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# Module 1: Setting up the problem
Before we bgin, import SimPEG into ipython notebook as follows:
```
from SimPEG import *
from IPython.html.widgets import interactive
```
Efficiency Warning: Interpolation will be slow, use setup.py!
python setup.py build_ext --inplace
**Introdu... | f48bab604fde4d9d093cc731b13f41772d91afdc | 10,512 | ipynb | Jupyter Notebook | .ipynb_checkpoints/Module 1-checkpoint.ipynb | jokulhaup/directed_studies | 99f0e6e8cc7010d34db1d9bc37988e4944f66826 | [
"MIT"
] | 2 | 2017-10-08T02:10:35.000Z | 2017-10-18T17:49:21.000Z | .ipynb_checkpoints/Module 1-checkpoint.ipynb | jokulhaup/directed_studies | 99f0e6e8cc7010d34db1d9bc37988e4944f66826 | [
"MIT"
] | null | null | null | .ipynb_checkpoints/Module 1-checkpoint.ipynb | jokulhaup/directed_studies | 99f0e6e8cc7010d34db1d9bc37988e4944f66826 | [
"MIT"
] | 3 | 2016-09-01T20:38:20.000Z | 2020-05-13T22:19:16.000Z | 31.473054 | 550 | 0.475171 | true | 2,135 | Qwen/Qwen-72B | 1. YES
2. YES | 0.909907 | 0.849971 | 0.773395 | __label__eng_Latn | 0.93898 | 0.635187 |
# 6. Internal dynamic factor
Based on:
[1] ISO 6336-1:2006 Calculation of load capacity of spur and helical gears -- Part 1: Basic principles, introduction and general influence factors
```python
from sympy import *
from matplotlib import pyplot
from numpy import arange
init_printing()
def symb(x, y, z = ''):
... | 74dfab5f15cd456569d2b848e4d6c70db4d0b02f | 25,327 | ipynb | Jupyter Notebook | notes/.ipynb_checkpoints/internal_dynamic_factor-checkpoint.ipynb | gfsReboucas/Drivetrain-python | 90cc8a0b26fa6dd851a8ddaaf321f5ae9f5cf431 | [
"MIT"
] | 1 | 2020-10-17T13:43:01.000Z | 2020-10-17T13:43:01.000Z | notes/.ipynb_checkpoints/internal_dynamic_factor-checkpoint.ipynb | gfsReboucas/Drivetrain-python | 90cc8a0b26fa6dd851a8ddaaf321f5ae9f5cf431 | [
"MIT"
] | null | null | null | notes/.ipynb_checkpoints/internal_dynamic_factor-checkpoint.ipynb | gfsReboucas/Drivetrain-python | 90cc8a0b26fa6dd851a8ddaaf321f5ae9f5cf431 | [
"MIT"
] | null | null | null | 78.900312 | 3,268 | 0.772417 | true | 718 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91118 | 0.839734 | 0.765149 | __label__eng_Latn | 0.912823 | 0.616029 |
## Exercise 1 : LDA Classification
From the last assignment, we have a basc understanding of how LDA works. Here, we want to use LDA on a practical example and see how it can help us in the classification process. Besides dimensionality reduction, LDA proides us with inforation of how important the new axes are for cl... | a7c218908deef46d674c5d895b14d809272ae246 | 9,713 | ipynb | Jupyter Notebook | 07_lda_classification/lda_classification.ipynb | jhinga-la-la/pattern-recognition-course | 7ad4f70b2c427f3c37f59f47768b90371873823c | [
"Apache-2.0"
] | null | null | null | 07_lda_classification/lda_classification.ipynb | jhinga-la-la/pattern-recognition-course | 7ad4f70b2c427f3c37f59f47768b90371873823c | [
"Apache-2.0"
] | null | null | null | 07_lda_classification/lda_classification.ipynb | jhinga-la-la/pattern-recognition-course | 7ad4f70b2c427f3c37f59f47768b90371873823c | [
"Apache-2.0"
] | null | null | null | 74.145038 | 636 | 0.695357 | true | 1,918 | Qwen/Qwen-72B | 1. YES
2. YES | 0.863392 | 0.861538 | 0.743845 | __label__eng_Latn | 0.999611 | 0.566533 |
# Module 1: Setting up the problem
### Introduction
Geophysical surveys consist of a similar basic framework. An energy source is delivered into the earth, which can be natural (for example, the Earth's magnetic field) or human-made (current in the ground, acoustic wave energy, etc.), and this stimulates a response a... | f9627d936c90278f54f1a4a3f779aa0bab540f8e | 24,825 | ipynb | Jupyter Notebook | .ipynb_checkpoints/Mod 1-Copy1-checkpoint.ipynb | jokulhaup/directed_studies | 99f0e6e8cc7010d34db1d9bc37988e4944f66826 | [
"MIT"
] | 2 | 2017-10-08T02:10:35.000Z | 2017-10-18T17:49:21.000Z | .ipynb_checkpoints/Mod 1-Copy1-checkpoint.ipynb | jokulhaup/directed_studies | 99f0e6e8cc7010d34db1d9bc37988e4944f66826 | [
"MIT"
] | null | null | null | .ipynb_checkpoints/Mod 1-Copy1-checkpoint.ipynb | jokulhaup/directed_studies | 99f0e6e8cc7010d34db1d9bc37988e4944f66826 | [
"MIT"
] | 3 | 2016-09-01T20:38:20.000Z | 2020-05-13T22:19:16.000Z | 39.342314 | 867 | 0.50566 | true | 5,590 | Qwen/Qwen-72B | 1. YES
2. YES | 0.899121 | 0.845942 | 0.760605 | __label__eng_Latn | 0.990223 | 0.605472 |
# Deutsch-Jozsa algorithm(ドイチ・ジョザ アルゴリズム)（概要）
Deutsch algorithm の一般化である Deutsch-Jozsa algorithm を説明します。
Deutsch-Jozsa algorithm は 00...000 から 11...111の $2^n$ 通りの入力をとりうる $f$ について、以下の条件のどちらかが成り立つものとします。
1. 全ての入力で $f(x)$ が同じ。
すなわち、全ての $x$ で $f(x)=0$ または 全ての $x$ で $f(x)=1$
2. 入力の半分で $f(x)$ が異なる。
すなわち、$2^{n-1}$ 個の $x$ ... | a595fee14c9a71a243b4ba84810bb14425694bb3 | 8,509 | ipynb | Jupyter Notebook | tutorial-ja/101_deutsch-jozsa_ja.ipynb | ssmi1975/Blueqat-tutorials | f2962a7eda733568d228cb1ebbcd2c2f409f84cb | [
"Apache-2.0"
] | 1 | 2022-02-09T02:10:48.000Z | 2022-02-09T02:10:48.000Z | tutorial-ja/101_deutsch-jozsa_ja.ipynb | ssmi1975/Blueqat-tutorials | f2962a7eda733568d228cb1ebbcd2c2f409f84cb | [
"Apache-2.0"
] | null | null | null | tutorial-ja/101_deutsch-jozsa_ja.ipynb | ssmi1975/Blueqat-tutorials | f2962a7eda733568d228cb1ebbcd2c2f409f84cb | [
"Apache-2.0"
] | null | null | null | 29.040956 | 316 | 0.477377 | true | 2,749 | Qwen/Qwen-72B | 1. YES
2. YES | 0.874077 | 0.715424 | 0.625336 | __label__yue_Hant | 0.359524 | 0.291195 |
```python
# stan implementation
import pystan
%pylab inline
from scipy.special import polygamma as pg
```
Populating the interactive namespace from numpy and matplotlib
Bad key "axes.color_cycle" on line 250 in
/home/matus/Desktop/matustools/matplotlibrc.
You probably need to get an updated matp... | dfe6e0fd27c677eef716102d379b852d2ee8e423 | 430,863 | ipynb | Jupyter Notebook | Statformulas.ipynb | simkovic/matustools | bd2444bfea5a02396e4960a7946160a60edebd49 | [
"MIT"
] | null | null | null | Statformulas.ipynb | simkovic/matustools | bd2444bfea5a02396e4960a7946160a60edebd49 | [
"MIT"
] | null | null | null | Statformulas.ipynb | simkovic/matustools | bd2444bfea5a02396e4960a7946160a60edebd49 | [
"MIT"
] | null | null | null | 226.412507 | 138,268 | 0.886395 | true | 13,156 | Qwen/Qwen-72B | 1. YES
2. YES | 0.787931 | 0.727975 | 0.573595 | __label__kor_Hang | 0.112504 | 0.170982 |
```python
# Add graph and math features
# 그래프, 수학 기능 추가
import pylab as py
# scipy.optimize.newton()
import scipy.optimize as so
```
```python
# symbolic processor
# 기호처리기
import sympy as sym
import sympy.utilities as su
sym.init_printing()
```
# 복소근과 뉴튼 랩슨법<br>Newton Rapson Method and Complex Roots
## A pol... | 25990ca1a78bb231cafd61adbd81b40b65d442f0 | 12,538 | ipynb | Jupyter Notebook | 10_root_finding/45_newton_raphson_complex.ipynb | kangwon-naver/nmisp | 141f8148b3ce783d3df27ee0c9986f530cada8fb | [
"BSD-3-Clause"
] | 7 | 2019-05-14T11:00:53.000Z | 2020-08-27T01:04:29.000Z | 10_root_finding/45_newton_raphson_complex.ipynb | kangwon-naver/nmisp | 141f8148b3ce783d3df27ee0c9986f530cada8fb | [
"BSD-3-Clause"
] | 170 | 2018-07-12T06:06:21.000Z | 2022-01-28T09:06:55.000Z | 10_root_finding/45_newton_raphson_complex.ipynb | kangwon-naver/nmisp | 141f8148b3ce783d3df27ee0c9986f530cada8fb | [
"BSD-3-Clause"
] | 57 | 2018-08-28T08:38:59.000Z | 2020-09-02T03:40:47.000Z | 21.286927 | 143 | 0.472563 | true | 2,046 | Qwen/Qwen-72B | 1. YES
2. YES | 0.845942 | 0.7773 | 0.657551 | __label__kor_Hang | 0.386067 | 0.366042 |
```python
import time
import random
from typing import List
import sympy
import math
import string
import types
```
```python
sympy.init_printing()
```
```python
def quick_sort(collection: list) -> list:
if len(collection) < 2:
return collection
pivot = collection.pop()
greater: List[int] = []
... | da8f40a3234b70ecef2933f0f68610534c60b054 | 60,724 | ipynb | Jupyter Notebook | sorts/my_algo.ipynb | wuchenchen/Python | 301ccb57d6ce5fc1d0edff40260464152da5bbc7 | [
"MIT"
] | 1 | 2021-08-25T13:29:58.000Z | 2021-08-25T13:29:58.000Z | sorts/my_algo.ipynb | wuchenchen/Python | 301ccb57d6ce5fc1d0edff40260464152da5bbc7 | [
"MIT"
] | null | null | null | sorts/my_algo.ipynb | wuchenchen/Python | 301ccb57d6ce5fc1d0edff40260464152da5bbc7 | [
"MIT"
] | null | null | null | 100.703151 | 18,867 | 0.81192 | true | 1,448 | Qwen/Qwen-72B | 1. YES
2. YES | 0.857768 | 0.83762 | 0.718484 | __label__eng_Latn | 0.331226 | 0.50761 |
# CHEM 1000 - Spring 2022
Prof. Geoffrey Hutchison, University of Pittsburgh
## Graded Homework 6
For this homework, we'll focus on:
- integrals in 2D polar and 3D spherical space
- probability (including integrating continuous distributions)
---
As a reminder, you do not need to use Python to solve the problems. If... | 1ce0df6b99e963f4fc402df420d611e96b0bf2a1 | 8,669 | ipynb | Jupyter Notebook | homework/ps6/ps6.ipynb | ghutchis/chem1000 | 07a7eac20cc04ee9a1bdb98339fbd5653a02a38d | [
"CC-BY-4.0"
] | 12 | 2020-06-23T18:44:37.000Z | 2022-03-14T10:13:05.000Z | homework/ps6/ps6.ipynb | ghutchis/chem1000 | 07a7eac20cc04ee9a1bdb98339fbd5653a02a38d | [
"CC-BY-4.0"
] | null | null | null | homework/ps6/ps6.ipynb | ghutchis/chem1000 | 07a7eac20cc04ee9a1bdb98339fbd5653a02a38d | [
"CC-BY-4.0"
] | 4 | 2021-07-29T10:45:23.000Z | 2021-10-16T09:51:00.000Z | 27.520635 | 334 | 0.560272 | true | 977 | Qwen/Qwen-72B | 1. YES
2. YES | 0.904651 | 0.909907 | 0.823148 | __label__eng_Latn | 0.980534 | 0.750781 |
# Cart-pole swing-up problem: interactive demonstration
Hello and welcome. This is a Jupyter Notebook, a kind of document that can alternate between static content, like text and images, and executable cells of code.
This document ilustrates the Cart-pole swing-up test case of the paper: "Collocation Methods for Seco... | 8061224c1e7c6ca917e0f0ad15bc00a63315d585 | 39,007 | ipynb | Jupyter Notebook | Cartpole-demo.ipynb | AunSiro/Second-Order-Schemes | ef7ac9a6755e166d81b83f584f82055d38265087 | [
"MIT"
] | null | null | null | Cartpole-demo.ipynb | AunSiro/Second-Order-Schemes | ef7ac9a6755e166d81b83f584f82055d38265087 | [
"MIT"
] | null | null | null | Cartpole-demo.ipynb | AunSiro/Second-Order-Schemes | ef7ac9a6755e166d81b83f584f82055d38265087 | [
"MIT"
] | null | null | null | 34.79661 | 298 | 0.508242 | true | 8,390 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91611 | 0.903294 | 0.827517 | __label__eng_Latn | 0.538571 | 0.760931 |
# Lecture 18 - Intro to data science (https://bit.ly/intro_python_18)
Today we're going to look at doing simple machine learning with Python, as an intro to very basic data science.
The idea is not to give you a full knowledge of any single package or technique, rather to give you a sense for what is possible.
To ke... | ae8d32dcb0bdab8ff5661d152858d0913ffcff98 | 179,818 | ipynb | Jupyter Notebook | lecture_notebooks/L18 Data Science .ipynb | chmote/intro_python | f38be255ba37e7f6ea4a95e694c2a6580bebc4d3 | [
"MIT"
] | 1 | 2022-02-02T00:01:05.000Z | 2022-02-02T00:01:05.000Z | lecture_notebooks/L18 Data Science .ipynb | chmote/intro_python | f38be255ba37e7f6ea4a95e694c2a6580bebc4d3 | [
"MIT"
] | null | null | null | lecture_notebooks/L18 Data Science .ipynb | chmote/intro_python | f38be255ba37e7f6ea4a95e694c2a6580bebc4d3 | [
"MIT"
] | null | null | null | 40.728879 | 8,200 | 0.674204 | true | 12,001 | Qwen/Qwen-72B | 1. YES
2. YES | 0.849971 | 0.793106 | 0.674117 | __label__eng_Latn | 0.955633 | 0.404531 |
# Optimizer tweaks
```python
%load_ext autoreload
%autoreload 2
%matplotlib inline
```
```python
#export
from exp.nb_08 import *
```
## Imagenette data
We grab the data from the previous notebook.
```python
path = datasets.untar_data(datasets.URLs.IMAGENETTE_160)
```
```python
tfms = [make_rgb, ResizeFixed(1... | dcb3ba4d482fd3ef35d1297db70ccbf0157d3295 | 409,557 | ipynb | Jupyter Notebook | dev_course/dl2/09_optimizers-Copy1.ipynb | LaurenSpiegel/fastai_docs | 4fe6b62116d88dea9610548133e6cadb6b260a73 | [
"Apache-2.0"
] | null | null | null | dev_course/dl2/09_optimizers-Copy1.ipynb | LaurenSpiegel/fastai_docs | 4fe6b62116d88dea9610548133e6cadb6b260a73 | [
"Apache-2.0"
] | null | null | null | dev_course/dl2/09_optimizers-Copy1.ipynb | LaurenSpiegel/fastai_docs | 4fe6b62116d88dea9610548133e6cadb6b260a73 | [
"Apache-2.0"
] | null | null | null | 302.033186 | 108,604 | 0.927146 | true | 6,703 | Qwen/Qwen-72B | 1. YES
2. YES | 0.72487 | 0.749087 | 0.542991 | __label__eng_Latn | 0.915667 | 0.09988 |
## Calculation of exponent function using Maclaurin Series for x = 1
\begin{align}
e^x = \sum\limits_{n=0}^{\infty}\frac{x^n}{n!}
\end{align}
```python
# importing dependency functions
from math import exp as ideal_exp
from matplotlib import pyplot as plt
# initial guess for iteration number
iter_num = 25
# implem... | 2f621e33228403607ef161db5eaed5352f4920a7 | 110,190 | ipynb | Jupyter Notebook | exponent.ipynb | BatyaGG/numerical_methods | 40036c07ed4db2fb03fe0d188feeb440aa260ce2 | [
"MIT"
] | 1 | 2018-06-23T12:19:55.000Z | 2018-06-23T12:19:55.000Z | exponent.ipynb | BatyaGG/numerical_methods | 40036c07ed4db2fb03fe0d188feeb440aa260ce2 | [
"MIT"
] | null | null | null | exponent.ipynb | BatyaGG/numerical_methods | 40036c07ed4db2fb03fe0d188feeb440aa260ce2 | [
"MIT"
] | null | null | null | 256.853147 | 18,092 | 0.927834 | true | 1,455 | Qwen/Qwen-72B | 1. YES
2. YES | 0.942507 | 0.930458 | 0.876963 | __label__eng_Latn | 0.959154 | 0.875813 |
###### Content provided under a Creative Commons Attribution license, CC-BY 4.0; code under MIT license. (c)2014 Lorena A. Barba, Olivier Mesnard. Thanks: NSF for support via CAREER award #1149784.
[@LorenaABarba](https://twitter.com/LorenaABarba)
##### Version 0.4 -- April 2015
# Source panel method
We are now get... | 93931aa6d28e9d3fbaf9f605e4d26ed1ad449b27 | 176,488 | ipynb | Jupyter Notebook | lessons/10_Lesson10_sourcePanelMethod.ipynb | cpop-fr/AeroPython | 5b4a6f15ff2d6e49ad6ffbce0ad7ea72f15af451 | [
"CC-BY-4.0"
] | null | null | null | lessons/10_Lesson10_sourcePanelMethod.ipynb | cpop-fr/AeroPython | 5b4a6f15ff2d6e49ad6ffbce0ad7ea72f15af451 | [
"CC-BY-4.0"
] | null | null | null | lessons/10_Lesson10_sourcePanelMethod.ipynb | cpop-fr/AeroPython | 5b4a6f15ff2d6e49ad6ffbce0ad7ea72f15af451 | [
"CC-BY-4.0"
] | null | null | null | 148.184719 | 42,097 | 0.838607 | true | 8,864 | Qwen/Qwen-72B | 1. YES
2. YES | 0.893309 | 0.835484 | 0.746345 | __label__eng_Latn | 0.967478 | 0.572342 |
# Immersed Boundary Method
---
### Author: Marin Lauber
```python
import numpy as np
import matplotlib.pyplot as plt
import NSsolver as ns
try:
plt.style.use("jupyter")
except OSError:
print("Delaut syle in use")
```
Charles S Peskin (1972) developed the immersed boundary method (IBM) to tackle the problem ... | 29bb1f98988c03eb897bfdd9a77856803b299c61 | 53,838 | ipynb | Jupyter Notebook | 1D-Piston/Immersed-Boundary-Method.ipynb | marinlauber/FlexibleSheets | 487b035a5aea4a0f4cf5aa49c3eab5cb238aa1f7 | [
"MIT"
] | null | null | null | 1D-Piston/Immersed-Boundary-Method.ipynb | marinlauber/FlexibleSheets | 487b035a5aea4a0f4cf5aa49c3eab5cb238aa1f7 | [
"MIT"
] | null | null | null | 1D-Piston/Immersed-Boundary-Method.ipynb | marinlauber/FlexibleSheets | 487b035a5aea4a0f4cf5aa49c3eab5cb238aa1f7 | [
"MIT"
] | 2 | 2020-12-18T18:57:16.000Z | 2022-03-04T06:58:09.000Z | 229.097872 | 29,040 | 0.905773 | true | 1,419 | Qwen/Qwen-72B | 1. YES
2. YES | 0.903294 | 0.79053 | 0.714081 | __label__eng_Latn | 0.598078 | 0.497382 |
<a href="https://colab.research.google.com/github/nickwotton/MQP2019/blob/master/Nick/Copy_of_linearfunction01.ipynb" target="_parent"></a>
# Attempt to Improve Solving a Linear Function using a Nueral Network
Given code to use a neural network to fit a linear function, try to optimize the code to get a better fit, i.... | 07dd1b9f05749e7558944ce5181ea0f23d4b42c1 | 20,917 | ipynb | Jupyter Notebook | Nick/Copy_of_linearfunction01.ipynb | xulisong1/MQP2019 | c0fb22fd5a6ea23d579493d591b08f94375c07b8 | [
"MIT"
] | null | null | null | Nick/Copy_of_linearfunction01.ipynb | xulisong1/MQP2019 | c0fb22fd5a6ea23d579493d591b08f94375c07b8 | [
"MIT"
] | null | null | null | Nick/Copy_of_linearfunction01.ipynb | xulisong1/MQP2019 | c0fb22fd5a6ea23d579493d591b08f94375c07b8 | [
"MIT"
] | null | null | null | 48.531323 | 8,178 | 0.659703 | true | 1,593 | Qwen/Qwen-72B | 1. YES
2. YES | 0.896251 | 0.766294 | 0.686792 | __label__eng_Latn | 0.959372 | 0.433979 |
# A Cournot competition model with product differentiation
## Model Project
### Group: Anders&Frederik
#### Group members: Frederik Andresen, rjv586. Anders Meelby, zpw286.
**The model**
Consider two firms who compete in the same market i.e. a duopoly. The market is characterized by Cournot competetion:
... | 95542eca9c94c490afa381c15b8f9e4d4df03676 | 197,756 | ipynb | Jupyter Notebook | modelproject/model_project.ipynb | NumEconCopenhagen/projects-2020-anders-frederik | 1e0b4b89c65c11c99a8ceaf6c49984667c02f1e8 | [
"MIT"
] | null | null | null | modelproject/model_project.ipynb | NumEconCopenhagen/projects-2020-anders-frederik | 1e0b4b89c65c11c99a8ceaf6c49984667c02f1e8 | [
"MIT"
] | 8 | 2020-04-18T13:06:58.000Z | 2020-05-12T15:03:09.000Z | modelproject/model_project.ipynb | NumEconCopenhagen/projects-2020-anders-frederik | 1e0b4b89c65c11c99a8ceaf6c49984667c02f1e8 | [
"MIT"
] | 1 | 2020-04-19T09:34:52.000Z | 2020-04-19T09:34:52.000Z | 325.256579 | 62,236 | 0.923942 | true | 3,251 | Qwen/Qwen-72B | 1. YES
2. YES | 0.901921 | 0.859664 | 0.775349 | __label__eng_Latn | 0.986862 | 0.639727 |
```python
# General import
import numpy as np
import scipy.sparse as sparse
import time
import matplotlib.pyplot as plt
```
```python
# pyMPC import
from pyMPC.mpc import MPCController
```
## System dynamics ##
Point mass $M=2\; \text{Kg}$ subject to an input force $F_{ext}$ and viscous friction with coefficient... | 6f9f96dc82f52a3d89003a0b84fcf3fdd9941f4c | 59,138 | ipynb | Jupyter Notebook | examples/example_point_mass.ipynb | forgi86/pyMPC | 291db149554767a035fcb01df3fed7a6b3fe60e4 | [
"MIT"
] | 84 | 2019-05-28T09:27:37.000Z | 2022-03-31T08:38:23.000Z | examples/example_point_mass.ipynb | passion4energy/pyMPC | 4b004ba707dab49cd36d96a3575b8593c870a904 | [
"MIT"
] | 2 | 2020-04-17T00:03:27.000Z | 2021-01-30T11:35:58.000Z | examples/example_point_mass.ipynb | passion4energy/pyMPC | 4b004ba707dab49cd36d96a3575b8593c870a904 | [
"MIT"
] | 20 | 2019-10-13T13:50:16.000Z | 2022-03-31T08:38:25.000Z | 238.459677 | 52,688 | 0.910413 | true | 1,261 | Qwen/Qwen-72B | 1. YES
2. YES | 0.851953 | 0.815232 | 0.69454 | __label__eng_Latn | 0.570297 | 0.45198 |
>>> Work in Progress
#### Outline
- Perceptron
- Exponential Family
- Generalized Linear Models(GLM)
- Softmax Regression(Multiclass classification)
### Logistic Regression (Recap)
- Logistic Regression uses sigmoid function
- ranges from $-\infty$ to $\infty$, with values ranging from 0 to 1, which is probability
... | 2666fdacc5e0b736612814ce4656c4ded0a536e9 | 15,334 | ipynb | Jupyter Notebook | cs229_ml/lec04-Perceptron-GLM.ipynb | chandrabsingh/learnings | a3f507bbbf46582ce5a64991983dfc0759db0af5 | [
"MIT"
] | null | null | null | cs229_ml/lec04-Perceptron-GLM.ipynb | chandrabsingh/learnings | a3f507bbbf46582ce5a64991983dfc0759db0af5 | [
"MIT"
] | null | null | null | cs229_ml/lec04-Perceptron-GLM.ipynb | chandrabsingh/learnings | a3f507bbbf46582ce5a64991983dfc0759db0af5 | [
"MIT"
] | null | null | null | 40.459103 | 156 | 0.548128 | true | 3,115 | Qwen/Qwen-72B | 1. YES
2. YES | 0.872347 | 0.875787 | 0.76399 | __label__eng_Latn | 0.9835 | 0.613338 |
```python
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from chmp.ds import mpl_set, get_color_cycle
```
```python
# helper for gradient checking
def approximate_gradient(x, func, eps=1e-5):
res = np.zeros(x.size)
for i in range(x.size):
d = np.zeros(x.size)
d[i]... | 843683d65d333db52d4729f355d7b4bee4f41cbf | 56,429 | ipynb | Jupyter Notebook | BuildingBlocks/Bishop_Notes_03.ipynb | chmp/misc-exp | 2edc2ed598eb59f4ccb426e7a5c1a23343a6974b | [
"MIT"
] | 6 | 2017-10-31T20:54:37.000Z | 2020-10-23T19:03:00.000Z | BuildingBlocks/Bishop_Notes_03.ipynb | chmp/misc-exp | 2edc2ed598eb59f4ccb426e7a5c1a23343a6974b | [
"MIT"
] | 7 | 2020-03-24T16:14:34.000Z | 2021-03-18T20:51:37.000Z | BuildingBlocks/Bishop_Notes_03.ipynb | chmp/misc-exp | 2edc2ed598eb59f4ccb426e7a5c1a23343a6974b | [
"MIT"
] | 1 | 2019-07-29T07:55:49.000Z | 2019-07-29T07:55:49.000Z | 131.536131 | 42,816 | 0.850945 | true | 2,864 | Qwen/Qwen-72B | 1. YES
2. YES | 0.849971 | 0.782662 | 0.665241 | __label__eng_Latn | 0.667948 | 0.383908 |
# Divorce rates and their relationship with Marriage rate and Median Age Marriage
```R
# load data and copy
library(rethinking)
options(mc.cores = parallel::detectCores())
data(WaffleDivorce)
d <- WaffleDivorce
# standardize variables
d$A <- scale( d$MedianAgeMarriage )
d$D <- scale( d$Divorce )
```
Loading requ... | 05af6c8344b00922449750019985506438594d3f | 412,536 | ipynb | Jupyter Notebook | The Many Variables & The Spurious Waffles.ipynb | GodEater8042/Statistical-Rethinking-Jupyter-R | ba305082b8fb24cefc43d02208de361e5adade3e | [
"MIT"
] | null | null | null | The Many Variables & The Spurious Waffles.ipynb | GodEater8042/Statistical-Rethinking-Jupyter-R | ba305082b8fb24cefc43d02208de361e5adade3e | [
"MIT"
] | null | null | null | The Many Variables & The Spurious Waffles.ipynb | GodEater8042/Statistical-Rethinking-Jupyter-R | ba305082b8fb24cefc43d02208de361e5adade3e | [
"MIT"
] | null | null | null | 138.203015 | 42,506 | 0.841883 | true | 14,332 | Qwen/Qwen-72B | 1. YES
2. YES | 0.863392 | 0.875787 | 0.756147 | __label__yue_Hant | 0.169327 | 0.595115 |
```python
import numpy as np
from scipy.integrate import odeint
import numpy as np
from sympy import symbols,sqrt,sech,Rational,lambdify,Matrix,exp,cosh,cse,simplify,cos,sin
from sympy.vector import CoordSysCartesian
#from theano.scalar.basic_sympy import SymPyCCode
#from theano import function
#from theano.scalar im... | ac41d824f52063ad95a2d311f5556ef6040cfdd2 | 39,338 | ipynb | Jupyter Notebook | src/ionotomo/notebooks/FermatPrincipleTricubic.ipynb | Joshuaalbert/IonoTomo | 9f50fbac698d43a824dd098d76dce93504c7b879 | [
"Apache-2.0"
] | 7 | 2017-06-22T08:47:07.000Z | 2021-07-01T12:33:02.000Z | src/ionotomo/notebooks/FermatPrincipleTricubic.ipynb | Joshuaalbert/IonoTomo | 9f50fbac698d43a824dd098d76dce93504c7b879 | [
"Apache-2.0"
] | 1 | 2019-04-03T15:21:19.000Z | 2019-04-03T15:48:31.000Z | src/ionotomo/notebooks/FermatPrincipleTricubic.ipynb | Joshuaalbert/IonoTomo | 9f50fbac698d43a824dd098d76dce93504c7b879 | [
"Apache-2.0"
] | 2 | 2020-03-01T16:20:00.000Z | 2020-07-07T15:09:02.000Z | 45.268124 | 193 | 0.482231 | true | 10,003 | Qwen/Qwen-72B | 1. YES
2. YES | 0.865224 | 0.596433 | 0.516048 | __label__eng_Latn | 0.160347 | 0.037282 |
## 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... | bf411cf464b00e112afa7362bad9eea035a3cfc4 | 631,034 | ipynb | Jupyter Notebook | 1_4_Feature_Vectors/3_1. HOG.ipynb | georgiagn/CVND_Exercises | 4de186c80d14ed7d1e61c6bc51098ad0d9b4c54b | [
"MIT"
] | 1 | 2020-11-16T20:18:21.000Z | 2020-11-16T20:18:21.000Z | 1_4_Feature_Vectors/3_1. HOG.ipynb | georgiagn/CVND_Exercises | 4de186c80d14ed7d1e61c6bc51098ad0d9b4c54b | [
"MIT"
] | null | null | null | 1_4_Feature_Vectors/3_1. HOG.ipynb | georgiagn/CVND_Exercises | 4de186c80d14ed7d1e61c6bc51098ad0d9b4c54b | [
"MIT"
] | null | null | null | 427.819661 | 300,427 | 0.916125 | true | 7,765 | Qwen/Qwen-72B | 1. YES
2. YES
| 0.752013 | 0.774583 | 0.582496 | __label__eng_Latn | 0.997521 | 0.191664 |
```python
%matplotlib inline
import numpy as np
import pylab as plt
import pandas as pd
from sklearn import svm
from sklearn.metrics import classification_report,confusion_matrix,accuracy_score
```
```python
np.random.seed(0)
X = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]]
Y = [0] * 20 + [... | 2167cc5717d860c4929ed61e24aa11f32ff13514 | 599,888 | ipynb | Jupyter Notebook | day1/4-sklearn.ipynb | vafaei-ar/IUMS-workshops | 4d68d069e311d00a3283602536841ab548f57ce1 | [
"MIT"
] | null | null | null | day1/4-sklearn.ipynb | vafaei-ar/IUMS-workshops | 4d68d069e311d00a3283602536841ab548f57ce1 | [
"MIT"
] | null | null | null | day1/4-sklearn.ipynb | vafaei-ar/IUMS-workshops | 4d68d069e311d00a3283602536841ab548f57ce1 | [
"MIT"
] | null | null | null | 630.134454 | 131,120 | 0.946192 | true | 3,649 | Qwen/Qwen-72B | 1. YES
2. YES | 0.907312 | 0.877477 | 0.796145 | __label__eng_Latn | 0.603149 | 0.688045 |
# Realization of Recursive Filters
*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).*
## Cascaded Structures
The realization of rec... | e1f99e9146c6f45091bbc04cd4385dea3ca32aca | 290,256 | ipynb | Jupyter Notebook | recursive_filters/cascaded_structures.ipynb | ZeroCommits/digital-signal-processing-lecture | e1e65432a5617a309ec02327a14962e37a0f7ec5 | [
"MIT"
] | 630 | 2016-01-05T17:11:43.000Z | 2022-03-30T07:48:27.000Z | recursive_filters/cascaded_structures.ipynb | alirezaopmc/digital-signal-processing-lecture | e1e65432a5617a309ec02327a14962e37a0f7ec5 | [
"MIT"
] | 12 | 2016-11-07T15:49:55.000Z | 2022-03-10T13:05:50.000Z | recursive_filters/cascaded_structures.ipynb | alirezaopmc/digital-signal-processing-lecture | e1e65432a5617a309ec02327a14962e37a0f7ec5 | [
"MIT"
] | 172 | 2015-12-26T21:05:40.000Z | 2022-03-10T23:13:30.000Z | 59.995039 | 24,122 | 0.594286 | true | 2,022 | Qwen/Qwen-72B | 1. YES
2. YES | 0.793106 | 0.879147 | 0.697257 | __label__eng_Latn | 0.976409 | 0.458292 |
```python
import numpy as np
import sympy as sy
import control.matlab as cm
```
```python
s,z = sy.symbols('s,z', real=False)
h,t = sy.symbols('h,t', real=True, positive=True)
```
```python
G = (s+1)/(s+2)
Ya = sy.apart(G/s**2)
```
```python
ya = sy.inverse_laplace_transform(Ya, s, t)
print sy.pretty_print(ya)
pr... | 4d6cbf22ea907aef6be191d827e2414a09cfbfd6 | 2,715 | ipynb | Jupyter Notebook | approximating-cont-controller/notebooks/L8-spring16-ramp-invariance.ipynb | kjartan-at-tec/mr2007-computerized-control | 16e35f5007f53870eaf344eea1165507505ab4aa | [
"MIT"
] | 2 | 2020-11-07T05:20:37.000Z | 2020-12-22T09:46:13.000Z | approximating-cont-controller/notebooks/L8-spring16-ramp-invariance.ipynb | alfkjartan/control-computarizado | 5b9a3ae67602d131adf0b306f3ffce7a4914bf8e | [
"MIT"
] | 4 | 2020-06-12T20:44:41.000Z | 2020-06-12T20:49:00.000Z | approximating-cont-controller/notebooks/L8-spring16-ramp-invariance.ipynb | alfkjartan/control-computarizado | 5b9a3ae67602d131adf0b306f3ffce7a4914bf8e | [
"MIT"
] | 1 | 2019-09-25T20:02:23.000Z | 2019-09-25T20:02:23.000Z | 18.986014 | 88 | 0.448987 | true | 397 | Qwen/Qwen-72B | 1. YES
2. YES | 0.875787 | 0.839734 | 0.735428 | __label__kor_Hang | 0.112804 | 0.546978 |
```python
import math
import numpy as np;
import matplotlib.pyplot as plt
```
```python
def inf_n(z, a):
return 1-(9*a)/(8*z)+math.pow(a,3)/(2*math.pow(z,3))-math.pow(a,5)/(8*math.pow(z,5))
def inf_t(z, a):
return 1-(9*a)/(16*z)+2*math.pow(a,3)/(16*math.pow(z,3))-math.pow(a,5)/(16*math.pow(z,5))
def channel... | b50bb0103790cfcc91d16c1edf03a0ddba511bd8 | 188,889 | ipynb | Jupyter Notebook | tools/notebooks/channel_mob.ipynb | jackieyao0114/FHDeX | 63b455d48d1845a66c295cb35d1b890e34a07d8d | [
"BSD-3-Clause-LBNL"
] | 3 | 2018-06-25T13:23:13.000Z | 2021-12-28T21:31:54.000Z | tools/notebooks/channel_mob.ipynb | jackieyao0114/FHDeX | 63b455d48d1845a66c295cb35d1b890e34a07d8d | [
"BSD-3-Clause-LBNL"
] | 44 | 2019-09-24T15:31:52.000Z | 2022-02-24T21:05:21.000Z | tools/notebooks/channel_mob.ipynb | jackieyao0114/FHDeX | 63b455d48d1845a66c295cb35d1b890e34a07d8d | [
"BSD-3-Clause-LBNL"
] | 7 | 2019-10-01T15:47:08.000Z | 2022-02-22T23:04:58.000Z | 282.345291 | 58,224 | 0.915659 | true | 5,299 | Qwen/Qwen-72B | 1. YES
2. YES | 0.891811 | 0.665411 | 0.59342 | __label__eng_Latn | 0.060553 | 0.217045 |
# Linear and Quadratic Discriminant Analysis
## Linear Discriminant Analysis
### Classifying with Bayes' Theorem
In a previous chapter we discussed logistic regression for the case of two response classes (e.g. 0 and 1). It models the conditional probability $\Pr(Y=k|X=x)$ directly through the use of the Sigmoid fun... | 016ac2300d7be87be83b1a9f0c0205b1f722b783 | 263,162 | ipynb | Jupyter Notebook | 0208_LDA-QDA.ipynb | bMzi/ML_in_Finance | 9b92e9bdf371d22b279d76556364f4645b080803 | [
"MIT"
] | 8 | 2018-02-16T10:33:13.000Z | 2022-02-19T13:56:57.000Z | 0208_LDA-QDA.ipynb | bMzi/ML_in_Finance | 9b92e9bdf371d22b279d76556364f4645b080803 | [
"MIT"
] | null | null | null | 0208_LDA-QDA.ipynb | bMzi/ML_in_Finance | 9b92e9bdf371d22b279d76556364f4645b080803 | [
"MIT"
] | 13 | 2018-02-16T09:11:01.000Z | 2021-12-22T08:19:46.000Z | 185.194933 | 42,748 | 0.884432 | true | 11,579 | Qwen/Qwen-72B | 1. YES
2. YES | 0.891811 | 0.901921 | 0.804343 | __label__eng_Latn | 0.987863 | 0.707091 |
# Bayesian Inference in the Poisson Generalized Linear Model
**References:**
- Chapter 16 of BDA3 contains background material on generalized linear models.
- Chapter 7.1 of BDA3 introduces notation for model evaluation based on predictive log likelihoods.
## The Poisson GLM
The Poisson distribution is a common mode... | 06b87bb64d02a52f1f3e97a2693332d70fc5a8e3 | 290,443 | ipynb | Jupyter Notebook | notebooks/jjl-poisson-glm.ipynb | jilanglois-su/cobs10-dengai | 101d3434db6330e9794b2e266b02c93793abfb82 | [
"MIT"
] | null | null | null | notebooks/jjl-poisson-glm.ipynb | jilanglois-su/cobs10-dengai | 101d3434db6330e9794b2e266b02c93793abfb82 | [
"MIT"
] | null | null | null | notebooks/jjl-poisson-glm.ipynb | jilanglois-su/cobs10-dengai | 101d3434db6330e9794b2e266b02c93793abfb82 | [
"MIT"
] | null | null | null | 627.306695 | 78,076 | 0.945504 | true | 2,390 | Qwen/Qwen-72B | 1. YES
2. YES | 0.924142 | 0.782662 | 0.723291 | __label__eng_Latn | 0.5492 | 0.518779 |
<link rel="stylesheet" href="../../styles/theme_style.css">
<!--link rel="stylesheet" href="../../styles/header_style.css"-->
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">
<table width="100%">
<tr>
<td id="image_td" width="15%" class="head... | f2f5a57d09aaf652ef3d5ac653e68dd8a41fcc15 | 902,942 | ipynb | Jupyter Notebook | biosignalsnotebooks_notebooks/unpublished_notebooks/Pre-Process/temporal_statistical_parameters.ipynb | csavur/biosignalsnotebooks | c99596741a854c58bdefb429906023ac48ddc3b7 | [
"MIT"
] | 1 | 2020-06-26T05:05:11.000Z | 2020-06-26T05:05:11.000Z | biosignalsnotebooks_notebooks/unpublished_notebooks/Pre-Process/temporal_statistical_parameters.ipynb | csavur/biosignalsnotebooks | c99596741a854c58bdefb429906023ac48ddc3b7 | [
"MIT"
] | null | null | null | biosignalsnotebooks_notebooks/unpublished_notebooks/Pre-Process/temporal_statistical_parameters.ipynb | csavur/biosignalsnotebooks | c99596741a854c58bdefb429906023ac48ddc3b7 | [
"MIT"
] | null | null | null | 279.462086 | 136,349 | 0.861279 | true | 12,626 | Qwen/Qwen-72B | 1. YES
2. YES | 0.795658 | 0.810479 | 0.644864 | __label__eng_Latn | 0.837905 | 0.336566 |
# Five-Link biped walking loop problem: interactive demonstration
Hello and welcome. This is a Jupyter Notebook, a kind of document that can alternate between static content, like text and images, and executable cells of code.
This document ilustrates the Five-link biped walking loop test case of the paper: "Collocat... | a1d91564870d9ee35478d3d68f1b4e154dc9d2e5 | 64,663 | ipynb | Jupyter Notebook | Five-Link-Biped-demo.ipynb | AunSiro/Second-Order-Schemes | ef7ac9a6755e166d81b83f584f82055d38265087 | [
"MIT"
] | null | null | null | Five-Link-Biped-demo.ipynb | AunSiro/Second-Order-Schemes | ef7ac9a6755e166d81b83f584f82055d38265087 | [
"MIT"
] | null | null | null | Five-Link-Biped-demo.ipynb | AunSiro/Second-Order-Schemes | ef7ac9a6755e166d81b83f584f82055d38265087 | [
"MIT"
] | null | null | null | 32.202689 | 328 | 0.52486 | true | 14,056 | Qwen/Qwen-72B | 1. YES
2. YES | 0.928409 | 0.817574 | 0.759043 | __label__eng_Latn | 0.343656 | 0.601844 |
## Lecture topic 5:
## Ordinary and partial differential equations
```python
from lecture_utils import *
```
_This is the first part of the lecture material and should enable you to solve exercises 5.1, 5.2 and 5.3._
#### What are differential equations?
A differential equation is an equation that contains next ... | ad6a20f90f0dfefc4ca5d7d979c64f2940d0d77d | 163,370 | ipynb | Jupyter Notebook | Lecture 5 - Differential equations/lecture_topic5_differential_eq_part1.ipynb | hlappal/comp-phys | 8d78a459bc5849ddf5c6c21d484503136bccccbd | [
"MIT"
] | null | null | null | Lecture 5 - Differential equations/lecture_topic5_differential_eq_part1.ipynb | hlappal/comp-phys | 8d78a459bc5849ddf5c6c21d484503136bccccbd | [
"MIT"
] | null | null | null | Lecture 5 - Differential equations/lecture_topic5_differential_eq_part1.ipynb | hlappal/comp-phys | 8d78a459bc5849ddf5c6c21d484503136bccccbd | [
"MIT"
] | null | null | null | 95.426402 | 28,424 | 0.831083 | true | 8,993 | Qwen/Qwen-72B | 1. YES
2. YES | 0.819893 | 0.7773 | 0.637303 | __label__eng_Latn | 0.987227 | 0.318999 |
```python
from sympy import *
from sympy.abc import r,x,y,z
from scipy.integrate import quad, nquad
import matplotlib.pyplot as plt
%matplotlib inline
init_printing()
```
# Energy of the Hydrogen Atom
The variational principle states a trial wavefunction will have an energy greater than or equal to the ground state en... | 2166ce9847e281a2a483c8efa4b2e05cb3914019 | 32,621 | ipynb | Jupyter Notebook | Variational/Variational_Hydrogen.ipynb | QMCPACK/qmc_algorithms | 015fd1973e94f98662149418adc6b06dcd78946d | [
"MIT"
] | 3 | 2018-02-06T06:15:19.000Z | 2019-11-26T23:54:53.000Z | Variational/Variational_Hydrogen.ipynb | chrinide/qmc_algorithms | 015fd1973e94f98662149418adc6b06dcd78946d | [
"MIT"
] | 1 | 2017-03-23T17:17:04.000Z | 2017-03-23T17:17:04.000Z | Variational/Variational_Hydrogen.ipynb | chrinide/qmc_algorithms | 015fd1973e94f98662149418adc6b06dcd78946d | [
"MIT"
] | 4 | 2016-06-30T21:29:32.000Z | 2019-10-22T16:10:03.000Z | 78.604819 | 12,516 | 0.824346 | true | 982 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91611 | 0.874077 | 0.800751 | __label__eng_Latn | 0.971514 | 0.698744 |
## Variational Inference: Ising Model
This notebook focuses on Variational Inference (VI) for the Ising model in application to binary image de-noising. The Ising model is an example of a Markov Random Field (MRF) and it originated from statistical physics. The Ising model assumes that we have a grid of nodes, where e... | de844bc9d4ca7bf7f4e623f57b914272e009871d | 503,997 | ipynb | Jupyter Notebook | chp02/mean_field_mrf.ipynb | gerket/experiments_with_python | 5dd6dbd69deaaa318bfa7d2c3c9f7fae6220c460 | [
"MIT"
] | 382 | 2017-08-22T13:14:54.000Z | 2022-03-28T17:56:59.000Z | chp02/mean_field_mrf.ipynb | gerket/experiments_with_python | 5dd6dbd69deaaa318bfa7d2c3c9f7fae6220c460 | [
"MIT"
] | 4 | 2017-07-31T00:52:36.000Z | 2018-10-01T14:29:51.000Z | chp02/mean_field_mrf.ipynb | gerket/experiments_with_python | 5dd6dbd69deaaa318bfa7d2c3c9f7fae6220c460 | [
"MIT"
] | 280 | 2017-08-23T08:08:32.000Z | 2022-03-09T07:04:01.000Z | 936.797398 | 380,880 | 0.942837 | true | 2,382 | Qwen/Qwen-72B | 1. YES
2. YES | 0.935347 | 0.877477 | 0.820745 | __label__eng_Latn | 0.95954 | 0.745198 |
# !!! D . R . A . F . T !!!
# Lightness
[Lightness](http://en.wikipedia.org/wiki/Lightness) is defined as the brightness of an area judged relative to the brightness of a similarly illuminated area that appears to be white or highly transmitting. <a name="back_reference_1"></a><a href="#reference_1">[1]</a>
[Colour]... | 17f2f0656a1b237f979e611f42635ae50ee86136 | 433,163 | ipynb | Jupyter Notebook | notebooks/colorimetry/lightness.ipynb | Legendin/colour-notebooks | 357b64e60e24468c88a7d6789003a6283c809c01 | [
"BSD-3-Clause"
] | null | null | null | notebooks/colorimetry/lightness.ipynb | Legendin/colour-notebooks | 357b64e60e24468c88a7d6789003a6283c809c01 | [
"BSD-3-Clause"
] | null | null | null | notebooks/colorimetry/lightness.ipynb | Legendin/colour-notebooks | 357b64e60e24468c88a7d6789003a6283c809c01 | [
"BSD-3-Clause"
] | null | null | null | 657.30349 | 68,126 | 0.939674 | true | 2,345 | Qwen/Qwen-72B | 1. YES
2. YES | 0.884039 | 0.812867 | 0.718607 | __label__eng_Latn | 0.620185 | 0.507896 |
```python
# In mathematics, the exponential integral Ei is a special function on the complex plane.
# It is defined as one particular definite integral of the ratio between an exponential function and its argument.
from sympy import *
from sympy import E
from sympy.abc import x,omega,u,m,g
f = lambda x: E**(E**x)
exp... | ce1fa9b57a77383a7d08a37e2e71d1394d2728e9 | 10,159 | ipynb | Jupyter Notebook | Personal_Projects/Exponential_Integrals/Exponential Integrals Clocktested.ipynb | NSC9/Sample_of_Work | 8f8160fbf0aa4fd514d4a5046668a194997aade6 | [
"MIT"
] | null | null | null | Personal_Projects/Exponential_Integrals/Exponential Integrals Clocktested.ipynb | NSC9/Sample_of_Work | 8f8160fbf0aa4fd514d4a5046668a194997aade6 | [
"MIT"
] | null | null | null | Personal_Projects/Exponential_Integrals/Exponential Integrals Clocktested.ipynb | NSC9/Sample_of_Work | 8f8160fbf0aa4fd514d4a5046668a194997aade6 | [
"MIT"
] | null | null | null | 28.94302 | 188 | 0.423959 | true | 1,207 | Qwen/Qwen-72B | 1. YES
2. YES | 0.90599 | 0.731059 | 0.662332 | __label__eng_Latn | 0.503814 | 0.377149 |
# Optimizer tweaks
```python
%load_ext autoreload
%autoreload 2
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
%matplotlib inline
```
```python
#export
from exp.nb_08 import *
```
```python
listify??
```
## Imagenette data
We grab the data from the p... | 9abe96c32a4ceeacae0a6e745e069d5c4398a57e | 406,684 | ipynb | Jupyter Notebook | nbs/dl2/09_optimizers_sz_20191009.ipynb | stuartzong/course-v3 | 496c8d06d401e53f5cd517e3805a85befa6795cc | [
"Apache-2.0"
] | null | null | null | nbs/dl2/09_optimizers_sz_20191009.ipynb | stuartzong/course-v3 | 496c8d06d401e53f5cd517e3805a85befa6795cc | [
"Apache-2.0"
] | null | null | null | nbs/dl2/09_optimizers_sz_20191009.ipynb | stuartzong/course-v3 | 496c8d06d401e53f5cd517e3805a85befa6795cc | [
"Apache-2.0"
] | null | null | null | 276.467709 | 109,560 | 0.925554 | true | 6,828 | Qwen/Qwen-72B | 1. YES
2. YES | 0.760651 | 0.800692 | 0.609047 | __label__eng_Latn | 0.882892 | 0.25335 |
# Sampled Softmax
For classification and prediction problems a typical criterion function is cross-entropy with softmax. If the number of output classes is high the computation of this criterion and the corresponding gradients could be quite costly. Sampled Softmax is a heuristic to speed up training in these cases. (... | 23391f99543c70634a3636f95335a6f541764eb6 | 74,613 | ipynb | Jupyter Notebook | Tutorials/CNTK_207_Training_with_Sampled_Softmax.ipynb | mukehvier/CNTK | 0ee09cf771bda9d4912790e0fed7322e89d86d87 | [
"RSA-MD"
] | 1 | 2019-04-03T09:12:57.000Z | 2019-04-03T09:12:57.000Z | Tutorials/CNTK_207_Training_with_Sampled_Softmax.ipynb | zhuyawen/CNTK | 0ee09cf771bda9d4912790e0fed7322e89d86d87 | [
"RSA-MD"
] | null | null | null | Tutorials/CNTK_207_Training_with_Sampled_Softmax.ipynb | zhuyawen/CNTK | 0ee09cf771bda9d4912790e0fed7322e89d86d87 | [
"RSA-MD"
] | 1 | 2020-12-24T14:50:54.000Z | 2020-12-24T14:50:54.000Z | 117.500787 | 24,782 | 0.830123 | true | 5,078 | Qwen/Qwen-72B | 1. YES
2. YES | 0.877477 | 0.833325 | 0.731223 | __label__eng_Latn | 0.968693 | 0.537208 |
# Hopf Bifurcation: The Emergence of Limit-cycle Dynamics
*Cem Özen*, May 2017.
A *Hopf bifurcation* is a critical point in which a periodic orbit appears or disappears through a local change in the stability of a fixed point in a dynamical system as one of the system parameters is varied. Hopf bifurcations occur in ... | ea2d14f88f0858fe87c6488a202633885c6ef02c | 523,717 | ipynb | Jupyter Notebook | .ipynb_checkpoints/brusselator_hopf-checkpoint.ipynb | cemozen/pattern_formation_in_reaction-diffusion_systems | 7788c2dec71bcbe47758cabdc9816d99f88df589 | [
"MIT"
] | 1 | 2021-04-04T02:01:50.000Z | 2021-04-04T02:01:50.000Z | .ipynb_checkpoints/brusselator_hopf-checkpoint.ipynb | cemozen/pattern_formation_in_reaction-diffusion_systems | 7788c2dec71bcbe47758cabdc9816d99f88df589 | [
"MIT"
] | null | null | null | .ipynb_checkpoints/brusselator_hopf-checkpoint.ipynb | cemozen/pattern_formation_in_reaction-diffusion_systems | 7788c2dec71bcbe47758cabdc9816d99f88df589 | [
"MIT"
] | null | null | null | 107.649949 | 100,204 | 0.851072 | true | 4,955 | Qwen/Qwen-72B | 1. YES
2. YES | 0.939025 | 0.891811 | 0.837433 | __label__eng_Latn | 0.983167 | 0.78397 |
# Immersed Interface Method
---
### Author: Marin Lauber
```python
import numpy as np
import matplotlib.pyplot as plt
import NSsolver as ns
try:
plt.style.use("jupyter")
except OSerror:
print("Using default ploting style")
```
The Immersed Interface Method (IIM) was initially developed for elliptical equati... | 0436307b69355718549b74a77c237e7bd10d4a2f | 49,423 | ipynb | Jupyter Notebook | 1D-Piston/Immersed-Interface-Method.ipynb | marinlauber/FlexibleSheets | 487b035a5aea4a0f4cf5aa49c3eab5cb238aa1f7 | [
"MIT"
] | null | null | null | 1D-Piston/Immersed-Interface-Method.ipynb | marinlauber/FlexibleSheets | 487b035a5aea4a0f4cf5aa49c3eab5cb238aa1f7 | [
"MIT"
] | null | null | null | 1D-Piston/Immersed-Interface-Method.ipynb | marinlauber/FlexibleSheets | 487b035a5aea4a0f4cf5aa49c3eab5cb238aa1f7 | [
"MIT"
] | 2 | 2020-12-18T18:57:16.000Z | 2022-03-04T06:58:09.000Z | 115.205128 | 21,708 | 0.832689 | true | 3,423 | Qwen/Qwen-72B | 1. YES
2. YES | 0.877477 | 0.810479 | 0.711176 | __label__eng_Latn | 0.871637 | 0.490633 |
# Linear programming with scipy
See https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linprog.html
```python
import scipy.optimize
```
Problem examples:
- http://people.brunel.ac.uk/~mastjjb/jeb/or/morelp.html
## Scipy's syntax
Example for a problem of 2 dimensions:
$$
\begin{align}
\min_{x... | e2b4ae6daf557b63ee3754f935363337dfe078c6 | 6,101 | ipynb | Jupyter Notebook | nb_dev_python/python_scipy_linear_programming_en.ipynb | jdhp-docs/python-notebooks | 91a97ea5cf374337efa7409e4992ea3f26b99179 | [
"MIT"
] | 3 | 2017-05-03T12:23:36.000Z | 2020-10-26T17:30:56.000Z | nb_dev_python/python_scipy_linear_programming_en.ipynb | jdhp-docs/python-notebooks | 91a97ea5cf374337efa7409e4992ea3f26b99179 | [
"MIT"
] | null | null | null | nb_dev_python/python_scipy_linear_programming_en.ipynb | jdhp-docs/python-notebooks | 91a97ea5cf374337efa7409e4992ea3f26b99179 | [
"MIT"
] | 1 | 2020-10-26T17:30:57.000Z | 2020-10-26T17:30:57.000Z | 26.876652 | 185 | 0.496968 | true | 1,143 | Qwen/Qwen-72B | 1. YES
2. YES | 0.958538 | 0.92079 | 0.882612 | __label__eng_Latn | 0.881216 | 0.888936 |
## Exercise 10.1 (search)
We want to find the largest and smallest values in a long list of numbers. Implement
two algorithms, based on:
1. Iterating over the list entries; and
1. First applying a built-in sort operation to the list.
Encapsulate each algorithm in a function. To create lists of numbers for testing u... | 67328aaa748c65e4efad83f2d5915788c201d82b | 13,697 | ipynb | Jupyter Notebook | Assignment/10 Exercises.ipynb | reddyprasade/PYTHON-BASIC-FOR-ALL | 4fa4bf850f065e9ac1cea0365b93257e1f04e2cb | [
"MIT"
] | 21 | 2019-06-28T05:11:17.000Z | 2022-03-16T02:02:28.000Z | Assignment/10 Exercises.ipynb | chandhukogila/Python-Basic-For-All-3.x | f4105833759a271fa0777f3d6fb96db32bbfaaa4 | [
"MIT"
] | 2 | 2021-12-28T14:15:58.000Z | 2021-12-28T14:16:02.000Z | Assignment/10 Exercises.ipynb | chandhukogila/Python-Basic-For-All-3.x | f4105833759a271fa0777f3d6fb96db32bbfaaa4 | [
"MIT"
] | 18 | 2019-07-07T03:20:33.000Z | 2021-05-08T10:44:18.000Z | 26.089524 | 249 | 0.544645 | true | 1,746 | Qwen/Qwen-72B | 1. YES
2. YES | 0.890294 | 0.931463 | 0.829276 | __label__eng_Latn | 0.978028 | 0.765018 |
```python
import sympy as sm
sm.init_printing()
from pchem import solve
```
```python
P, V, n, R, T = sm.symbols('P V n R T', positive=True)
subs = dict(
P=2,
V=0.1,
R=0.083145,
T=275,
n=1,
)
gas_law = P*V - n * R *T
n1 = solve(gas_law, n, subs)
```
```python
R_J = 8.3145
n1 * 5/2*R_J*(550-275)
```
```python
sm.d... | 9fde25939643d2ee07869309edfe96b4abbc7ebc | 3,712 | ipynb | Jupyter Notebook | notebooks/test-2.ipynb | ryanpdwyer/pchem | ad097d7fce07669f4ad269e895e2185fa51ac2d2 | [
"MIT"
] | null | null | null | notebooks/test-2.ipynb | ryanpdwyer/pchem | ad097d7fce07669f4ad269e895e2185fa51ac2d2 | [
"MIT"
] | null | null | null | notebooks/test-2.ipynb | ryanpdwyer/pchem | ad097d7fce07669f4ad269e895e2185fa51ac2d2 | [
"MIT"
] | null | null | null | 32 | 956 | 0.68319 | true | 156 | Qwen/Qwen-72B | 1. YES
2. YES | 0.944177 | 0.705785 | 0.666386 | __label__eng_Latn | 0.280023 | 0.386569 |
<p align="center">
</p>
## Data Analytics
### Basic Bivariate Statistics in Python
#### Michael Pyrcz, Associate Professor, University of Texas at Austin
##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https... | 358e22180cc63b1e83fd9f8ed52acfc04534dcbf | 39,135 | ipynb | Jupyter Notebook | PythonDataBasics_Bivariate_Statistics.ipynb | caf3676/PythonNumericalDemos | 206a3d876f79e137af88b85ba98aff171e8d8e06 | [
"MIT"
] | 403 | 2017-10-15T02:07:38.000Z | 2022-03-30T15:27:14.000Z | PythonDataBasics_Bivariate_Statistics.ipynb | caf3676/PythonNumericalDemos | 206a3d876f79e137af88b85ba98aff171e8d8e06 | [
"MIT"
] | 4 | 2019-08-21T10:35:09.000Z | 2021-02-04T04:57:13.000Z | PythonDataBasics_Bivariate_Statistics.ipynb | caf3676/PythonNumericalDemos | 206a3d876f79e137af88b85ba98aff171e8d8e06 | [
"MIT"
] | 276 | 2018-06-27T11:20:30.000Z | 2022-03-25T16:04:24.000Z | 79.542683 | 20,572 | 0.779967 | true | 3,331 | Qwen/Qwen-72B | 1. YES
2. YES | 0.810479 | 0.651355 | 0.527909 | __label__eng_Latn | 0.922235 | 0.06484 |
# ***Introduction to Radar Using Python and MATLAB***
## Andy Harrison - Copyright (C) 2019 Artech House
<br/>
# Stratified Sphere Radar Cross Section
***
Referring to Section 7.4.1.5, Mie gives the exact solution for scattering from a sphere. The solution is composed of vector wave functions defined in a spherical ... | 1335724216572f2ee029c71cbc56082a9eda8020 | 89,561 | ipynb | Jupyter Notebook | jupyter/Chapter07/stratified_sphere.ipynb | mberkanbicer/software | 89f8004f567129216b92c156bbed658a9c03745a | [
"Apache-2.0"
] | null | null | null | jupyter/Chapter07/stratified_sphere.ipynb | mberkanbicer/software | 89f8004f567129216b92c156bbed658a9c03745a | [
"Apache-2.0"
] | null | null | null | jupyter/Chapter07/stratified_sphere.ipynb | mberkanbicer/software | 89f8004f567129216b92c156bbed658a9c03745a | [
"Apache-2.0"
] | null | null | null | 279.006231 | 80,436 | 0.92183 | true | 1,738 | Qwen/Qwen-72B | 1. YES
2. YES | 0.899121 | 0.763484 | 0.686465 | __label__eng_Latn | 0.939721 | 0.433218 |
<h3>Simulación matemática 2018 </h3>
<div style="background-color:#0099cc;">
<font color = white>
<ul>
<li>Lázaro Alonso </li>
<li>Email: `alonsosilva@iteso.mx, lazarus.alon@gmail.com`</li>
</ul>
</font>
</div>
<!--NAVIGATION-->
< [git GitHub tutorial 2](Clase2_GitTutorial2.ipynb) | [Guía](Clase0_GuiaSi... | 814a5699098c393beeb9f0ad6bf3b8d19d1973e6 | 68,756 | ipynb | Jupyter Notebook | Modulo1/Clase4_OptimizacionSympy.ipynb | douglasparism/SimulacionM2018 | 85953efb86c7ebf2f398474608dfda18cb4cf5b8 | [
"MIT"
] | null | null | null | Modulo1/Clase4_OptimizacionSympy.ipynb | douglasparism/SimulacionM2018 | 85953efb86c7ebf2f398474608dfda18cb4cf5b8 | [
"MIT"
] | null | null | null | Modulo1/Clase4_OptimizacionSympy.ipynb | douglasparism/SimulacionM2018 | 85953efb86c7ebf2f398474608dfda18cb4cf5b8 | [
"MIT"
] | null | null | null | 58.021941 | 14,756 | 0.780266 | true | 4,139 | Qwen/Qwen-72B | 1. YES
2. YES | 0.73412 | 0.835484 | 0.613345 | __label__spa_Latn | 0.925993 | 0.263336 |
## Classical Mechanics - Week 9
### Last Week:
- We saw how a potential can be used to analyze a system
- Gained experience with plotting and integrating in Python
### This Week:
- We will study harmonic oscillations using packages
- Further develope our analysis skills
- Gain more experience wtih sympy
```python... | 97e4ea8cb9864cd7b107c814f69f83269316fbc5 | 11,393 | ipynb | Jupyter Notebook | doc/AdminBackground/PHY321/CM_Jupyter_Notebooks/Student_Work/CM_Notebook9.ipynb | Shield94/Physics321 | 9875a3bf840b0fa164b865a3cb13073aff9094ca | [
"CC0-1.0"
] | 20 | 2020-01-09T17:41:16.000Z | 2022-03-09T00:48:58.000Z | doc/AdminBackground/PHY321/CM_Jupyter_Notebooks/Student_Work/CM_Notebook9.ipynb | Shield94/Physics321 | 9875a3bf840b0fa164b865a3cb13073aff9094ca | [
"CC0-1.0"
] | 6 | 2020-01-08T03:47:53.000Z | 2020-12-15T15:02:57.000Z | doc/AdminBackground/PHY321/CM_Jupyter_Notebooks/Student_Work/CM_Notebook9.ipynb | Shield94/Physics321 | 9875a3bf840b0fa164b865a3cb13073aff9094ca | [
"CC0-1.0"
] | 33 | 2020-01-10T20:40:55.000Z | 2022-02-11T20:28:41.000Z | 32.458689 | 430 | 0.581761 | true | 1,936 | Qwen/Qwen-72B | 1. YES
2. YES | 0.896251 | 0.935347 | 0.838306 | __label__eng_Latn | 0.994606 | 0.785998 |
# The Bayesian Bootstrap Is Not a Free Lunch
Some recent work has suggested that we can solve computationally difficult, multi-modal Bayesian posterior calculations with optimization and bootstrap sampling. There are many variations such methods; for shorthand I will simply refer to them collectively as Bayesian boot... | 0a64a7fb4ea8112a3269704bd478b0fbf3e29734 | 93,110 | ipynb | Jupyter Notebook | assets/post_assets/bayesian_bootstrap_v1.ipynb | rgiordan/rgiordan.github.io | 378cadac03ef1a9f7c5ac4007339004e61cef61e | [
"Apache-2.0"
] | null | null | null | assets/post_assets/bayesian_bootstrap_v1.ipynb | rgiordan/rgiordan.github.io | 378cadac03ef1a9f7c5ac4007339004e61cef61e | [
"Apache-2.0"
] | 2 | 2021-07-12T17:49:04.000Z | 2021-07-12T17:49:06.000Z | assets/post_assets/bayesian_bootstrap_v1.ipynb | rgiordan/rgiordan.github.io | 378cadac03ef1a9f7c5ac4007339004e61cef61e | [
"Apache-2.0"
] | null | null | null | 175.348399 | 40,264 | 0.880367 | true | 3,543 | Qwen/Qwen-72B | 1. YES
2. YES | 0.72487 | 0.822189 | 0.59598 | __label__eng_Latn | 0.965234 | 0.222992 |
# Variational Principle using Symbolic Mathematics in Python
## 1. Introduction
The variational principle tells us that we can use a trial wavefunction to solve the Schrodinger equation using the following theorem:
$${{\int {{\Psi ^*}\hat H{\rm{ }}\Psi } d\tau } \over {\int {{\Psi ^*}\Psi } d\tau }} \ge {E_0}$$
W... | cf7991e68f7a0266e06442385b5bc5c0ee7f2099 | 127,957 | ipynb | Jupyter Notebook | variational-principle.ipynb | sju-chem264-2019/10-3-19-lecture-deannapatti | 50e8c8cd80378db01e3b7876025a8eb0dc800e88 | [
"MIT"
] | null | null | null | variational-principle.ipynb | sju-chem264-2019/10-3-19-lecture-deannapatti | 50e8c8cd80378db01e3b7876025a8eb0dc800e88 | [
"MIT"
] | null | null | null | variational-principle.ipynb | sju-chem264-2019/10-3-19-lecture-deannapatti | 50e8c8cd80378db01e3b7876025a8eb0dc800e88 | [
"MIT"
] | null | null | null | 138.932682 | 23,784 | 0.801566 | true | 3,424 | Qwen/Qwen-72B | 1. YES
2. YES | 0.841826 | 0.815232 | 0.686284 | __label__eng_Latn | 0.848399 | 0.432798 |
# Two Degree-of-Freedom four well Potential
## Introduction and Development of the Problem
In this chapter we continue the study of Collins et al. {% cite collins2011 --file SNreac %} by considering the phase space structures that govern different reaction pathways and we then consider the influence of symmetry brea... | 3be0f23588f2b42cd4bd96afa1ccef0fce34d04d | 41,071 | ipynb | Jupyter Notebook | content/act2/four_well_morse/four_well_morse-jekyll.ipynb | champsproject/chem_react_dyn | 53ee9b30fbcfa4316eb08fd3ca69cba82cf7b598 | [
"CC-BY-4.0"
] | 11 | 2019-12-09T11:23:13.000Z | 2020-12-16T09:49:55.000Z | content/act2/four_well_morse/four_well_morse-jekyll.ipynb | champsproject/chem_react_dyn | 53ee9b30fbcfa4316eb08fd3ca69cba82cf7b598 | [
"CC-BY-4.0"
] | 40 | 2019-12-09T14:52:38.000Z | 2022-02-26T06:10:08.000Z | content/act2/four_well_morse/four_well_morse-jekyll.ipynb | champsproject/chem_react_dyn | 53ee9b30fbcfa4316eb08fd3ca69cba82cf7b598 | [
"CC-BY-4.0"
] | 3 | 2020-05-12T06:27:20.000Z | 2022-02-08T05:29:56.000Z | 41,071 | 41,071 | 0.702272 | true | 10,138 | Qwen/Qwen-72B | 1. YES
2. YES | 0.785309 | 0.839734 | 0.65945 | __label__eng_Latn | 0.997932 | 0.370455 |
```python
import sympy as sm
```
## Depth
```python
u1, u2, r, k, mu_b, d = sm.symbols("u1, u2, r, k, mu_b, d", real=True)
mu = sm.sqrt(1 - r ** 2)
ir = 1 - u1 * (1 - mu) - u2 * (1 - mu) ** 2
```
```python
f0 = sm.simplify(sm.integrate(2 * sm.pi * r * ir, (r, 0, 1)))
f0
```
```python
df = sm.pi * k ** 2 * ir.sub... | b583295b11034c9e8ff0e953408a237c21ff534a | 3,090 | ipynb | Jupyter Notebook | paper/figures/depth-and-duration.ipynb | exoplanet-dev/tess.world | 06ee11db96351d167451615a98b72ff84b5f7765 | [
"MIT"
] | 1 | 2020-09-08T10:43:48.000Z | 2020-09-08T10:43:48.000Z | paper/figures/depth-and-duration.ipynb | exoplanet-dev/tess.world | 06ee11db96351d167451615a98b72ff84b5f7765 | [
"MIT"
] | null | null | null | paper/figures/depth-and-duration.ipynb | exoplanet-dev/tess.world | 06ee11db96351d167451615a98b72ff84b5f7765 | [
"MIT"
] | 1 | 2020-09-08T10:43:59.000Z | 2020-09-08T10:43:59.000Z | 19.3125 | 87 | 0.460841 | true | 458 | Qwen/Qwen-72B | 1. YES
2. YES | 0.933431 | 0.7773 | 0.725556 | __label__eng_Latn | 0.061626 | 0.524041 |
# Solving Linear Systems
```python
import numpy as np
import matplotlib.pyplot as plt
import scipy.linalg as la
%matplotlib inline
```
## Linear Systems
A [linear system of equations](https://en.wikipedia.org/wiki/System_of_linear_equations) is a collection of linear equations
\begin{align}
a_{0,0}x_0 + a_{0,1}x_2... | fd3ba13adeae56b7679f0dc8f2b6671678a7c8a3 | 29,979 | ipynb | Jupyter Notebook | Python/3. Computational Sciences and Mathematics/Linear Algebra/Solving Systems of Linear Equations.ipynb | okara83/Becoming-a-Data-Scientist | f09a15f7f239b96b77a2f080c403b2f3e95c9650 | [
"MIT"
] | null | null | null | Python/3. Computational Sciences and Mathematics/Linear Algebra/Solving Systems of Linear Equations.ipynb | okara83/Becoming-a-Data-Scientist | f09a15f7f239b96b77a2f080c403b2f3e95c9650 | [
"MIT"
] | null | null | null | Python/3. Computational Sciences and Mathematics/Linear Algebra/Solving Systems of Linear Equations.ipynb | okara83/Becoming-a-Data-Scientist | f09a15f7f239b96b77a2f080c403b2f3e95c9650 | [
"MIT"
] | 2 | 2022-02-09T15:41:33.000Z | 2022-02-11T07:47:40.000Z | 29,979 | 29,979 | 0.590347 | true | 6,002 | Qwen/Qwen-72B | 1. YES
2. YES | 0.949669 | 0.875787 | 0.831708 | __label__eng_Latn | 0.732075 | 0.77067 |
```python
from sympy.physics.units import *
from sympy import *
# Rounding:
import decimal
from decimal import Decimal as DX
def iso_round(obj, pv, rounding=decimal.ROUND_HALF_EVEN):
import sympy
"""
Rounding acc. to DIN EN ISO 80000-1:2013-08
place value = Rundestellenwert
"""
assert pv in set... | f23bff096363ed09e1034c5e680ea7b13778db14 | 6,412 | ipynb | Jupyter Notebook | ipynb/TM_3/5_SL/Modal/2_DOFs/Beam/2dofs_cc.ipynb | kassbohm/tm-snippets | 5e0621ba2470116e54643b740d1b68b9f28bff12 | [
"MIT"
] | null | null | null | ipynb/TM_3/5_SL/Modal/2_DOFs/Beam/2dofs_cc.ipynb | kassbohm/tm-snippets | 5e0621ba2470116e54643b740d1b68b9f28bff12 | [
"MIT"
] | null | null | null | ipynb/TM_3/5_SL/Modal/2_DOFs/Beam/2dofs_cc.ipynb | kassbohm/tm-snippets | 5e0621ba2470116e54643b740d1b68b9f28bff12 | [
"MIT"
] | null | null | null | 31.586207 | 125 | 0.371179 | true | 1,301 | Qwen/Qwen-72B | 1. YES
2. YES | 0.877477 | 0.795658 | 0.698172 | __label__eng_Latn | 0.184437 | 0.460418 |
last edited by Claire Valva on May 13, 2019, with update and cleanup on June 24, 2019
# Test ENSO simulations and plotting
```python
# import packages
import numpy as np
from scipy.fftpack import fft, ifft, fftfreq, fftshift, ifftshift
import scipy.integrate as sciint
import pandas as pd
from math import pi
from sym... | c5b873032d3cf4e7f37ba6ca3915907e2a4b6d33 | 83,819 | ipynb | Jupyter Notebook | lin-assumption-2/enso_rep_test.ipynb | clairevalva/wavy-sims | 259c81078e6069777fdef455b0d806e4f8c0c262 | [
"MIT"
] | null | null | null | lin-assumption-2/enso_rep_test.ipynb | clairevalva/wavy-sims | 259c81078e6069777fdef455b0d806e4f8c0c262 | [
"MIT"
] | null | null | null | lin-assumption-2/enso_rep_test.ipynb | clairevalva/wavy-sims | 259c81078e6069777fdef455b0d806e4f8c0c262 | [
"MIT"
] | null | null | null | 133.469745 | 17,416 | 0.861117 | true | 2,843 | Qwen/Qwen-72B | 1. YES
2. YES | 0.903294 | 0.743168 | 0.671299 | __label__eng_Latn | 0.870875 | 0.397984 |
# Linear Gaussian filtering and smoothing
Provided are two examples of linear state-space models on which one can perform Bayesian filtering and smoothing in order to obtain
a posterior distribution over a latent state trajectory based on noisy observations.
In order to understand the theory behind these methods in de... | b1b6cd32644ba97e6f8d2a3aeb1962b4d1c7888e | 467,097 | ipynb | Jupyter Notebook | docs/source/tutorials/filtsmooth/linear_gaussian_filtering_smoothing.ipynb | christopheroates/probnum | 4ae63da307bd7279c3ce477ef68cbd0b8e30c73a | [
"MIT"
] | null | null | null | docs/source/tutorials/filtsmooth/linear_gaussian_filtering_smoothing.ipynb | christopheroates/probnum | 4ae63da307bd7279c3ce477ef68cbd0b8e30c73a | [
"MIT"
] | null | null | null | docs/source/tutorials/filtsmooth/linear_gaussian_filtering_smoothing.ipynb | christopheroates/probnum | 4ae63da307bd7279c3ce477ef68cbd0b8e30c73a | [
"MIT"
] | null | null | null | 76.111618 | 115,418 | 0.723017 | true | 4,174 | Qwen/Qwen-72B | 1. YES
2. YES | 0.817574 | 0.752013 | 0.614826 | __label__eng_Latn | 0.806303 | 0.266778 |
```python
# import stuff from sympy
from sympy import *
import random
import numpy as np
# Visualization
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
# x, y, z, t = symbols('x y z t')
# k, m, n = symbols('k m n', integer=True)
# f, g, h = symbols('f g h', cls=Function)
```
```python
# THIS IS WRONG... | 1562310cd6efe18859cfa7573dba4016e4c3694a | 129,998 | ipynb | Jupyter Notebook | Rcode/homework2/problema1.ipynb | ijpulidos/statlearn | fbe0964247d6466396d1e26fd63dae04be56a3ec | [
"MIT"
] | null | null | null | Rcode/homework2/problema1.ipynb | ijpulidos/statlearn | fbe0964247d6466396d1e26fd63dae04be56a3ec | [
"MIT"
] | null | null | null | Rcode/homework2/problema1.ipynb | ijpulidos/statlearn | fbe0964247d6466396d1e26fd63dae04be56a3ec | [
"MIT"
] | null | null | null | 212.068515 | 97,764 | 0.913129 | true | 2,554 | Qwen/Qwen-72B | 1. YES
2. YES | 0.740174 | 0.867036 | 0.641758 | __label__eng_Latn | 0.64116 | 0.329349 |
# Variational Principle using Symbolic Mathematics in Python
## 1. Introduction
The variational principle tells us that we can use a trial wavefunction to solve the Schrodinger equation using the following theorem:
$${{\int {{\Psi ^*}\hat H{\rm{ }}\Psi } d\tau } \over {\int {{\Psi ^*}\Psi } d\tau }} \ge {E_0}$$
W... | da82d32504175620ef6a58c97a68291e64c8dcf6 | 50,135 | ipynb | Jupyter Notebook | variational-principle.ipynb | sju-chem264-2019/10-3-19-lecture-justyn-cespedes | c1c6bc8a0affee8b90fd80cf197240cffcd0e293 | [
"MIT"
] | null | null | null | variational-principle.ipynb | sju-chem264-2019/10-3-19-lecture-justyn-cespedes | c1c6bc8a0affee8b90fd80cf197240cffcd0e293 | [
"MIT"
] | null | null | null | variational-principle.ipynb | sju-chem264-2019/10-3-19-lecture-justyn-cespedes | c1c6bc8a0affee8b90fd80cf197240cffcd0e293 | [
"MIT"
] | null | null | null | 93.014842 | 18,692 | 0.70757 | true | 2,012 | Qwen/Qwen-72B | 1. YES
2. YES | 0.803174 | 0.766294 | 0.615467 | __label__eng_Latn | 0.969066 | 0.268266 |
<a href="https://colab.research.google.com/github/martin-fabbri/colab-notebooks/blob/master/deeplearning.ai/nlp/c2_w4_assignment.ipynb" target="_parent"></a>
# Assignment 4: Word Embeddings
Welcome to the fourth (and last) programming assignment of Course 2!
In this assignment, you will practice how to compute wor... | 3f831722ed8f2fe178ffd2fe20afccea65e47b6a | 37,096 | ipynb | Jupyter Notebook | deeplearning.ai/nlp/c2_w4_assignment.ipynb | martin-fabbri/colab-notebooks | 03658a7772fbe71612e584bbc767009f78246b6b | [
"Apache-2.0"
] | 8 | 2020-01-18T18:39:49.000Z | 2022-02-17T19:32:26.000Z | deeplearning.ai/nlp/c2_w4_assignment.ipynb | martin-fabbri/colab-notebooks | 03658a7772fbe71612e584bbc767009f78246b6b | [
"Apache-2.0"
] | null | null | null | deeplearning.ai/nlp/c2_w4_assignment.ipynb | martin-fabbri/colab-notebooks | 03658a7772fbe71612e584bbc767009f78246b6b | [
"Apache-2.0"
] | 6 | 2020-01-18T18:40:02.000Z | 2020-09-27T09:26:38.000Z | 37,096 | 37,096 | 0.641875 | true | 7,439 | Qwen/Qwen-72B | 1. YES
2. YES | 0.760651 | 0.824462 | 0.627128 | __label__eng_Latn | 0.903634 | 0.295358 |
In this notebook there are presented examples of usage of shiroin, a python library for proving inequalities of multivariate polynomials.
At the beginning we need to load the packages.
```python
from sympy import *
from shiroin import *
from IPython.display import Latex
shiro.seed=1
shiro.display=lambda x:display(La... | d74b5062289dd255397c707ce31cdfb0cb3982db | 119,456 | ipynb | Jupyter Notebook | .ipynb_checkpoints/tutorial-checkpoint.ipynb | urojony/shiroin | 64157741dfd705d7e0be6b0be88d89a28e178f40 | [
"BSD-3-Clause"
] | 1 | 2020-12-13T19:58:17.000Z | 2020-12-13T19:58:17.000Z | .ipynb_checkpoints/tutorial-checkpoint.ipynb | urojony/shiroin | 64157741dfd705d7e0be6b0be88d89a28e178f40 | [
"BSD-3-Clause"
] | null | null | null | .ipynb_checkpoints/tutorial-checkpoint.ipynb | urojony/shiroin | 64157741dfd705d7e0be6b0be88d89a28e178f40 | [
"BSD-3-Clause"
] | 1 | 2020-12-13T19:55:52.000Z | 2020-12-13T19:55:52.000Z | 28.833213 | 2,397 | 0.441711 | true | 27,224 | Qwen/Qwen-72B | 1. YES
2. YES | 0.879147 | 0.803174 | 0.706108 | __label__eng_Latn | 0.429031 | 0.478856 |
<a href="https://colab.research.google.com/github/engdorm/semi-supervised-pytorch/blob/master/examples/notebooks/Deep Generative Model.ipynb" target="_parent"></a>
```python
# Imports
import torch
cuda = torch.cuda.is_available()
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import sys
sys.pa... | e8233c91c8f17ae21b5d8ca8fca8c09739bd0348 | 45,660 | ipynb | Jupyter Notebook | examples/notebooks/Deep Generative Model.ipynb | engdorm/semi-supervised-pytorch | b149e06aa413dd426886149930c8c265fd9cc746 | [
"MIT"
] | null | null | null | examples/notebooks/Deep Generative Model.ipynb | engdorm/semi-supervised-pytorch | b149e06aa413dd426886149930c8c265fd9cc746 | [
"MIT"
] | null | null | null | examples/notebooks/Deep Generative Model.ipynb | engdorm/semi-supervised-pytorch | b149e06aa413dd426886149930c8c265fd9cc746 | [
"MIT"
] | null | null | null | 82.717391 | 25,762 | 0.76671 | true | 3,799 | Qwen/Qwen-72B | 1. YES
2. YES | 0.839734 | 0.760651 | 0.638744 | __label__eng_Latn | 0.914469 | 0.322347 |
# Problem set 7: Solving the consumer problem with income risk
```python
import numpy as np
import scipy as sp
from scipy import linalg
from scipy import optimize
from scipy import interpolate
import sympy as sm
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
from matplotlib imp... | e72520150ec26ea907b1e8a3c5d534fa31bf7f79 | 742,975 | ipynb | Jupyter Notebook | PS7/problem_set_7.ipynb | mariusgruenewald/exercises-2019 | 9621af3054a2eb53efa5974640b581687853f820 | [
"MIT"
] | 4 | 2019-02-28T07:45:15.000Z | 2019-06-27T19:42:01.000Z | PS7/problem_set_7.ipynb | Teresepasquali/exercises-2020 | 9621af3054a2eb53efa5974640b581687853f820 | [
"MIT"
] | null | null | null | PS7/problem_set_7.ipynb | Teresepasquali/exercises-2020 | 9621af3054a2eb53efa5974640b581687853f820 | [
"MIT"
] | 19 | 2019-01-09T15:32:14.000Z | 2020-01-13T10:55:09.000Z | 450.834345 | 77,992 | 0.939466 | true | 7,556 | Qwen/Qwen-72B | 1. YES
2. YES | 0.868827 | 0.874077 | 0.759422 | __label__eng_Latn | 0.589017 | 0.602723 |
# Programming Exercise 5:
# Regularized Linear Regression and Bias vs Variance
## Introduction
In this exercise, you will implement regularized linear regression and use it to study models with different bias-variance properties. Before starting on the programming exercise, we strongly recommend watching the video le... | ebf0524bee04ea661656e046451be541cc4d4659 | 159,946 | ipynb | Jupyter Notebook | Supervised Learning/Learning Curve - Bias vs Variance/exercise5.ipynb | Jawwad-Fida/Machine-Learning-Algorithms | c326cd83850b771b979b8dfcbca6a54c508b035a | [
"MIT"
] | 1 | 2021-07-07T07:44:20.000Z | 2021-07-07T07:44:20.000Z | Supervised Learning/Learning Curve - Bias vs Variance/exercise5.ipynb | Jawwad-Fida/Machine-Learning-Algorithms | c326cd83850b771b979b8dfcbca6a54c508b035a | [
"MIT"
] | null | null | null | Supervised Learning/Learning Curve - Bias vs Variance/exercise5.ipynb | Jawwad-Fida/Machine-Learning-Algorithms | c326cd83850b771b979b8dfcbca6a54c508b035a | [
"MIT"
] | null | null | null | 131.859852 | 24,368 | 0.832193 | true | 9,132 | Qwen/Qwen-72B | 1. YES
2. YES | 0.766294 | 0.7773 | 0.59564 | __label__eng_Latn | 0.993049 | 0.222201 |
### Calculates price-equilibrium in the market for blockchain records, with and without the lightning network.
### Includes symbolic calculations and plots for specific parameter values.
```python
import numpy as np
import sympy
sympy.init_printing(use_unicode=True)
from sympy import symbols,simplify,diff,latex,Pi... | 4cd827a724855ff4c1ccbc952b45c08557d3ea2f | 752,733 | ipynb | Jupyter Notebook | old/market-equilibrium-symbolic-uniform.ipynb | erelsgl/bitcoin-simulations | 79bfa0930ab9ad17be59b9cad1ec6e7c3530aa3b | [
"MIT"
] | 1 | 2018-11-26T02:44:38.000Z | 2018-11-26T02:44:38.000Z | old/market-equilibrium-symbolic-uniform.ipynb | erelsgl/bitcoin-simulations | 79bfa0930ab9ad17be59b9cad1ec6e7c3530aa3b | [
"MIT"
] | null | null | null | old/market-equilibrium-symbolic-uniform.ipynb | erelsgl/bitcoin-simulations | 79bfa0930ab9ad17be59b9cad1ec6e7c3530aa3b | [
"MIT"
] | 3 | 2018-09-06T00:11:26.000Z | 2021-08-29T17:14:59.000Z | 246.555192 | 30,020 | 0.866124 | true | 6,910 | Qwen/Qwen-72B | 1. YES
2. YES | 0.874077 | 0.824462 | 0.720643 | __label__eng_Latn | 0.210219 | 0.512628 |
**Competing in different settings**
In this project we consider 2 firms who compete in the same duopolistic market. We will look at three possible competition forms, which are characterized by
**Cournot**
- Firms compete in quantities, and decide upon these independently and simultaneously
- Firms profit maximize ... | 2c48770b621b3f1e8036488beefff5d54fd35582 | 168,493 | ipynb | Jupyter Notebook | modelproject/modelproject_done4.0_rework3.ipynb | AskerNC/projects-2021-aristochats | cade4c02de648f4cd1220216598dc24b67bb8559 | [
"MIT"
] | null | null | null | modelproject/modelproject_done4.0_rework3.ipynb | AskerNC/projects-2021-aristochats | cade4c02de648f4cd1220216598dc24b67bb8559 | [
"MIT"
] | null | null | null | modelproject/modelproject_done4.0_rework3.ipynb | AskerNC/projects-2021-aristochats | cade4c02de648f4cd1220216598dc24b67bb8559 | [
"MIT"
] | null | null | null | 97.790482 | 45,640 | 0.835536 | true | 8,885 | Qwen/Qwen-72B | 1. YES
2. YES | 0.90053 | 0.782662 | 0.704811 | __label__eng_Latn | 0.993041 | 0.475843 |
# Computational Astrophysics
## Partial Differential Equations. 01 Generalities
---
## Eduard Larrañaga
Observatorio Astronómico Nacional\
Facultad de Ciencias\
Universidad Nacional de Colombia
---
### About this notebook
In this notebook we present some of the generalities about systems of Partial Differential Eq... | 4541eb6c92261fc6d04996d379dc087e7078c3e7 | 12,556 | ipynb | Jupyter Notebook | 13._PDE1/presentation/PDE01.ipynb | ashcat2005/ComputationalAstrophysics | edda507d0d0a433dfd674a2451d750cf6ad3f1b7 | [
"MIT"
] | 2 | 2020-09-23T02:49:10.000Z | 2021-08-21T06:04:39.000Z | 13._PDE1/presentation/PDE01.ipynb | ashcat2005/ComputationalAstrophysics | edda507d0d0a433dfd674a2451d750cf6ad3f1b7 | [
"MIT"
] | null | null | null | 13._PDE1/presentation/PDE01.ipynb | ashcat2005/ComputationalAstrophysics | edda507d0d0a433dfd674a2451d750cf6ad3f1b7 | [
"MIT"
] | 2 | 2020-12-05T14:06:28.000Z | 2022-01-25T04:51:58.000Z | 32.612987 | 137 | 0.572396 | true | 2,272 | Qwen/Qwen-72B | 1. YES
2. YES | 0.812867 | 0.890294 | 0.723691 | __label__eng_Latn | 0.996416 | 0.519709 |
# 13 Root Finding (Students)
An important tool in the computational tool box is to find roots of equations for which no closed form solutions exist:
We want to find the roots $x_0$ of
$$
f(x_0) = 0
$$
## Problem: Projectile range
The equations of motion for the projectile with linear air resistance (see *12 ODE ap... | 415590f53cfc37a605a05c2d822949957077bf8d | 13,387 | ipynb | Jupyter Notebook | 13_root_finding/13-Root-finding-students.ipynb | ASU-CompMethodsPhysics-PHY494/PHY494-resources-2020 | 20e08c20995eab567063b1845487e84c0e690e96 | [
"CC-BY-4.0"
] | null | null | null | 13_root_finding/13-Root-finding-students.ipynb | ASU-CompMethodsPhysics-PHY494/PHY494-resources-2020 | 20e08c20995eab567063b1845487e84c0e690e96 | [
"CC-BY-4.0"
] | null | null | null | 13_root_finding/13-Root-finding-students.ipynb | ASU-CompMethodsPhysics-PHY494/PHY494-resources-2020 | 20e08c20995eab567063b1845487e84c0e690e96 | [
"CC-BY-4.0"
] | null | null | null | 23.948122 | 301 | 0.488832 | true | 2,453 | Qwen/Qwen-72B | 1. YES
2. YES | 0.83762 | 0.879147 | 0.736391 | __label__eng_Latn | 0.81336 | 0.549215 |
---
author: Nathan Carter (ncarter@bentley.edu)
---
This answer assumes you have imported SymPy as follows.
```python
from sympy import * # load all math functions
init_printing( use_latex='mathjax' ) # use pretty math output
```
Let's assume we've defined a variable and created a formula, as cov... | 140d951638e08ebbdca3a83391b792743e81e51b | 3,615 | ipynb | Jupyter Notebook | database/tasks/How to substitute a value for a symbolic variable/Python, using SymPy.ipynb | nathancarter/how2data | 7d4f2838661f7ce98deb1b8081470cec5671b03a | [
"MIT"
] | null | null | null | database/tasks/How to substitute a value for a symbolic variable/Python, using SymPy.ipynb | nathancarter/how2data | 7d4f2838661f7ce98deb1b8081470cec5671b03a | [
"MIT"
] | null | null | null | database/tasks/How to substitute a value for a symbolic variable/Python, using SymPy.ipynb | nathancarter/how2data | 7d4f2838661f7ce98deb1b8081470cec5671b03a | [
"MIT"
] | 2 | 2021-07-18T19:01:29.000Z | 2022-03-29T06:47:11.000Z | 19.862637 | 99 | 0.487137 | true | 302 | Qwen/Qwen-72B | 1. YES
2. YES | 0.941654 | 0.872347 | 0.82145 | __label__eng_Latn | 0.974607 | 0.746835 |
## Multidimensional search with gradient-search methods
## The objective is to find a minimum of a multivariate function
Luca Magri (lm547@cam.ac.uk)
(With many thanks to Professor Gábor Csányi.)
Multivariate function = multi-variable function = function that depends on two variables at least
## Direct search for m... | 351d84f8e61267e9655b1203296fae268ade0a08 | 1,046,342 | ipynb | Jupyter Notebook | Lectures_3_4_Multidimensional_search_methods.ipynb | LukeMagher/3M1 | d3b6f06d8ecde209c405b412dcdcf1af3c9cfb98 | [
"BSD-2-Clause"
] | 2 | 2020-09-23T08:16:18.000Z | 2021-12-28T12:35:26.000Z | Lectures_3_4_Multidimensional_search_methods.ipynb | LukeMagher/3M1 | d3b6f06d8ecde209c405b412dcdcf1af3c9cfb98 | [
"BSD-2-Clause"
] | null | null | null | Lectures_3_4_Multidimensional_search_methods.ipynb | LukeMagher/3M1 | d3b6f06d8ecde209c405b412dcdcf1af3c9cfb98 | [
"BSD-2-Clause"
] | null | null | null | 1,238.274556 | 172,843 | 0.944039 | true | 13,548 | Qwen/Qwen-72B | 1. YES
2. YES | 0.859664 | 0.849971 | 0.730689 | __label__eng_Latn | 0.947995 | 0.535968 |
# PCA
```python
import pandas
# For lots of great things.
import numpy as np
# To make our plots.
import matplotlib.pyplot as plt
%matplotlib inline
# Because sympy and LaTeX make
# everything look wonderful!
from sympy import *
init_printing(use_latex=True)
from IPython.display import display
# We will use this to ... | 123434b29481ce681ec0f01dd3095167d06a8959 | 70,120 | ipynb | Jupyter Notebook | PCA.ipynb | holypolarpanda7/S19-team2-project | 09b51f07849e3288dfa4ba91cf5d8d13909e35e2 | [
"MIT"
] | null | null | null | PCA.ipynb | holypolarpanda7/S19-team2-project | 09b51f07849e3288dfa4ba91cf5d8d13909e35e2 | [
"MIT"
] | null | null | null | PCA.ipynb | holypolarpanda7/S19-team2-project | 09b51f07849e3288dfa4ba91cf5d8d13909e35e2 | [
"MIT"
] | null | null | null | 215.092025 | 39,956 | 0.904535 | true | 765 | Qwen/Qwen-72B | 1. YES
2. YES | 0.917303 | 0.901921 | 0.827334 | __label__eng_Latn | 0.946215 | 0.760508 |
<a href="https://colab.research.google.com/github/ValerieLangat/DS-Unit-1-Sprint-4-Statistical-Tests-and-Experiments/blob/master/Valerie_Intermediate_Linear_Algebra_Assignment.ipynb" target="_parent"></a>
# Statistics
## 1.1 Sales for the past week was the following amounts: [3505, 2400, 3027, 2798, 3700, 3250, 2689]... | 77adaa088bb62c1324398f7d26a643fb127a0185 | 72,717 | ipynb | Jupyter Notebook | Valerie_Intermediate_Linear_Algebra_Assignment.ipynb | ValerieLangat/DS-Unit-1-Sprint-4-Statistical-Tests-and-Experiments | 3392c2e3fcadef510f9b7cb7832e186af64fe881 | [
"MIT"
] | null | null | null | Valerie_Intermediate_Linear_Algebra_Assignment.ipynb | ValerieLangat/DS-Unit-1-Sprint-4-Statistical-Tests-and-Experiments | 3392c2e3fcadef510f9b7cb7832e186af64fe881 | [
"MIT"
] | null | null | null | Valerie_Intermediate_Linear_Algebra_Assignment.ipynb | ValerieLangat/DS-Unit-1-Sprint-4-Statistical-Tests-and-Experiments | 3392c2e3fcadef510f9b7cb7832e186af64fe881 | [
"MIT"
] | null | null | null | 51.101195 | 8,696 | 0.600864 | true | 5,985 | Qwen/Qwen-72B | 1. YES
2. YES | 0.861538 | 0.853913 | 0.735678 | __label__eng_Latn | 0.532441 | 0.547559 |
# 3. 신경망 (Neural Network)
* Perceptron은 복잡한 함수도 표현이 가능
> ex) 컴퓨터가 수행하는 복잡한 처리도 표현 가능, 하지만 가중치(weight)를 설정하는 작업 <br> (원하는 결과를 출력하도록 가중치 값을 적절히 정하는 작업)은 여전히 사람이 수동으로 조정. <br> 이전에는 AND, OR 게이트의 logic table을 보면서 적절한 가중치 값을 정함
* 신경망(neural network)는 위와 같은 문제를 해결해줌.<br> (**가중치의 매개변수의 적절한 값을 데이터로부터 자동으로 학습하는 능력이 신경망의 중요... | 6bacdf8f3b029c3daf626a1856c85163880a0899 | 54,104 | ipynb | Jupyter Notebook | deep_learning_from_scratch/ch3_neural_network.ipynb | Fintecuriosity11/TIL | 6a3e87f01b1010eafb2b9a3f12e67bfcc5274c45 | [
"MIT"
] | null | null | null | deep_learning_from_scratch/ch3_neural_network.ipynb | Fintecuriosity11/TIL | 6a3e87f01b1010eafb2b9a3f12e67bfcc5274c45 | [
"MIT"
] | 2 | 2020-03-22T12:15:43.000Z | 2020-03-22T12:29:54.000Z | deep_learning_from_scratch/ch3_neural_network.ipynb | Fintecuriosity11/TIL | 6a3e87f01b1010eafb2b9a3f12e67bfcc5274c45 | [
"MIT"
] | null | null | null | 54,104 | 54,104 | 0.753013 | true | 5,748 | Qwen/Qwen-72B | 1. YES
2. YES | 0.851953 | 0.712232 | 0.606788 | __label__kor_Hang | 1.000009 | 0.248103 |
# Logit and Logistic of array values
This notebook illustrates the level of control and flexibility available in Julia functions. The task is to evaluate the *logistic* function $(-\infty, \infty)\rightarrow(0,1)$
\begin{equation}
x \rightarrow \frac{1}{1 + e^{-x}}
\end{equation}
and its inverse, the *logit* or "lo... | 7835f0e7406d4fce17ffcabefe3884408e2e5cf5 | 15,812 | ipynb | Jupyter Notebook | CaseStudies/LogitLogistic.ipynb | dmbates/MixedMod | d7e8acd4ad40d1bfcb691cf17ec30143f7d797e6 | [
"MIT"
] | 23 | 2016-12-06T00:02:58.000Z | 2021-12-10T13:39:48.000Z | CaseStudies/LogitLogistic.ipynb | dmbates/MixedMod | d7e8acd4ad40d1bfcb691cf17ec30143f7d797e6 | [
"MIT"
] | null | null | null | CaseStudies/LogitLogistic.ipynb | dmbates/MixedMod | d7e8acd4ad40d1bfcb691cf17ec30143f7d797e6 | [
"MIT"
] | 6 | 2016-12-13T21:17:14.000Z | 2021-12-10T13:39:18.000Z | 25.627229 | 398 | 0.554832 | true | 2,442 | Qwen/Qwen-72B | 1. YES
2. YES | 0.867036 | 0.867036 | 0.751751 | __label__eng_Latn | 0.992166 | 0.584902 |
## Overview
Kamodo provides a *functional* interface for space weather analysis, visualization, and knowledge discovery, allowing many problems in scientific data analysis to be posed in terms of function composition and evaluation. We'll walk through its general features here.
## Kamodo objects
Users primarily int... | a40a97252f269cf52a0e0f8294e1e4510310c063 | 36,131 | ipynb | Jupyter Notebook | docs/notebooks/Kamodo.ipynb | iamjavaexpert/Kamodo | 26e7de66e67b9196ab19f13e73136db75832813c | [
"NASA-1.3"
] | null | null | null | docs/notebooks/Kamodo.ipynb | iamjavaexpert/Kamodo | 26e7de66e67b9196ab19f13e73136db75832813c | [
"NASA-1.3"
] | null | null | null | docs/notebooks/Kamodo.ipynb | iamjavaexpert/Kamodo | 26e7de66e67b9196ab19f13e73136db75832813c | [
"NASA-1.3"
] | null | null | null | 30.77598 | 457 | 0.457004 | true | 4,458 | Qwen/Qwen-72B | 1. YES
2. YES | 0.83762 | 0.874077 | 0.732145 | __label__eng_Latn | 0.709794 | 0.539349 |
## Visualizing Convolutional Neural Networks and Neural Style Transfer
July 2019 <br>
**Author:** Matthew Stewart
```python
#RUN THIS CELL
import requests
from IPython.core.display import HTML
styles = requests.get("https://raw.githubusercontent.com/Harvard-IACS/2019-CS109B/master/content/styles/cs109.css").text
HT... | d125c43f026017917a82b2a7f83e1a2fdf1bac58 | 26,003 | ipynb | Jupyter Notebook | Neural-Style-Transfer/Neural-Style-Transfer.ipynb | victorwu89/Neural-Networks | 6de5378701e5f8bac3be92ebf41ce778162a3d34 | [
"MIT"
] | 70 | 2019-06-18T07:32:23.000Z | 2022-01-18T07:53:08.000Z | Neural-Style-Transfer/Neural-Style-Transfer.ipynb | victorwu89/Neural-Networks | 6de5378701e5f8bac3be92ebf41ce778162a3d34 | [
"MIT"
] | null | null | null | Neural-Style-Transfer/Neural-Style-Transfer.ipynb | victorwu89/Neural-Networks | 6de5378701e5f8bac3be92ebf41ce778162a3d34 | [
"MIT"
] | 38 | 2019-06-18T13:33:44.000Z | 2022-03-15T13:16:10.000Z | 38.183554 | 567 | 0.57278 | true | 4,893 | Qwen/Qwen-72B | 1. YES
2. YES | 0.831143 | 0.851953 | 0.708095 | __label__eng_Latn | 0.954358 | 0.483473 |
```python
import numpy as np
import pandas as pd
import linearsolve as ls
import matplotlib.pyplot as plt
plt.style.use('classic')
%matplotlib inline
pd.plotting.register_matplotlib_converters()
```
# Class 16: Introduction to New-Keynesian Business Cycle Modeling
In this notebook, we will briefly explore US macroec... | ea58833929bfa97f3a96cbb1ce230eef12278a53 | 14,279 | ipynb | Jupyter Notebook | Lecture Notebooks/Econ126_Class_16_blank.ipynb | t-hdd/econ126 | 17029937bd6c40e606d145f8d530728585c30a1d | [
"MIT"
] | null | null | null | Lecture Notebooks/Econ126_Class_16_blank.ipynb | t-hdd/econ126 | 17029937bd6c40e606d145f8d530728585c30a1d | [
"MIT"
] | null | null | null | Lecture Notebooks/Econ126_Class_16_blank.ipynb | t-hdd/econ126 | 17029937bd6c40e606d145f8d530728585c30a1d | [
"MIT"
] | null | null | null | 39.885475 | 681 | 0.415575 | true | 1,775 | Qwen/Qwen-72B | 1. YES
2. YES | 0.63341 | 0.803174 | 0.508739 | __label__eng_Latn | 0.972699 | 0.020299 |
# Sampling of Signals
*This Jupyter notebook is part of a [collection of notebooks](../index.ipynb) in the bachelors module Signals and Systems, Comunications Engineering, Universität Rostock. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*
## Ideal Sampl... | 82811ae674bf9706b2372fce12bbd06a00ae9ef8 | 111,996 | ipynb | Jupyter Notebook | sampling/ideal.ipynb | swchao/signalsAndSystemsLecture | 7f135d091499e1d3d635bac6ddf22adee15454f8 | [
"MIT"
] | 3 | 2019-01-27T12:39:27.000Z | 2022-03-15T10:26:12.000Z | sampling/ideal.ipynb | xushoucai/signals-and-systems-lecture | 30dbbf9226d93b454639955f5462d57546a921c5 | [
"MIT"
] | null | null | null | sampling/ideal.ipynb | xushoucai/signals-and-systems-lecture | 30dbbf9226d93b454639955f5462d57546a921c5 | [
"MIT"
] | 2 | 2020-09-18T06:26:48.000Z | 2021-12-10T06:11:45.000Z | 249.434298 | 18,674 | 0.89155 | true | 3,959 | Qwen/Qwen-72B | 1. YES
2. YES
| 0.752013 | 0.740174 | 0.55662 | __label__eng_Latn | 0.990483 | 0.131545 |
```python
import os
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['mathtext.fontset'] = 'stix'
```
# Calculate $\kappa$ sampled from the first training
In the first training, we let 200 independent LSTMs predict 200 trajectories of 200$ns$. Since we are using LSTM as a generative model, we can also ... | dc88489b3731815ae6bed99795615000d172525f | 73,212 | ipynb | Jupyter Notebook | path_sampling_kappa.ipynb | tiwarylab/ps-LSTM | 2b9a7b825a2236abf279cd0e5f8b522e2c780dfa | [
"MIT"
] | 2 | 2022-03-02T12:56:22.000Z | 2022-03-02T21:13:25.000Z | path_sampling_kappa.ipynb | tiwarylab/ps-LSTM | 2b9a7b825a2236abf279cd0e5f8b522e2c780dfa | [
"MIT"
] | null | null | null | path_sampling_kappa.ipynb | tiwarylab/ps-LSTM | 2b9a7b825a2236abf279cd0e5f8b522e2c780dfa | [
"MIT"
] | null | null | null | 198.406504 | 43,204 | 0.911107 | true | 1,543 | Qwen/Qwen-72B | 1. YES
2. YES | 0.835484 | 0.763484 | 0.637878 | __label__eng_Latn | 0.670131 | 0.320335 |
# Prospect Theory and Cumulative Prospect Theory Agent Demo
The PTAgent and CPTAgent classes reproduce patterns of choice behavior described by Kahneman & Tverski's survey data in their seminal papers on Prospect Theory and Cumulative Prospect Theory. These classes expresses valuations of single lottery inputs, or exp... | d0c4a69a8845a8ea910dad591cb8a7363d3077a8 | 23,159 | ipynb | Jupyter Notebook | Prospect_Theory_Agent_Demo.ipynb | cognitionswitch/decisionscience | ef6e3363dc87b682853c7e23be32d9224ee366b6 | [
"MIT"
] | null | null | null | Prospect_Theory_Agent_Demo.ipynb | cognitionswitch/decisionscience | ef6e3363dc87b682853c7e23be32d9224ee366b6 | [
"MIT"
] | null | null | null | Prospect_Theory_Agent_Demo.ipynb | cognitionswitch/decisionscience | ef6e3363dc87b682853c7e23be32d9224ee366b6 | [
"MIT"
] | 1 | 2022-02-07T09:43:33.000Z | 2022-02-07T09:43:33.000Z | 33.515195 | 578 | 0.577227 | true | 4,530 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.833325 | 0.739329 | __label__eng_Latn | 0.936046 | 0.556042 |
# Cadenas de Markov
## Transiciones de Estado
La secuencia de variables aleatorias $x_0, x_1, x_2, \dots, x_t , \dots$ representa un **proceso estocástico**.
Cuando se indexan solamente los puntos en el tiempo en el que ocurren *cambios* significativos, se habla de **procesos estocásticos de tiempo discreto**.
Habl... | 499179a15d77cc950ecaef70937490892854e71c | 30,342 | ipynb | Jupyter Notebook | docs/01cm_definiciones.ipynb | map0logo/tci-2019 | 64b83aadf88bf1d666dee6b94eb698a8b6125c14 | [
"Unlicense"
] | 1 | 2022-03-27T04:04:33.000Z | 2022-03-27T04:04:33.000Z | docs/01cm_definiciones.ipynb | map0logo/tci-2019 | 64b83aadf88bf1d666dee6b94eb698a8b6125c14 | [
"Unlicense"
] | null | null | null | docs/01cm_definiciones.ipynb | map0logo/tci-2019 | 64b83aadf88bf1d666dee6b94eb698a8b6125c14 | [
"Unlicense"
] | null | null | null | 51.689949 | 486 | 0.529497 | true | 2,859 | Qwen/Qwen-72B | 1. YES
2. YES | 0.885631 | 0.891811 | 0.789816 | __label__spa_Latn | 0.983051 | 0.67334 |
<!-- dom:TITLE: Week 3 January 18-22: Building a Variational Monte Carlo program -->
# Week 3 January 18-22: Building a Variational Monte Carlo program
<!-- dom:AUTHOR: Morten Hjorth-Jensen Email morten.hjorth-jensen@fys.uio.no at Department of Physics and Center fo Computing in Science Education, University of Oslo... | a1f7e4404e2ae3aba3d6af02a79e39148334b2d8 | 113,161 | ipynb | Jupyter Notebook | doc/pub/week2/ipynb/week2.ipynb | Schoyen/ComputationalPhysics2 | 9cf10ffb2557cc73c4e6bab060d53690ee39426f | [
"CC0-1.0"
] | 87 | 2015-01-21T08:29:56.000Z | 2022-03-28T07:11:53.000Z | doc/pub/week2/ipynb/week2.ipynb | Schoyen/ComputationalPhysics2 | 9cf10ffb2557cc73c4e6bab060d53690ee39426f | [
"CC0-1.0"
] | 3 | 2020-01-18T10:43:38.000Z | 2020-02-08T13:15:42.000Z | doc/pub/week2/ipynb/week2.ipynb | Schoyen/ComputationalPhysics2 | 9cf10ffb2557cc73c4e6bab060d53690ee39426f | [
"CC0-1.0"
] | 54 | 2015-02-09T10:02:00.000Z | 2022-03-07T10:44:14.000Z | 81.528098 | 41,156 | 0.796476 | true | 9,189 | Qwen/Qwen-72B | 1. YES
2. YES | 0.66888 | 0.853913 | 0.571165 | __label__eng_Latn | 0.976544 | 0.165338 |
```python
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import sympy
%matplotlib inline
```
```python
df = pd.read_csv('../data/raw/cities.csv', index_col=['CityId'])
primes = list(sympy.primerange(0, max(df.index)))
df['prime'] = df.index.isin(primes).astype(int)
``... | 6a3df7f265b6ab66c440deda6fd8e9c3e7fda375 | 4,126 | ipynb | Jupyter Notebook | notebooks/Primes-add-from-best.ipynb | alexandrnikitin/kaggle-traveling-santa-2018-prime-paths | 44a537ee3388d52dba5abffedd8f014820c8fd40 | [
"MIT"
] | null | null | null | notebooks/Primes-add-from-best.ipynb | alexandrnikitin/kaggle-traveling-santa-2018-prime-paths | 44a537ee3388d52dba5abffedd8f014820c8fd40 | [
"MIT"
] | null | null | null | notebooks/Primes-add-from-best.ipynb | alexandrnikitin/kaggle-traveling-santa-2018-prime-paths | 44a537ee3388d52dba5abffedd8f014820c8fd40 | [
"MIT"
] | null | null | null | 20.733668 | 73 | 0.480368 | true | 530 | Qwen/Qwen-72B | 1. YES
2. YES | 0.795658 | 0.682574 | 0.543095 | __label__eng_Latn | 0.146335 | 0.100122 |
# From Second Quantization to Equation-of-Motion Coupled-Cluster using SymPy
## Table of contents
1. [Introduction](#Introduction)
2. [Second Quantization](#Second-Quantization)
3. [Normal product](#Normal-product)
4. [Contraction](#Contraction)
5. [Wicks theorem](#Wicks-theorem)
6. [Particle-Hole formalism](#Particl... | 792f957ee53076b125068a419bf5e24a00f79a24 | 78,462 | ipynb | Jupyter Notebook | SQ2EOM.ipynb | sgulania/SQ2EOM | d10b79fc661ded29a6712e447eee3ea852e22882 | [
"MIT"
] | null | null | null | SQ2EOM.ipynb | sgulania/SQ2EOM | d10b79fc661ded29a6712e447eee3ea852e22882 | [
"MIT"
] | null | null | null | SQ2EOM.ipynb | sgulania/SQ2EOM | d10b79fc661ded29a6712e447eee3ea852e22882 | [
"MIT"
] | null | null | null | 51.585799 | 4,423 | 0.521361 | true | 12,705 | Qwen/Qwen-72B | 1. YES
2. YES | 0.826712 | 0.819893 | 0.677815 | __label__eng_Latn | 0.26408 | 0.413124 |
```python
import utils
%load_ext autoreload
%autoreload 2
from utils import build_transf, full_homo_transf, prop_velo, prop_force_torque, comp_jacobian
import utils
from sympy import sqrt
import sympy as sy
from IPython.display import display, Math
```
The autoreload extension is already loaded. To reload it, use... | 7e57ec402907c3f5e4783bdf9f5928a15487d920 | 7,816 | ipynb | Jupyter Notebook | examples/test_2021F.ipynb | philippwulff/robotics_calc | 8365ed3931206ca3788086e261d800ebe21ef86b | [
"MIT"
] | null | null | null | examples/test_2021F.ipynb | philippwulff/robotics_calc | 8365ed3931206ca3788086e261d800ebe21ef86b | [
"MIT"
] | null | null | null | examples/test_2021F.ipynb | philippwulff/robotics_calc | 8365ed3931206ca3788086e261d800ebe21ef86b | [
"MIT"
] | null | null | null | 34.737778 | 389 | 0.480553 | true | 1,653 | Qwen/Qwen-72B | 1. YES
2. YES | 0.849971 | 0.679179 | 0.577282 | __label__kor_Hang | 0.119974 | 0.17955 |
# simbMoments
*simbMoments* determines a system of equations corresponding to the first and second moments of the population observations. The process to find each moment is quite similar to the way it was done to find the system of differential equations using *simbODE*. The equations are sympy objects which one can ... | ff6773c85447b207b07a002415ca9f0e03139f6c | 9,073 | ipynb | Jupyter Notebook | Single_Units/simbMoments.ipynb | Jebrayam/systemsbiology | 65041a2bf6c5e06842042a0bdf5f7528c778fe3f | [
"MIT"
] | null | null | null | Single_Units/simbMoments.ipynb | Jebrayam/systemsbiology | 65041a2bf6c5e06842042a0bdf5f7528c778fe3f | [
"MIT"
] | 1 | 2020-10-16T03:30:51.000Z | 2020-10-16T03:33:01.000Z | Single_Units/simbMoments.ipynb | Jebrayam/systemsbiology | 65041a2bf6c5e06842042a0bdf5f7528c778fe3f | [
"MIT"
] | null | null | null | 30.243333 | 637 | 0.458393 | true | 1,341 | Qwen/Qwen-72B | 1. YES
2. YES | 0.822189 | 0.763484 | 0.627728 | __label__eng_Latn | 0.967184 | 0.296753 |
```python
import numpy as np
import scipy.stats as si
import scipy
import sympy as sy
import matplotlib.pyplot as plt
import pandas as pd
# import sympy.statistics as systats
```
```python
def euro_opt(S, K, T, r, sigma, option = 'call'):
#S: spot price
#K: strike price
#T: time to maturity
#r: i... | 68e0d0a6a421c7230d4777e9ee66fc9ca6f595ae | 156,203 | ipynb | Jupyter Notebook | mod-mat-financas-I-2019-1/Project_3/proj3.ipynb | mirandagil/university-courses | e70ce5262555e84cffb13e53e139e7eec21e8907 | [
"MIT"
] | 1 | 2019-12-23T16:39:01.000Z | 2019-12-23T16:39:01.000Z | mod-mat-financas-I-2019-1/Project_3/proj3.ipynb | mirandagil/university-courses | e70ce5262555e84cffb13e53e139e7eec21e8907 | [
"MIT"
] | null | null | null | mod-mat-financas-I-2019-1/Project_3/proj3.ipynb | mirandagil/university-courses | e70ce5262555e84cffb13e53e139e7eec21e8907 | [
"MIT"
] | null | null | null | 197.975919 | 44,404 | 0.908805 | true | 3,680 | Qwen/Qwen-72B | 1. YES
2. YES | 0.901921 | 0.798187 | 0.719901 | __label__yue_Hant | 0.139701 | 0.510903 |
# The Material Derivative
## Learning outcomes
* Understand the chain rule and the material derivative
The material derivative (or the substantive derivative) is an important concept in the analysis of fluid flow so it is worth taking some time to understand it.
Consider a time invariant flow in a nozzle. The conti... | 36a6f5b73ac4bcde756afae113d13c804c964837 | 8,941 | ipynb | Jupyter Notebook | 3.2a The Material Derivative and the Chain Rule.ipynb | nolankucd/MEEN20010 | c82ad69956839bef123b6d4e3b5d74a096c32046 | [
"MIT"
] | 4 | 2020-09-21T11:35:24.000Z | 2020-10-22T18:19:10.000Z | 3.2a The Material Derivative and the Chain Rule.ipynb | iitrabhi/MEEN20010 | c82ad69956839bef123b6d4e3b5d74a096c32046 | [
"MIT"
] | null | null | null | 3.2a The Material Derivative and the Chain Rule.ipynb | iitrabhi/MEEN20010 | c82ad69956839bef123b6d4e3b5d74a096c32046 | [
"MIT"
] | 6 | 2019-09-17T18:10:22.000Z | 2021-05-01T12:34:19.000Z | 48.32973 | 506 | 0.587294 | true | 1,934 | Qwen/Qwen-72B | 1. YES
2. YES | 0.833325 | 0.847968 | 0.706632 | __label__eng_Latn | 0.994478 | 0.480075 |
# EECS 445: Machine Learning
## Hands On 10: Bias Variance Tradeoff
Consider a sequence of IID random variable:
$$
X_i =
\begin{cases}
100 & \text{ with prob. } 0.02 \\
0 & \text{ with prob. } 0.97 \\
-100 & \text{ with prob. } 0.01 \\
\end{cases}
$$
The true mean of $X_i$ is
$$
0.02 \times 100 + 0.97 \times 0 + 0.... | ded95dbda472655b1573d1eeff50467bcea2727c | 8,490 | ipynb | Jupyter Notebook | handsOn_lecture10_bias-variance_tradeoff/draft/bias_variance_solutions.ipynb | xipengwang/umich-eecs445-f16 | 298407af9fd417c1b6daa6127b17cb2c34c2c772 | [
"MIT"
] | 97 | 2016-09-11T23:15:35.000Z | 2022-02-22T08:03:24.000Z | handsOn_lecture10_bias-variance_tradeoff/draft/bias_variance_solutions.ipynb | eecs445-f16/umich-eecs445-f16 | 298407af9fd417c1b6daa6127b17cb2c34c2c772 | [
"MIT"
] | null | null | null | handsOn_lecture10_bias-variance_tradeoff/draft/bias_variance_solutions.ipynb | eecs445-f16/umich-eecs445-f16 | 298407af9fd417c1b6daa6127b17cb2c34c2c772 | [
"MIT"
] | 77 | 2016-09-12T20:50:46.000Z | 2022-01-03T14:41:23.000Z | 36.753247 | 618 | 0.546879 | true | 1,827 | Qwen/Qwen-72B | 1. YES
2. YES | 0.800692 | 0.859664 | 0.688326 | __label__eng_Latn | 0.977394 | 0.437543 |
# Matrix Factorization for Recommendations in Python <a class="anchor" id="mfrp"></a>
In this post, I'll detail a basic version of low-rank matrix factorization for recommendations employ it on a dataset of 1 million movie ratings (from 1 to 5) available from the [MovieLens](http://grouplens.org/datasets/movielens/) p... | aa4b79aec03a3064ba670e6c3fde3ea7edaf5314 | 36,982 | ipynb | Jupyter Notebook | _notebooks/2022-01-16-mf-ml.ipynb | recohut/notebook | 610670666a1c3d8ef430d42f712ff72ecdbd8f86 | [
"Apache-2.0"
] | null | null | null | _notebooks/2022-01-16-mf-ml.ipynb | recohut/notebook | 610670666a1c3d8ef430d42f712ff72ecdbd8f86 | [
"Apache-2.0"
] | 1 | 2022-01-12T05:40:57.000Z | 2022-01-12T05:40:57.000Z | _notebooks/2022-01-16-mf-ml.ipynb | recohut/notebook | 610670666a1c3d8ef430d42f712ff72ecdbd8f86 | [
"Apache-2.0"
] | null | null | null | 36,982 | 36,982 | 0.545725 | true | 7,725 | Qwen/Qwen-72B | 1. YES
2. YES | 0.880797 | 0.808067 | 0.711743 | __label__eng_Latn | 0.816673 | 0.49195 |
# **Фильтр Калмана для системы ДУ второго порядка**
## Филаткин Алексей
Построим фильтр Калмана для системы
\begin{cases}
\dot x(t) = \begin{pmatrix} 0 & 1\\ 1 & 0 \end{pmatrix}x(t) + \begin{pmatrix} 1\\ 0\end{pmatrix}u(t) + \widetilde{w}(t)\\
z(t) = \begin{pmatrix} 1& 0\end{pmatrix}x(t) + v(t)
\end{cases}
гд... | aadf314e679de58d3e822dc84d52d333471e1ded | 653,691 | ipynb | Jupyter Notebook | Homework Problems/Kalman Filtering/Kalman_Filter_for_second_order_system.ipynb | DPritykin/Control-Theory-Course | f27c13cd0bf9671518c78414f8c3963c7cb870d6 | [
"MIT"
] | 6 | 2022-02-21T06:42:30.000Z | 2022-03-14T05:18:00.000Z | Homework Problems/Kalman Filtering/Kalman_Filter_for_second_order_system.ipynb | DPritykin/Control-Theory-Course | f27c13cd0bf9671518c78414f8c3963c7cb870d6 | [
"MIT"
] | null | null | null | Homework Problems/Kalman Filtering/Kalman_Filter_for_second_order_system.ipynb | DPritykin/Control-Theory-Course | f27c13cd0bf9671518c78414f8c3963c7cb870d6 | [
"MIT"
] | 1 | 2022-03-07T16:25:30.000Z | 2022-03-07T16:25:30.000Z | 1,328.640244 | 262,964 | 0.95528 | true | 4,167 | Qwen/Qwen-72B | 1. YES
2. YES | 0.913677 | 0.72487 | 0.662297 | __label__rus_Cyrl | 0.127872 | 0.377069 |
# Best responses
---
## Definition of a best response
[Video](https://youtu.be/cJUZEmfhdcA?list=PLnC5h3PY-znxMsG0TRYGOyrnEO-QhVwLb)
In a two player game $(A,B)\in{\mathbb{R}^{m\times n}}^2$ a mixed strategy $\sigma_r^*$ of the row player is a best response to a column players' strategy $\sigma_c$ iff:
$$
\sigma_r... | 923ce5883593a39448505dbc85ac687b44eea902 | 59,572 | ipynb | Jupyter Notebook | nbs/chapters/04-Nash-equilibria.ipynb | prokolyvakis/gt | e679e5d54d9a98583ad4981411ce505cea31f028 | [
"MIT"
] | 27 | 2017-05-25T08:10:40.000Z | 2021-12-07T21:01:51.000Z | nbs/chapters/04-Nash-equilibria.ipynb | prokolyvakis/gt | e679e5d54d9a98583ad4981411ce505cea31f028 | [
"MIT"
] | 65 | 2017-05-23T16:12:03.000Z | 2022-03-30T13:42:25.000Z | nbs/chapters/04-Nash-equilibria.ipynb | prokolyvakis/gt | e679e5d54d9a98583ad4981411ce505cea31f028 | [
"MIT"
] | 10 | 2017-06-19T11:04:06.000Z | 2020-08-30T11:28:00.000Z | 189.11746 | 23,936 | 0.893071 | true | 2,129 | Qwen/Qwen-72B | 1. YES
2. YES | 0.847968 | 0.845942 | 0.717332 | __label__eng_Latn | 0.940996 | 0.504934 |
### DEMDP06
# Deterministic Optimal Economic Growth Model
Welfare maximizing social planner must decide how much society should consume and invest. Model is of special interest because it has a known closed-form solution.
- States
- s stock of wealth
- Actions
- k capital investment
- Pa... | 64aa1da8038222065e9031a7fbe85185d0122ccf | 214,958 | ipynb | Jupyter Notebook | notebooks/dp/06 Deterministic Optimal Economic Growth Model.ipynb | daniel-schaefer/CompEcon-python | d3f66e04a7e02be648fc5a68065806ec7cc6ffd6 | [
"MIT"
] | null | null | null | notebooks/dp/06 Deterministic Optimal Economic Growth Model.ipynb | daniel-schaefer/CompEcon-python | d3f66e04a7e02be648fc5a68065806ec7cc6ffd6 | [
"MIT"
] | null | null | null | notebooks/dp/06 Deterministic Optimal Economic Growth Model.ipynb | daniel-schaefer/CompEcon-python | d3f66e04a7e02be648fc5a68065806ec7cc6ffd6 | [
"MIT"
] | 1 | 2021-06-01T03:47:35.000Z | 2021-06-01T03:47:35.000Z | 215.388778 | 61,552 | 0.899506 | true | 4,440 | Qwen/Qwen-72B | 1. YES
2. YES | 0.891811 | 0.890294 | 0.793974 | __label__eng_Latn | 0.548218 | 0.683001 |
# Introduction to Decision Theory using Probabilistic Graphical Models
> So far, we have seen that probabilistic graphical models are useful for modeling situations that involve uncertainty. Furthermore, we will see in the next module how using inference algorithms we will also reach conclusions abount the current s... | 6fdcb1d567e41e10023e47c22b5f87ace57d2fc1 | 267,071 | ipynb | Jupyter Notebook | Modulo1/Clase4/DecisionTheory.ipynb | esjimenezro/mgp_online_public | b2d2a49c1c8730d1e507144ac4f65ec6842a5d94 | [
"MIT"
] | null | null | null | Modulo1/Clase4/DecisionTheory.ipynb | esjimenezro/mgp_online_public | b2d2a49c1c8730d1e507144ac4f65ec6842a5d94 | [
"MIT"
] | null | null | null | Modulo1/Clase4/DecisionTheory.ipynb | esjimenezro/mgp_online_public | b2d2a49c1c8730d1e507144ac4f65ec6842a5d94 | [
"MIT"
] | null | null | null | 262.865157 | 52,860 | 0.920261 | true | 4,614 | Qwen/Qwen-72B | 1. YES
2. YES | 0.795658 | 0.787931 | 0.626924 | __label__eng_Latn | 0.993975 | 0.294885 |
```python
import numpy as np
import pandas as pd
import linearsolve as ls
import matplotlib.pyplot as plt
plt.style.use('classic')
%matplotlib inline
```
# Homework 8
**Instructions:** Complete the notebook below. Download the completed notebook in HTML format. Upload assignment using Canvas.
**Due:** Mar. 12 at **1... | 5df99fc1b50c563d3caa8e462cde31ca6f3c3ee2 | 11,512 | ipynb | Jupyter Notebook | Homework/Econ126_Winter2020_Homework_08_blank.ipynb | t-hdd/econ126 | 17029937bd6c40e606d145f8d530728585c30a1d | [
"MIT"
] | null | null | null | Homework/Econ126_Winter2020_Homework_08_blank.ipynb | t-hdd/econ126 | 17029937bd6c40e606d145f8d530728585c30a1d | [
"MIT"
] | null | null | null | Homework/Econ126_Winter2020_Homework_08_blank.ipynb | t-hdd/econ126 | 17029937bd6c40e606d145f8d530728585c30a1d | [
"MIT"
] | null | null | null | 39.696552 | 586 | 0.392547 | true | 1,463 | Qwen/Qwen-72B | 1. YES
2. YES | 0.808067 | 0.839734 | 0.678561 | __label__eng_Latn | 0.978918 | 0.414857 |
# 微積分の計算について N0.3 不定積分の内容－１
### 学籍番号[＿＿＿＿＿＿＿＿＿]クラス[＿＿＿＿＿] クラス番号[＿＿＿＿＿] 名前[＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿]
積分の式
（１）変形、整理できるか
(２）部分分数に変換できるか
（３）三角関数などは公式を使って変形できるか
（４）分数の分母の有理化できるか
（５）分母を平方完成形にできるか
積分のルール
$$ \int cf(x) dx = c \int f(x) dx $$
$$ \int \{ f(x)\pm g(x)\} dx = \int f(x) dx \pm \int g(x) ... | 101501a149db7cca175021c830f550a63998509f | 31,524 | ipynb | Jupyter Notebook | 03_20181023-sekibun-1-Ex&ans.ipynb | kt-pro-git-1/Calculus_Differential_Equation-public | d5deaf117e6841c4f6ceb53bc80b020220fd4814 | [
"MIT"
] | 1 | 2019-07-10T11:33:18.000Z | 2019-07-10T11:33:18.000Z | 03_20181023-sekibun-1-Ex&ans.ipynb | kt-pro-git-1/Calculus_Differential_Equation-public | d5deaf117e6841c4f6ceb53bc80b020220fd4814 | [
"MIT"
] | null | null | null | 03_20181023-sekibun-1-Ex&ans.ipynb | kt-pro-git-1/Calculus_Differential_Equation-public | d5deaf117e6841c4f6ceb53bc80b020220fd4814 | [
"MIT"
] | null | null | null | 55.893617 | 2,684 | 0.77506 | true | 717 | Qwen/Qwen-72B | 1. YES
2. YES | 0.882428 | 0.808067 | 0.713061 | __label__roh_Latn | 0.269096 | 0.495011 |
# Click "Edit App" to see the code
# Histogram and normal distribution
In this tutorial we'll learn how to read a CSV file into a _pands_ DataFrame, compute the average of the data in the second column, build a histogram and compare it to the _normal_ distribution.
# The Jupyter Notebook
Let's start by loading the us... | 8997d3aa9be6b1cbb40d802fc3b764bd6cdc2086 | 13,991 | ipynb | Jupyter Notebook | codeSnippets/1_averageAndHistogram.ipynb | praiteri/TeachingNotebook | 75ee8baf8ef81154dffcac556d4739bf73eba712 | [
"MIT"
] | null | null | null | codeSnippets/1_averageAndHistogram.ipynb | praiteri/TeachingNotebook | 75ee8baf8ef81154dffcac556d4739bf73eba712 | [
"MIT"
] | null | null | null | codeSnippets/1_averageAndHistogram.ipynb | praiteri/TeachingNotebook | 75ee8baf8ef81154dffcac556d4739bf73eba712 | [
"MIT"
] | 1 | 2022-02-23T11:36:12.000Z | 2022-02-23T11:36:12.000Z | 31.091111 | 443 | 0.594525 | true | 2,183 | Qwen/Qwen-72B | 1. YES
2. YES | 0.897695 | 0.879147 | 0.789206 | __label__eng_Latn | 0.992877 | 0.671922 |
# Thomson's "Multitaper" Estimator
This notebook is a demo & test of new multitaper estimator code.
**TODO**: the jackknife is not working in spawn mode
```python
import multiprocessing as mp
try:
mp.set_start_method('spawn')
except:
pass
```
```python
%matplotlib inline
```
```python
# basic stuff
impo... | 0a1a87e2fffab9ced868788530479ed468705425 | 27,506 | ipynb | Jupyter Notebook | docs/source/usage_demos/multitaper_estimator.ipynb | miketrumpis/ecoglib | 2ecc5bc64920d96e01297cce5472d4b4797c3a7d | [
"BSD-3-Clause"
] | 1 | 2021-11-06T21:39:01.000Z | 2021-11-06T21:39:01.000Z | docs/source/usage_demos/multitaper_estimator.ipynb | miketrumpis/ecoglib | 2ecc5bc64920d96e01297cce5472d4b4797c3a7d | [
"BSD-3-Clause"
] | null | null | null | docs/source/usage_demos/multitaper_estimator.ipynb | miketrumpis/ecoglib | 2ecc5bc64920d96e01297cce5472d4b4797c3a7d | [
"BSD-3-Clause"
] | 1 | 2022-01-10T20:40:18.000Z | 2022-01-10T20:40:18.000Z | 41.424699 | 696 | 0.605504 | true | 6,221 | Qwen/Qwen-72B | 1. YES
2. YES | 0.861538 | 0.833325 | 0.717941 | __label__eng_Latn | 0.917912 | 0.506349 |
# Unrestricted Open-Shell Hartree-Fock
In the first two tutorials in this module, we wrote programs which implement a closed-shell formulation of Hartree-Fock theory using restricted orbitals, aptly named Restricted Hartree-Fock (RHF). In this tutorial, we will abandon strictly closed-shell systems and the notion of ... | 3888d8295b949bad6f4528677bd606f5426e7067 | 22,150 | ipynb | Jupyter Notebook | Tutorials/03_Hartree-Fock/3c_unrestricted-hartree-fock.ipynb | zyth0s/psi4julia | beb0384028f1a3654b8a2f8690b7db5bd9c24b86 | [
"BSD-3-Clause"
] | 4 | 2021-02-13T22:14:21.000Z | 2021-04-17T07:34:10.000Z | Tutorials/03_Hartree-Fock/3c_unrestricted-hartree-fock.ipynb | zyth0s/psi4julia | beb0384028f1a3654b8a2f8690b7db5bd9c24b86 | [
"BSD-3-Clause"
] | null | null | null | Tutorials/03_Hartree-Fock/3c_unrestricted-hartree-fock.ipynb | zyth0s/psi4julia | beb0384028f1a3654b8a2f8690b7db5bd9c24b86 | [
"BSD-3-Clause"
] | null | null | null | 44.3 | 597 | 0.573138 | true | 4,929 | Qwen/Qwen-72B | 1. YES
2. YES | 0.872347 | 0.800692 | 0.698482 | __label__eng_Latn | 0.960547 | 0.461138 |
# Consumption Equivalent Variation (CEV)
1. Use the model in the **ConsumptionSaving.pdf** slides and solve it using **egm**
2. This notebooks estimates the *cost of income risk* through the Consumption Equivalent Variation (CEV)
We will here focus on the cost of income risk, but the CEV can be used to estimate the ... | 6ad9f6a094b82bab66af718a10fe430258b2b528 | 24,603 | ipynb | Jupyter Notebook | 00. DynamicProgramming/extra/Consumption Equivalent Variation (CEV).ipynb | alanlujan91/ConsumptionSavingNotebooks | 4455500d17fed4dd1f3f4844aeb5dd5d3b89903f | [
"MIT"
] | 20 | 2019-03-09T02:08:49.000Z | 2022-03-28T15:56:04.000Z | 00. DynamicProgramming/extra/Consumption Equivalent Variation (CEV).ipynb | alanlujan91/ConsumptionSavingNotebooks | 4455500d17fed4dd1f3f4844aeb5dd5d3b89903f | [
"MIT"
] | 1 | 2019-06-03T18:33:44.000Z | 2019-07-02T13:51:21.000Z | 00. DynamicProgramming/extra/Consumption Equivalent Variation (CEV).ipynb | alanlujan91/ConsumptionSavingNotebooks | 4455500d17fed4dd1f3f4844aeb5dd5d3b89903f | [
"MIT"
] | 34 | 2019-02-26T19:27:37.000Z | 2021-12-27T09:34:04.000Z | 86.024476 | 16,056 | 0.830834 | true | 1,402 | Qwen/Qwen-72B | 1. YES
2. YES | 0.808067 | 0.766294 | 0.619217 | __label__eng_Latn | 0.920455 | 0.276979 |
# Transformée de Fourier
```python
%matplotlib inline
import matplotlib
matplotlib.rcParams['figure.figsize'] = (6, 6)
import math
import cmath # math functions for complex numbers
import numpy as np
import matplotlib.pyplot as plt
import ipywidgets
from ipywidgets import interact
import sympy as sp
# S... | b90d74d5cd5dd4938e2e665a6026d21e29622b15 | 30,147 | ipynb | Jupyter Notebook | nb_sci_signal_processing/signal_processing_fourier_transform_fr.ipynb | jdhp-docs/python-notebooks | 91a97ea5cf374337efa7409e4992ea3f26b99179 | [
"MIT"
] | 3 | 2017-05-03T12:23:36.000Z | 2020-10-26T17:30:56.000Z | nb_sci_signal_processing/signal_processing_fourier_transform_fr.ipynb | jdhp-docs/python-notebooks | 91a97ea5cf374337efa7409e4992ea3f26b99179 | [
"MIT"
] | null | null | null | nb_sci_signal_processing/signal_processing_fourier_transform_fr.ipynb | jdhp-docs/python-notebooks | 91a97ea5cf374337efa7409e4992ea3f26b99179 | [
"MIT"
] | 1 | 2020-10-26T17:30:57.000Z | 2020-10-26T17:30:57.000Z | 25.988793 | 264 | 0.423624 | true | 6,987 | Qwen/Qwen-72B | 1. YES
2. YES | 0.771844 | 0.819893 | 0.632829 | __label__fra_Latn | 0.208426 | 0.308605 |
# Part 0: Hello Qiskit
While skip talking about how a quantum computer is important and hot in recent days, let's jump into the quantum circuit directly by using Qiskit.
Qiskit is an open-source SDK for working with quantum computers at the level of pulses, circuits, and algorithms. Qiskit supports many quantum backe... | 63db239c2666277086d9ba4ceb9bd457aa9585cc | 37,550 | ipynb | Jupyter Notebook | Lecture1/Lecture 1 - Intro to QC for the physicist Part0 Part1.ipynb | 0sophy1/Oct2021HKUST | c968f23e73469681a5a67882cef6a7dad8f36ab7 | [
"Apache-2.0"
] | null | null | null | Lecture1/Lecture 1 - Intro to QC for the physicist Part0 Part1.ipynb | 0sophy1/Oct2021HKUST | c968f23e73469681a5a67882cef6a7dad8f36ab7 | [
"Apache-2.0"
] | null | null | null | Lecture1/Lecture 1 - Intro to QC for the physicist Part0 Part1.ipynb | 0sophy1/Oct2021HKUST | c968f23e73469681a5a67882cef6a7dad8f36ab7 | [
"Apache-2.0"
] | 1 | 2021-10-31T10:30:15.000Z | 2021-10-31T10:30:15.000Z | 33.377778 | 989 | 0.544154 | true | 8,033 | Qwen/Qwen-72B | 1. YES
2. YES | 0.901921 | 0.841826 | 0.75926 | __label__eng_Latn | 0.91904 | 0.602347 |
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