Cylindrical walls cost \$6 per m$^2... | 164eb530e59640e762528d5a0a7769a57d1f9426 | 58,346 | ipynb | Jupyter Notebook | LineSearch.ipynb | BYUFLOWLab/MDOnotebooks | 49344cb874a52cd67cc04ebb728195fa025d5590 | [
"MIT"
] | 4 | 2017-03-13T23:22:32.000Z | 2017-08-10T14:15:31.000Z | LineSearch.ipynb | BYUFLOWLab/MDOnotebooks | 49344cb874a52cd67cc04ebb728195fa025d5590 | [
"MIT"
] | null | null | null | LineSearch.ipynb | BYUFLOWLab/MDOnotebooks | 49344cb874a52cd67cc04ebb728195fa025d5590 | [
"MIT"
] | 1 | 2019-03-12T11:31:01.000Z | 2019-03-12T11:31:01.000Z | 236.218623 | 28,090 | 0.903678 | true | 1,228 | Qwen/Qwen-72B | 1. YES
2. YES | 0.92079 | 0.893309 | 0.82255 | __label__eng_Latn | 0.980195 | 0.749392 |
$\newcommand{\xv}{\mathbf{x}}
\newcommand{\tv}{\mathbf{t}}
\newcommand{\wv}{\mathbf{w}}
\newcommand{\Chi}{\mathcal{X}}
\newcommand{\R}{\rm I\!R}
\newcommand{\sign}{\text{sign}}
\newcommand{\Tm}{\mathbf{T}}
\newcommand{\Xm}{\mathbf{X}}
\newcommand{\Im}{\mathbf{I}}
$
### ITCS6155
# Linear Model
**Supervised Le... | e07d8a481e03a89a6ea631a9d35ee102b61104fb | 56,922 | ipynb | Jupyter Notebook | reading_assignments/questions/1_Note-Linear Model.ipynb | biqar/Fall-2020-ITCS-8156-MachineLearning | ce14609327e5fa13f7af7b904a69da3aa3606f37 | [
"MIT"
] | null | null | null | reading_assignments/questions/1_Note-Linear Model.ipynb | biqar/Fall-2020-ITCS-8156-MachineLearning | ce14609327e5fa13f7af7b904a69da3aa3606f37 | [
"MIT"
] | null | null | null | reading_assignments/questions/1_Note-Linear Model.ipynb | biqar/Fall-2020-ITCS-8156-MachineLearning | ce14609327e5fa13f7af7b904a69da3aa3606f37 | [
"MIT"
] | null | null | null | 94.87 | 12,154 | 0.811338 | true | 3,598 | Qwen/Qwen-72B | 1. YES
2. YES | 0.805632 | 0.833325 | 0.671353 | __label__eng_Latn | 0.989409 | 0.398109 |
## Model Based Submodular Selection
Author: Jacob Schreiber <jmschreiber91@gmail.com>
Submodular selection is the task of identifying a representative subset of samples from a large set, and apricot focuses on the use of these algorithms to identify a good subset of data that can be used for the purpose of training m... | a4aedfaa6ade51b78cd22a2eee2e5dced1e72340 | 345,760 | ipynb | Jupyter Notebook | tutorials/3. Model-Based Selection.ipynb | domoritz/apricot | 6dff8d08dee9145ec6c7e3e79d77efa0bbf19474 | [
"MIT"
] | null | null | null | tutorials/3. Model-Based Selection.ipynb | domoritz/apricot | 6dff8d08dee9145ec6c7e3e79d77efa0bbf19474 | [
"MIT"
] | null | null | null | tutorials/3. Model-Based Selection.ipynb | domoritz/apricot | 6dff8d08dee9145ec6c7e3e79d77efa0bbf19474 | [
"MIT"
] | null | null | null | 704.195519 | 75,350 | 0.935687 | true | 4,017 | Qwen/Qwen-72B | 1. YES
2. YES | 0.76908 | 0.661923 | 0.509072 | __label__eng_Latn | 0.98845 | 0.021074 |
# Game instructions
Consider the following board game: A game board has 12 spaces. The swine senses the Christmas spirit and manages to run away from home couple of weeks beforehand. Fortunately for it, the butcher is a bit of a drunkard and easily distracted. The swine starts on space 7, and a butcher on space 1. On e... | b0b1f31011d80ff94471c0a6d78e4ab2d8afb62a | 12,936 | ipynb | Jupyter Notebook | dp.ipynb | MeekeRoet/swine-escape | 4201354dbc6cef7f84b6c6b7ad395292b29cccf8 | [
"MIT"
] | null | null | null | dp.ipynb | MeekeRoet/swine-escape | 4201354dbc6cef7f84b6c6b7ad395292b29cccf8 | [
"MIT"
] | null | null | null | dp.ipynb | MeekeRoet/swine-escape | 4201354dbc6cef7f84b6c6b7ad395292b29cccf8 | [
"MIT"
] | null | null | null | 35.152174 | 716 | 0.474103 | true | 2,486 | Qwen/Qwen-72B | 1. YES
2. YES | 0.921922 | 0.90599 | 0.835252 | __label__eng_Latn | 0.975705 | 0.778903 |
<h1 style='text-align:center'>Simulação de Canal de Comunicação segundo Modelo Erceg</h1>
```python
import numpy as np
import random
import matplotlib.pyplot as plt
from PIL import Image
import math
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
#%%latex
```
O modelo Erceg foi construído e estudad... | de05f8820f821f3042a22caea9437cf83133085f | 315,050 | ipynb | Jupyter Notebook | Channel-Simulation/Erceg-Model.ipynb | JoaoPedroPP/Channel-Simulation-and-OFDM-Study | 7b9bc7422c59012bd73ef6f33c4a18a24be4d309 | [
"Apache-2.0"
] | 1 | 2021-04-22T07:22:57.000Z | 2021-04-22T07:22:57.000Z | Channel-Simulation/Erceg-Model.ipynb | JoaoPedroPP/Channel-Simulation-and-OFDM-Study | 7b9bc7422c59012bd73ef6f33c4a18a24be4d309 | [
"Apache-2.0"
] | null | null | null | Channel-Simulation/Erceg-Model.ipynb | JoaoPedroPP/Channel-Simulation-and-OFDM-Study | 7b9bc7422c59012bd73ef6f33c4a18a24be4d309 | [
"Apache-2.0"
] | 2 | 2019-08-22T22:58:38.000Z | 2019-08-23T02:00:10.000Z | 628.842315 | 166,772 | 0.948281 | true | 3,258 | Qwen/Qwen-72B | 1. YES
2. YES | 0.754915 | 0.754915 | 0.569897 | __label__por_Latn | 0.95664 | 0.162391 |
## Histograms of Oriented Gradients (HOG)
As we saw with the ORB algorithm, we can use keypoints in images to do keypoint-based matching to detect objects in images. These type of algorithms work great when you want to detect objects that have a lot of consistent internal features that are not affected by the backgrou... | e6329139415ef5c9e43860dc689dcfc555bbb679 | 360,732 | ipynb | Jupyter Notebook | 1_4_Feature_Vectors/3_1. HOG.ipynb | matijazigic/CVND_Exercises | 52b9cdd76f64d5e5cb3454a657c8b64df06d0490 | [
"MIT"
] | null | null | null | 1_4_Feature_Vectors/3_1. HOG.ipynb | matijazigic/CVND_Exercises | 52b9cdd76f64d5e5cb3454a657c8b64df06d0490 | [
"MIT"
] | null | null | null | 1_4_Feature_Vectors/3_1. HOG.ipynb | matijazigic/CVND_Exercises | 52b9cdd76f64d5e5cb3454a657c8b64df06d0490 | [
"MIT"
] | null | null | null | 244.729986 | 285,087 | 0.8812 | true | 7,811 | Qwen/Qwen-72B | 1. YES
2. YES
| 0.754915 | 0.79053 | 0.596783 | __label__eng_Latn | 0.997525 | 0.224857 |
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