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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
```python
import numpy as np
import cv2
import matplotlib.pyplot as plt
import os
import argparse
import glob
import torch
import torch.nn as nn
from torch.autograd import Variable
from DnCNN.models import DnCNN
from DnCNN.utils import *
```
# Padding
Instead of padding an input image of dimensions $(w, h)$ so that i... | 70a1023abb1628d86cc12a1a5797948bc4cee2ce | 585,038 | ipynb | Jupyter Notebook | pnp_admm.ipynb | matthewachan/DiffuserCam | 60b40234c2ef26b669b33dc1faa518aeb33848fe | [
"MIT"
] | null | null | null | pnp_admm.ipynb | matthewachan/DiffuserCam | 60b40234c2ef26b669b33dc1faa518aeb33848fe | [
"MIT"
] | 3 | 2022-02-03T05:06:24.000Z | 2022-02-04T06:25:48.000Z | pnp_admm.ipynb | matthewachan/DiffuserCam | 60b40234c2ef26b669b33dc1faa518aeb33848fe | [
"MIT"
] | null | null | null | 937.560897 | 106,610 | 0.946959 | true | 4,732 | Qwen/Qwen-72B | 1. YES
2. YES | 0.793106 | 0.740174 | 0.587037 | __label__eng_Latn | 0.806757 | 0.202213 |
# Basis for grayscale images
## Introduction
Consider the set of real-valued matrices of size $M\times N$; we can turn this into a vector space by defining addition and scalar multiplication in the usual way:
\begin{align}
\mathbf{A} + \mathbf{B} &=
\left[
\begin{array}{ccc}
a_{0,0} & \do... | 069c785a4dedfd845f1cebf03b3abe4464474103 | 164,646 | ipynb | Jupyter Notebook | epfl/2020/hw-ipynb/HaarBasis/hb.ipynb | phunc20/dsp | e7c496eb5fd4b8694eab0fc049cf98a5e3dfd886 | [
"MIT"
] | 1 | 2021-03-12T18:32:06.000Z | 2021-03-12T18:32:06.000Z | epfl/2020/hw-ipynb/HaarBasis/hb.ipynb | phunc20/dsp | e7c496eb5fd4b8694eab0fc049cf98a5e3dfd886 | [
"MIT"
] | null | null | null | epfl/2020/hw-ipynb/HaarBasis/hb.ipynb | phunc20/dsp | e7c496eb5fd4b8694eab0fc049cf98a5e3dfd886 | [
"MIT"
] | null | null | null | 106.154739 | 17,060 | 0.827557 | true | 8,332 | Qwen/Qwen-72B | 1. YES
2. YES | 0.943348 | 0.90599 | 0.854663 | __label__eng_Latn | 0.964962 | 0.824002 |
```python
%matplotlib inline
```
# Lugiato-Lefever equation -- Soliton molecules
This example shows how to perform simulations for the Lugiato-Lefever equation
(LLE) [1], using functionality implemented by `py-fmas`.
In particular, this example implements the first-order propagation equation
\begin{align}\partial_... | c423caf9909c3943e3c13f08e39ba80172d69036 | 6,012 | ipynb | Jupyter Notebook | docs/_downloads/22a359d30725e68cca5dae08df9a6a4c/g_model_LLE.ipynb | nunoedgarhubsoftphotoflow/py-fmas | 241d942fe0cd6a49001b1bf110dd32bccc86bb16 | [
"MIT"
] | 4 | 2021-04-28T07:02:54.000Z | 2022-01-25T13:15:49.000Z | docs/_downloads/22a359d30725e68cca5dae08df9a6a4c/g_model_LLE.ipynb | Photonics-Precision-Technologies/py-fmas | 241d942fe0cd6a49001b1bf110dd32bccc86bb16 | [
"MIT"
] | 3 | 2021-06-10T07:11:35.000Z | 2021-11-22T15:23:01.000Z | docs/_downloads/22a359d30725e68cca5dae08df9a6a4c/g_model_LLE.ipynb | Photonics-Precision-Technologies/py-fmas | 241d942fe0cd6a49001b1bf110dd32bccc86bb16 | [
"MIT"
] | 5 | 2021-05-20T08:53:44.000Z | 2022-01-25T13:18:34.000Z | 111.333333 | 3,481 | 0.592149 | true | 1,568 | Qwen/Qwen-72B | 1. YES
2. YES | 0.771843 | 0.682574 | 0.52684 | __label__eng_Latn | 0.32087 | 0.062355 |
```python
import numpy as np
import pandas as pd
import linearsolve as ls
import matplotlib.pyplot as plt
plt.style.use('classic')
%matplotlib inline
```
# Class 13: Introduction to Real Business Cycle Modeling
Real business cycle (RBC) models are extensions of the stochastic Solow model. RBC models replace the ad ho... | 3bd82714c49ec1c6cab78492a61111e021d494f0 | 11,273 | ipynb | Jupyter Notebook | Lecture Notebooks/Econ126_Class_13_blank.ipynb | t-hdd/econ126 | 17029937bd6c40e606d145f8d530728585c30a1d | [
"MIT"
] | null | null | null | Lecture Notebooks/Econ126_Class_13_blank.ipynb | t-hdd/econ126 | 17029937bd6c40e606d145f8d530728585c30a1d | [
"MIT"
] | null | null | null | Lecture Notebooks/Econ126_Class_13_blank.ipynb | t-hdd/econ126 | 17029937bd6c40e606d145f8d530728585c30a1d | [
"MIT"
] | null | null | null | 36.71987 | 412 | 0.394837 | true | 1,270 | Qwen/Qwen-72B | 1. YES
2. YES | 0.815232 | 0.72487 | 0.590938 | __label__eng_Latn | 0.967399 | 0.211277 |
# Ordinary Differential Equation Solvers: Runge-Kutta Methods
### Christina Lee
### Category: Numerics
So what's an <i>Ordinary Differential Equation</i>?
Differential Equation means we have some equation (or equations) that have derivatives in them.
The <i>ordinary</i> part differentiates them from <i>partial</i>... | e01a5c070237185708fa77bb8a558f365b259ffb | 260,751 | ipynb | Jupyter Notebook | Numerics_Prog/Runge-Kutta-Methods.ipynb | albi3ro/M4 | ccd27d4b8b24861e22fe806ebaecef70915081a8 | [
"MIT"
] | 22 | 2015-11-15T08:47:04.000Z | 2022-02-25T10:47:12.000Z | Numerics_Prog/Runge-Kutta-Methods.ipynb | albi3ro/M4 | ccd27d4b8b24861e22fe806ebaecef70915081a8 | [
"MIT"
] | 11 | 2016-02-23T12:18:26.000Z | 2019-09-14T07:14:26.000Z | Numerics_Prog/Runge-Kutta-Methods.ipynb | albi3ro/M4 | ccd27d4b8b24861e22fe806ebaecef70915081a8 | [
"MIT"
] | 6 | 2016-02-24T03:08:22.000Z | 2022-03-10T18:57:19.000Z | 98.582609 | 392 | 0.643081 | true | 3,747 | Qwen/Qwen-72B | 1. YES
2. YES | 0.890294 | 0.847968 | 0.754941 | __label__eng_Latn | 0.957409 | 0.592312 |
## Rigid body 3 DOF
Devlop a system for a rigid body in 3 DOF and do a simualtion
```python
import warnings
#warnings.filterwarnings('ignore')
%matplotlib inline
%load_ext autoreload
%autoreload 2
```
```python
import sympy as sp
import sympy.physics.mechanics as me
import pandas as pd
import numpy as np
import mat... | ec8f612289f62823d8df698c3a394bde0c435523 | 84,185 | ipynb | Jupyter Notebook | rigid_body_3DOF.ipynb | axelande/rigidbodysimulator | a87c3eb3b7978ef01efca15e66a6de6518870cd8 | [
"MIT"
] | null | null | null | rigid_body_3DOF.ipynb | axelande/rigidbodysimulator | a87c3eb3b7978ef01efca15e66a6de6518870cd8 | [
"MIT"
] | 1 | 2020-10-26T19:47:02.000Z | 2020-10-26T19:47:02.000Z | rigid_body_3DOF.ipynb | axelande/rigidbodysimulator | a87c3eb3b7978ef01efca15e66a6de6518870cd8 | [
"MIT"
] | 1 | 2020-10-26T09:17:00.000Z | 2020-10-26T09:17:00.000Z | 116.117241 | 17,920 | 0.869763 | true | 2,216 | Qwen/Qwen-72B | 1. YES
2. YES | 0.849971 | 0.771844 | 0.656045 | __label__eng_Latn | 0.195212 | 0.362543 |
# Kernel Design
It's easy to make new kernels in GPflow. To demonstrate, we'll have a look at the Brownian motion kernel, whose function is
\begin{equation}
k(x, x') = \sigma^2 \text{min}(x, x')
\end{equation}
where $\sigma^2$ is a variance parameter.
```python
import gpflow
import numpy as np
import matplotlib.pypl... | 603a5190b16c964e18dc727f427e2cc638288e03 | 92,247 | ipynb | Jupyter Notebook | doc/source/notebooks/tailor/kernel_design.ipynb | paulinavaso/docs | afd2fa1742a743b3faf237b76811a93b5caf9936 | [
"Apache-2.0"
] | null | null | null | doc/source/notebooks/tailor/kernel_design.ipynb | paulinavaso/docs | afd2fa1742a743b3faf237b76811a93b5caf9936 | [
"Apache-2.0"
] | null | null | null | doc/source/notebooks/tailor/kernel_design.ipynb | paulinavaso/docs | afd2fa1742a743b3faf237b76811a93b5caf9936 | [
"Apache-2.0"
] | null | null | null | 308.518395 | 59,624 | 0.917959 | true | 1,513 | Qwen/Qwen-72B | 1. YES
2. YES | 0.904651 | 0.822189 | 0.743794 | __label__eng_Latn | 0.969398 | 0.566414 |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W1D3-ModelFitting/W1D3_Tutorial4.ipynb" target="_parent"></a>
# Neuromatch Academy: Week 1, Day 3, Tutorial 4
# Model Fitting: Multiple linear regression
#Tutorial Objectives
This is Tutorial 4 of a series on fi... | 9391b20a84a1cdd86bd2166b9341aef2c296a0d3 | 384,800 | ipynb | Jupyter Notebook | tutorials/W1D3_ModelFitting/hyo_W1D3_Tutorial4.ipynb | hyosubkim/course-content | 30370131c42fd3bf4f84c50e9c4eaf19f3193165 | [
"CC-BY-4.0"
] | null | null | null | tutorials/W1D3_ModelFitting/hyo_W1D3_Tutorial4.ipynb | hyosubkim/course-content | 30370131c42fd3bf4f84c50e9c4eaf19f3193165 | [
"CC-BY-4.0"
] | null | null | null | tutorials/W1D3_ModelFitting/hyo_W1D3_Tutorial4.ipynb | hyosubkim/course-content | 30370131c42fd3bf4f84c50e9c4eaf19f3193165 | [
"CC-BY-4.0"
] | null | null | null | 741.425819 | 338,020 | 0.95171 | true | 2,112 | Qwen/Qwen-72B | 1. YES
2. YES | 0.855851 | 0.721743 | 0.617705 | __label__eng_Latn | 0.951848 | 0.273466 |
# SVM
```python
import numpy as np
import sympy as sym
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
%matplotlib inline
np.random.seed(1)
```
## Simple Example Application
对于简单的数据样本例子(也就是说可以进行线性划分,且不包含噪声点)
**算法:**
... | 4adc355774bba553146fd29ec28d2bdccc339d2c | 229,148 | ipynb | Jupyter Notebook | 5-3 Support vector machines(Application01).ipynb | woaij100/Classic_machine_learning | 3bb29f5b7449f11270014184d999171a1c7f5e71 | [
"Apache-2.0"
] | 77 | 2018-12-14T02:09:06.000Z | 2020-03-07T03:47:22.000Z | 5-3 Support vector machines(Application01).ipynb | woaij100/Classic_machine_learning | 3bb29f5b7449f11270014184d999171a1c7f5e71 | [
"Apache-2.0"
] | null | null | null | 5-3 Support vector machines(Application01).ipynb | woaij100/Classic_machine_learning | 3bb29f5b7449f11270014184d999171a1c7f5e71 | [
"Apache-2.0"
] | 10 | 2019-03-05T09:50:55.000Z | 2019-08-07T01:37:45.000Z | 161.371831 | 158,104 | 0.877909 | true | 10,082 | Qwen/Qwen-72B | 1. YES
2. YES | 0.803174 | 0.699254 | 0.561623 | __label__eng_Latn | 0.26224 | 0.143168 |
```python
%matplotlib inline
```
Bad key "text.kerning_factor" on line 4 in
C:\Users\sensio\miniconda3\lib\site-packages\matplotlib\mpl-data\stylelib\_classic_test_patch.mplstyle.
You probably need to get an updated matplotlibrc file from
https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplo... | 16850ce6447851035682c50aa86c2f6c2c76c469 | 134,493 | ipynb | Jupyter Notebook | tutorials/00_how_it_works.ipynb | adantra/nangs | 7d027998cbb225ba2a5972344090e354c5e96480 | [
"Apache-2.0"
] | 1 | 2021-02-22T11:17:22.000Z | 2021-02-22T11:17:22.000Z | tutorials/00_how_it_works.ipynb | adantra/nangs | 7d027998cbb225ba2a5972344090e354c5e96480 | [
"Apache-2.0"
] | null | null | null | tutorials/00_how_it_works.ipynb | adantra/nangs | 7d027998cbb225ba2a5972344090e354c5e96480 | [
"Apache-2.0"
] | 2 | 2020-07-23T09:10:23.000Z | 2021-02-22T11:14:24.000Z | 91.305499 | 20,824 | 0.840356 | true | 3,055 | Qwen/Qwen-72B | 1. YES
2. YES | 0.695958 | 0.760651 | 0.529381 | __label__eng_Latn | 0.426161 | 0.068259 |
```python
from sympy import *
import matplotlib.pyplot as plt
import numpy as np
```
```python
alpha, gamma, a, b, c, d = symbols(
'alpha gamma a b c d', float=True
)
t = Symbol('t')
p = Function('p', is_real = true)(t)
D = Function('D', is_real = true)(p)
S = Function('S', is_real = true)(p)
... | 0524afb82a41270938db12f0ed925ecde792b518 | 133,944 | ipynb | Jupyter Notebook | diffeq/diffeq.ipynb | MilkyCousin/SymPy-and-Mathematics | 2426c42329a8ae938791656001da02c15ec4c6dd | [
"MIT"
] | null | null | null | diffeq/diffeq.ipynb | MilkyCousin/SymPy-and-Mathematics | 2426c42329a8ae938791656001da02c15ec4c6dd | [
"MIT"
] | null | null | null | diffeq/diffeq.ipynb | MilkyCousin/SymPy-and-Mathematics | 2426c42329a8ae938791656001da02c15ec4c6dd | [
"MIT"
] | null | null | null | 252.724528 | 54,384 | 0.866474 | true | 5,841 | Qwen/Qwen-72B | 1. YES
2. YES | 0.896251 | 0.766294 | 0.686792 | __label__kor_Hang | 0.089077 | 0.433979 |
#Coloring
### Cartesian Line Plot
```
from sympy.plotting import plot, plot_parametric, plot3d, plot3d_parametric_line, plot3d_parametric_surface
```
```
p = plot(sin(x))
```
If the `line_color` aesthetic is a function of arity 1 then the coloring is a function of the x value of a point.
```
p[0].line_color... | 4f661a0ada771f7d1942353c75686ef3e1a87873 | 7,379 | ipynb | Jupyter Notebook | examples/beginner/plot_colors.ipynb | Michal-Gagala/sympy | 3cc756c2af73b5506102abaeefd1b654e286e2c8 | [
"MIT"
] | null | null | null | examples/beginner/plot_colors.ipynb | Michal-Gagala/sympy | 3cc756c2af73b5506102abaeefd1b654e286e2c8 | [
"MIT"
] | null | null | null | examples/beginner/plot_colors.ipynb | Michal-Gagala/sympy | 3cc756c2af73b5506102abaeefd1b654e286e2c8 | [
"MIT"
] | null | null | null | 20.497222 | 122 | 0.404933 | true | 715 | Qwen/Qwen-72B | 1. YES
2. YES | 0.92079 | 0.882428 | 0.81253 | __label__eng_Latn | 0.813288 | 0.726113 |
Jupyter Notebook desenvolvido por [Gustavo S.S.](https://github.com/GSimas)
> "Na ciência, o crédito vai para o homem que convence o mundo,
não para o que primeiro teve a ideia" - Francis Darwin
# Capacitores e Indutores
**Contrastando com um resistor,
que gasta ou dissipa energia de
forma irreversível, um indutor o... | da2afa39abf83da4b6c06dc9bcdeb547dd9e6d9d | 19,853 | ipynb | Jupyter Notebook | Aula 9.1 - Capacitores.ipynb | ofgod2/Circuitos-electricos-Boylestad-12ed-Portugues | 60e815f6904858f3cda8b5c7ead8ea77aa09c7fd | [
"MIT"
] | 7 | 2019-08-13T13:33:15.000Z | 2021-11-16T16:46:06.000Z | Aula 9.1 - Capacitores.ipynb | ofgod2/Circuitos-electricos-Boylestad-12ed-Portugues | 60e815f6904858f3cda8b5c7ead8ea77aa09c7fd | [
"MIT"
] | 1 | 2017-08-24T17:36:15.000Z | 2017-08-24T17:36:15.000Z | Aula 9.1 - Capacitores.ipynb | ofgod2/Circuitos-electricos-Boylestad-12ed-Portugues | 60e815f6904858f3cda8b5c7ead8ea77aa09c7fd | [
"MIT"
] | 8 | 2019-03-29T14:31:49.000Z | 2021-12-30T17:59:23.000Z | 25.616774 | 302 | 0.501033 | true | 4,285 | Qwen/Qwen-72B | 1. YES
2. YES | 0.763484 | 0.779993 | 0.595512 | __label__por_Latn | 0.98881 | 0.221904 |
```python
import numpy as np
from numba import jit
import sympy
```
# Item XV
Considering the following inner product:
$$
\langle p(x),q(x) \rangle =\int_{-1}^{1} \overline{p(x)}q(x) dx
$$
* Let $A= [1|x|x^2|...|x^{n-1}]$ be the "matrix" whose "columns" are the monomials $x^j$, for $j=0,...,n-1$. Each column is a fu... | fa71e5ccd097a78ee7d3fd81671e651a77ae028a | 43,673 | ipynb | Jupyter Notebook | t1_questions/item_15.ipynb | autopawn/cc5-works | 63775574c82da85ed0e750a4d6978a071096f6e7 | [
"MIT"
] | null | null | null | t1_questions/item_15.ipynb | autopawn/cc5-works | 63775574c82da85ed0e750a4d6978a071096f6e7 | [
"MIT"
] | null | null | null | t1_questions/item_15.ipynb | autopawn/cc5-works | 63775574c82da85ed0e750a4d6978a071096f6e7 | [
"MIT"
] | null | null | null | 58.308411 | 2,572 | 0.470794 | true | 14,003 | Qwen/Qwen-72B | 1. YES
2. YES | 0.843895 | 0.822189 | 0.693841 | __label__yue_Hant | 0.127795 | 0.450357 |
# Finding Roots of Equations
## Calculus review
```python
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy as scipy
from scipy.interpolate import interp1d
```
Let's review the theory of optimization for multivariate functions. Recall that in the single-variable case, extreme values... | 509c810d8ef9496a287bfb5908550168541e93ba | 31,540 | ipynb | Jupyter Notebook | notebooks/T07B_Root_Finding.ipynb | Yijia17/sta-663-2021 | e6484e3116c041b8c8eaae487eff5f351ff499c9 | [
"MIT"
] | 18 | 2021-01-19T16:35:54.000Z | 2022-01-01T02:12:30.000Z | notebooks/T07B_Root_Finding.ipynb | Yijia17/sta-663-2021 | e6484e3116c041b8c8eaae487eff5f351ff499c9 | [
"MIT"
] | null | null | null | notebooks/T07B_Root_Finding.ipynb | Yijia17/sta-663-2021 | e6484e3116c041b8c8eaae487eff5f351ff499c9 | [
"MIT"
] | 24 | 2021-01-19T16:26:13.000Z | 2022-03-15T05:10:14.000Z | 28.93578 | 798 | 0.522036 | true | 6,676 | Qwen/Qwen-72B | 1. YES
2. YES | 0.927363 | 0.936285 | 0.868276 | __label__eng_Latn | 0.956898 | 0.85563 |
```
import scipy.stats as stats
figsize( 12.5, 4)
```
#Chapter 4
______
##The greatest theorem never told
> This relatively short chapter focuses on an idea that is always bouncing around our heads, but is rarely made explicit outside books devoted to statistics or Monte Carlo. In fact, we've been used this idea ... | 4e9b2bb23a8913864a16dba23f84fe25c66dfbaa | 409,956 | ipynb | Jupyter Notebook | Chapter4_TheGreatestTheoremNeverTold/LawOfLargeNumbers.ipynb | bzillins/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | c08a6344b8d0e39fcdb9702913b46e1b4e33fb9a | [
"MIT"
] | 1 | 2019-05-20T10:54:19.000Z | 2019-05-20T10:54:19.000Z | Chapter4_TheGreatestTheoremNeverTold/LawOfLargeNumbers.ipynb | bzillins/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | c08a6344b8d0e39fcdb9702913b46e1b4e33fb9a | [
"MIT"
] | null | null | null | Chapter4_TheGreatestTheoremNeverTold/LawOfLargeNumbers.ipynb | bzillins/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | c08a6344b8d0e39fcdb9702913b46e1b4e33fb9a | [
"MIT"
] | null | null | null | 673.162562 | 129,484 | 0.926502 | true | 5,343 | Qwen/Qwen-72B | 1. YES
2. YES | 0.757794 | 0.909907 | 0.689522 | __label__eng_Latn | 0.994978 | 0.440323 |
# Fitting a Morse Diatomic Absorption spectrum with a non-Condon Moment
In these spectroscopy calculations, we are given $\omega_e$, $\chi_e \omega_e$, the reduced mass $\mu$ and the equilibrium position $r_e$. For each atom, we want to create a system of units out of these.
\begin{align}
h &= A \cdot e_u\cdot T... | 0f2be000c759e8751f1964475cf86dc53943247a | 94,428 | ipynb | Jupyter Notebook | DetectingNonCondonPaper/code/.ipynb_checkpoints/Morse_fitting_procedure-checkpoint.ipynb | jgoodknight/dissertation | 012ad400e1246d2a7e63cc640be4f7b4bf56db00 | [
"MIT"
] | 1 | 2020-04-21T06:20:42.000Z | 2020-04-21T06:20:42.000Z | DetectingNonCondonPaper/code/.ipynb_checkpoints/Morse_fitting_procedure-checkpoint.ipynb | jgoodknight/dissertation | 012ad400e1246d2a7e63cc640be4f7b4bf56db00 | [
"MIT"
] | null | null | null | DetectingNonCondonPaper/code/.ipynb_checkpoints/Morse_fitting_procedure-checkpoint.ipynb | jgoodknight/dissertation | 012ad400e1246d2a7e63cc640be4f7b4bf56db00 | [
"MIT"
] | null | null | null | 203.070968 | 31,662 | 0.86737 | true | 2,769 | Qwen/Qwen-72B | 1. YES
2. YES | 0.803174 | 0.731059 | 0.587167 | __label__eng_Latn | 0.488734 | 0.202516 |
```python
from resources.workspace import *
```
### The Gaussian (i.e. Normal) distribution
Consider the random variable with a Gaussian distribution with mean $\mu$ (`mu`) and variance $P$. We write its probability density function (**pdf**) as
$$ p(x) = N(x|\mu,P) = (2 \pi P)^{-1/2} e^{-(x-\mu)^2/2P} \, . \qquad \... | 683d16b911b89bf2f38a76409eb785a893853ca4 | 17,449 | ipynb | Jupyter Notebook | tutorials/T2 - Bayesian inference.ipynb | geirev/DAPPER | c3f448a1912f3869eccdbd86fb24019655efcb4f | [
"MIT"
] | 1 | 2021-02-02T05:56:31.000Z | 2021-02-02T05:56:31.000Z | tutorials/T2 - Bayesian inference.ipynb | JIMMY-KSU/DAPPER | c3f448a1912f3869eccdbd86fb24019655efcb4f | [
"MIT"
] | null | null | null | tutorials/T2 - Bayesian inference.ipynb | JIMMY-KSU/DAPPER | c3f448a1912f3869eccdbd86fb24019655efcb4f | [
"MIT"
] | 1 | 2021-02-02T05:56:35.000Z | 2021-02-02T05:56:35.000Z | 31.214669 | 411 | 0.560032 | true | 3,183 | Qwen/Qwen-72B | 1. YES
2. YES | 0.7773 | 0.927363 | 0.720839 | __label__eng_Latn | 0.980611 | 0.513083 |
<a href="https://colab.research.google.com/github/cstorm125/abtestoo/blob/master/notebooks/frequentist_colab.ipynb" target="_parent"></a>
# A/B Testing from Scratch: Frequentist Approach
Frequentist A/B testing is one of the most used and abused statistical methods in the world. This article starts with a simple prob... | 9554054d426a2f030aa9dc956d697da4c957f474 | 967,004 | ipynb | Jupyter Notebook | notebooks/frequentist_colab.ipynb | TeamTamoad/abtestoo | 90e903ddbe945034b8226aad05a74fb46efb5326 | [
"Apache-2.0"
] | 1 | 2021-08-06T14:43:20.000Z | 2021-08-06T14:43:20.000Z | notebooks/frequentist_colab.ipynb | TeamTamoad/abtestoo | 90e903ddbe945034b8226aad05a74fb46efb5326 | [
"Apache-2.0"
] | null | null | null | notebooks/frequentist_colab.ipynb | TeamTamoad/abtestoo | 90e903ddbe945034b8226aad05a74fb46efb5326 | [
"Apache-2.0"
] | null | null | null | 390.866613 | 69,406 | 0.914276 | true | 17,682 | Qwen/Qwen-72B | 1. YES
2. YES | 0.868827 | 0.805632 | 0.699955 | __label__eng_Latn | 0.981288 | 0.464561 |
# Chapter 5
# Numerical Integration and Differentiation
In many computational economic applications, one must compute the definite integral
of a real-valued function f with respect to a "weighting" function w over an interval
$I$ of $R^n$:
$$\int_I f(x)w(x) dx$$
The weighting function may be the identity, $w = 1$, ... | b3c74322e8d98bf2e89a951e6e2b6a87d1c06c2d | 38,778 | ipynb | Jupyter Notebook | Chapter05.ipynb | lnsongxf/Applied_Computational_Economics_and_Finance | f14661bfbfa711d49539bda290d4be5a25087185 | [
"MIT"
] | 19 | 2018-05-09T08:17:44.000Z | 2021-12-26T07:02:17.000Z | Chapter05.ipynb | lnsongxf/Applied_Computational_Economics_and_Finance | f14661bfbfa711d49539bda290d4be5a25087185 | [
"MIT"
] | null | null | null | Chapter05.ipynb | lnsongxf/Applied_Computational_Economics_and_Finance | f14661bfbfa711d49539bda290d4be5a25087185 | [
"MIT"
] | 11 | 2017-12-15T13:39:35.000Z | 2021-05-15T15:06:02.000Z | 29.399545 | 173 | 0.522642 | true | 6,457 | Qwen/Qwen-72B | 1. YES
2. YES | 0.879147 | 0.843895 | 0.741908 | __label__eng_Latn | 0.983375 | 0.562032 |
# Classification using NAG Second-order Conic Programming via CVXPY
## Correct Rendering of this notebook
This notebook makes use of the `latex_envs` Jupyter extension for equations and references. If the LaTeX is not rendering properly in your local installation of Jupyter , it may be because you have not installed... | 0ae1273a1c42309281bd95f3e445416e2a3c6766 | 63,311 | ipynb | Jupyter Notebook | local_optimization/SOCP/cvxpy_classification.ipynb | Brunochris13/NAGPythonExamples | e57fc05ab9b27db66d06a52f9b9412205e984544 | [
"BSD-3-Clause"
] | 40 | 2018-12-06T20:20:01.000Z | 2022-03-05T23:09:31.000Z | local_optimization/SOCP/cvxpy_classification.ipynb | kelly1208/NAGPythonExamples | bd20f719c176bbbbc878fea7d0962e5fa10d9d3e | [
"BSD-3-Clause"
] | 11 | 2019-03-25T11:52:51.000Z | 2021-04-12T14:08:31.000Z | local_optimization/SOCP/cvxpy_classification.ipynb | kelly1208/NAGPythonExamples | bd20f719c176bbbbc878fea7d0962e5fa10d9d3e | [
"BSD-3-Clause"
] | 21 | 2019-01-22T13:30:57.000Z | 2021-12-15T13:05:14.000Z | 154.794621 | 29,616 | 0.854828 | true | 3,284 | Qwen/Qwen-72B | 1. YES
2. YES | 0.868827 | 0.819893 | 0.712345 | __label__eng_Latn | 0.649977 | 0.493348 |
# Exponentials, Radicals, and Logs
Up to this point, all of our equations have included standard arithmetic operations, such as division, multiplication, addition, and subtraction. Many real-world calculations involve exponential values in which numbers are raised by a specific power.
## Exponentials
A simple case of ... | 07a2862422e06b9505a6e51f4e3e9b9e94883028 | 55,729 | ipynb | Jupyter Notebook | Basics Of Algebra by Hiren/01-04-Exponentials Radicals and Logarithms.ipynb | serkin/Basic-Mathematics-for-Machine-Learning | ac0ae9fad82a9f0429c93e3da744af6e6d63e5ab | [
"Apache-2.0"
] | null | null | null | Basics Of Algebra by Hiren/01-04-Exponentials Radicals and Logarithms.ipynb | serkin/Basic-Mathematics-for-Machine-Learning | ac0ae9fad82a9f0429c93e3da744af6e6d63e5ab | [
"Apache-2.0"
] | null | null | null | Basics Of Algebra by Hiren/01-04-Exponentials Radicals and Logarithms.ipynb | serkin/Basic-Mathematics-for-Machine-Learning | ac0ae9fad82a9f0429c93e3da744af6e6d63e5ab | [
"Apache-2.0"
] | null | null | null | 103.778399 | 14,720 | 0.834826 | true | 3,193 | Qwen/Qwen-72B | 1. YES
2. YES | 0.96378 | 0.959762 | 0.924999 | __label__eng_Latn | 0.999242 | 0.987418 |
# Simulating readout noise on the Rigetti Quantum Virtual Machine
© Copyright 2018, Rigetti Computing.
$$
\newcommand{ket}[1]{\left|{#1}\right\rangle}
\newcommand{bra}[1]{\left\langle {#1}\right|}
\newcommand{tr}[1]{\mathrm{Tr}\,\left[ {#1}\right]}
\newcommand{expect}[1]{\left\langle {#1} \right \rangle}
$$
## Theore... | 31c8b2fb968b03a65e249bdbd9eb6d0b8b289698 | 19,758 | ipynb | Jupyter Notebook | examples/ReadoutNoise.ipynb | oliverdutton/pyquil | 027a3f6aecbd8206baf39189a0183ad0f85c262b | [
"Apache-2.0"
] | 1 | 2021-01-30T18:47:34.000Z | 2021-01-30T18:47:34.000Z | examples/ReadoutNoise.ipynb | abhayshivamtiwari/pyquil | 854bf41349393beeeedad7a4481797ad78ae36a5 | [
"Apache-2.0"
] | null | null | null | examples/ReadoutNoise.ipynb | abhayshivamtiwari/pyquil | 854bf41349393beeeedad7a4481797ad78ae36a5 | [
"Apache-2.0"
] | null | null | null | 38.439689 | 565 | 0.595708 | true | 4,182 | Qwen/Qwen-72B | 1. YES
2. YES | 0.766294 | 0.718594 | 0.550654 | __label__eng_Latn | 0.976972 | 0.117684 |
# Chapter 3 - Developing Templates
Generating SoftMax distributions from normals could get quite tedious – for any sufficiently complicated shape, the number of normals to be used could be excessive. Let's add a layer of abstraction onto all our work.
##Polygon Construction
We can put everything together from all we... | 27188c0b99a49b02081ae5750b6b1b11536715b8 | 847,579 | ipynb | Jupyter Notebook | resources/notebooks/softmax/03_from_templates.ipynb | COHRINT/cops_and_robots | 1df99caa1e38bde1b5ce2d04389bc232a68938d6 | [
"Apache-2.0"
] | 3 | 2016-01-19T17:54:51.000Z | 2019-10-21T12:09:03.000Z | resources/notebooks/softmax/03_from_templates.ipynb | COHRINT/cops_and_robots | 1df99caa1e38bde1b5ce2d04389bc232a68938d6 | [
"Apache-2.0"
] | null | null | null | resources/notebooks/softmax/03_from_templates.ipynb | COHRINT/cops_and_robots | 1df99caa1e38bde1b5ce2d04389bc232a68938d6 | [
"Apache-2.0"
] | 5 | 2015-02-19T02:53:24.000Z | 2019-03-05T20:29:12.000Z | 1,862.810989 | 214,532 | 0.946475 | true | 3,404 | Qwen/Qwen-72B | 1. YES
2. YES | 0.882428 | 0.857768 | 0.756918 | __label__eng_Latn | 0.967133 | 0.596907 |
# 01 Molecular Geometry Analysis
The purpose of this project is to introduce you to fundamental Python programming techniques in the context of a scientific problem, viz. the calculation of the internal coordinates (bond lengths, bond angles, dihedral angles), moments of inertia, and rotational constants of a polyatom... | a5f3c03ef8f3371331441de02bd00327ffc2fc98 | 66,041 | ipynb | Jupyter Notebook | source/Project_01/Project_01.ipynb | ajz34/PyCrawfordProgProj | d2ba51223a4e6e56deefc5c0d68aa4e663fbcd80 | [
"Apache-2.0"
] | 13 | 2020-08-13T06:59:08.000Z | 2022-03-21T15:48:09.000Z | source/Project_01/Project_01.ipynb | ajz34/PyCrawfordProgProj | d2ba51223a4e6e56deefc5c0d68aa4e663fbcd80 | [
"Apache-2.0"
] | null | null | null | source/Project_01/Project_01.ipynb | ajz34/PyCrawfordProgProj | d2ba51223a4e6e56deefc5c0d68aa4e663fbcd80 | [
"Apache-2.0"
] | 3 | 2021-04-26T03:28:48.000Z | 2021-09-06T21:04:07.000Z | 32.388916 | 617 | 0.506473 | true | 16,664 | Qwen/Qwen-72B | 1. YES
2. YES | 0.63341 | 0.822189 | 0.520783 | __label__eng_Latn | 0.783726 | 0.048283 |
```python
from sympy.physics.mechanics import *
import sympy as sp
mechanics_printing(pretty_print=True)
```
```python
m, M, l = sp.symbols(r'm M l')
t, g = sp.symbols('t g')
r, v = dynamicsymbols(r'r \theta')
dr, dv = dynamicsymbols(r'r \theta', 1)
```
```python
x = r*sp.sin(v)
y = -r*sp.cos(v)
X = sp.Rational(0,1... | 094f6e0a5729b9b1c9965dbd38372ba3164f2cfe | 31,154 | ipynb | Jupyter Notebook | Pendula/Misc/PendulumHangingMass/PendulumHangingMass.ipynb | ethank5149/Classical-Mechanics | 4684cc91abcf65a684237c6ec21246d5cebd232a | [
"MIT"
] | null | null | null | Pendula/Misc/PendulumHangingMass/PendulumHangingMass.ipynb | ethank5149/Classical-Mechanics | 4684cc91abcf65a684237c6ec21246d5cebd232a | [
"MIT"
] | null | null | null | Pendula/Misc/PendulumHangingMass/PendulumHangingMass.ipynb | ethank5149/Classical-Mechanics | 4684cc91abcf65a684237c6ec21246d5cebd232a | [
"MIT"
] | null | null | null | 230.77037 | 14,516 | 0.692784 | true | 286 | Qwen/Qwen-72B | 1. YES
2. YES | 0.946597 | 0.76908 | 0.728009 | __label__yue_Hant | 0.264964 | 0.52974 |
# Importing and reading data
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from scipy import integrate
import seaborn as sns; sns.set()
```
```python
# be sure to git pull upstream master before reading the data so it is up to date.
DATA_URL = 'https://raw.gi... | 8fb64696e6fdaf84581ada553b53b63ab96e84b2 | 417,623 | ipynb | Jupyter Notebook | covid_SEIRD_coupled_model.ipynb | aguirreFabian/COVID-19_Coupled-Epidemics | 3e64e1c66399bcbffa70605c219bb633819ff90c | [
"MIT"
] | null | null | null | covid_SEIRD_coupled_model.ipynb | aguirreFabian/COVID-19_Coupled-Epidemics | 3e64e1c66399bcbffa70605c219bb633819ff90c | [
"MIT"
] | null | null | null | covid_SEIRD_coupled_model.ipynb | aguirreFabian/COVID-19_Coupled-Epidemics | 3e64e1c66399bcbffa70605c219bb633819ff90c | [
"MIT"
] | null | null | null | 429.653292 | 67,012 | 0.930715 | true | 7,443 | Qwen/Qwen-72B | 1. YES
2. YES | 0.880797 | 0.833325 | 0.73399 | __label__eng_Latn | 0.905681 | 0.543636 |
# Probabilidad II
# 1. Cadenas de Markov
Una cadena de Markov es un proceso aleatorio con la propiedad de Markov. Un proceso aleatorio o estocástico, es un objeto matemático definido como una colección de variables aleatorias. Una cadena de Markov tiene ya sea un espacio de estado discreto (que representaría posible... | 482de24e930c526b1d791ee0057c7b922271c7ae | 36,697 | ipynb | Jupyter Notebook | 7Estadistica/4_ProbabilidadII.ipynb | sergiogaitan/Study_Guides | 083acd23f5faa6c6bc404d4d53df562096478e7c | [
"MIT"
] | 5 | 2020-09-12T17:16:12.000Z | 2021-02-03T01:37:02.000Z | 7Estadistica/4_ProbabilidadII.ipynb | sergiogaitan/Study_Guides | 083acd23f5faa6c6bc404d4d53df562096478e7c | [
"MIT"
] | null | null | null | 7Estadistica/4_ProbabilidadII.ipynb | sergiogaitan/Study_Guides | 083acd23f5faa6c6bc404d4d53df562096478e7c | [
"MIT"
] | 4 | 2020-05-22T12:57:49.000Z | 2021-02-03T01:37:07.000Z | 69.239623 | 6,916 | 0.727961 | true | 4,080 | Qwen/Qwen-72B | 1. YES
2. YES | 0.908618 | 0.699254 | 0.635355 | __label__spa_Latn | 0.967806 | 0.314473 |
# Lecture 16
## Systems of Differential Equations III:
### Phase Planes and Stability
```python
import numpy as np
import sympy as sp
import scipy.integrate
sp.init_printing()
##################################################
##### Matplotlib boilerplate for consistency #####
######################################... | 115e0f9f1748ae1b3928b90d1a13318d2a551dac | 18,315 | ipynb | Jupyter Notebook | lectures/lecture-16-systems3.ipynb | SABS-R3/2020-essential-maths | 5a53d60f1e8fdc04b7bb097ec15800a89f67a047 | [
"Apache-2.0"
] | 1 | 2021-11-27T12:07:13.000Z | 2021-11-27T12:07:13.000Z | lectures/lecture-16-systems3.ipynb | SABS-R3/2021-essential-maths | 8a81449928e602b51a4a4172afbcd70a02e468b8 | [
"Apache-2.0"
] | null | null | null | lectures/lecture-16-systems3.ipynb | SABS-R3/2021-essential-maths | 8a81449928e602b51a4a4172afbcd70a02e468b8 | [
"Apache-2.0"
] | 1 | 2020-10-30T17:34:52.000Z | 2020-10-30T17:34:52.000Z | 29.780488 | 247 | 0.47573 | true | 3,897 | Qwen/Qwen-72B | 1. YES
2. YES | 0.721743 | 0.853913 | 0.616306 | __label__eng_Latn | 0.773 | 0.270215 |
```python
import sympy as sym
x, L, C, D, c_0, c_1, = sym.symbols('x L C D c_0 c_1')
class TwoPtBoundaryValueProblem(object):
"""
Solve -(a*u')' = f(x) with boundary conditions
specified in subclasses (method get_bc).
a and f must be sympy expressions of x.
"""
def __init__(self, f, a=1, L=L, C... | 657967460713beec73ca454049309c136289d52a | 5,528 | ipynb | Jupyter Notebook | Data Science and Machine Learning/Machine-Learning-In-Python-THOROUGH/EXAMPLES/FINITE_ELEMENTS/INTRO/EXERCICES/27_U_XX_F_SYMPY_CLASS.ipynb | okara83/Becoming-a-Data-Scientist | f09a15f7f239b96b77a2f080c403b2f3e95c9650 | [
"MIT"
] | null | null | null | Data Science and Machine Learning/Machine-Learning-In-Python-THOROUGH/EXAMPLES/FINITE_ELEMENTS/INTRO/EXERCICES/27_U_XX_F_SYMPY_CLASS.ipynb | okara83/Becoming-a-Data-Scientist | f09a15f7f239b96b77a2f080c403b2f3e95c9650 | [
"MIT"
] | null | null | null | Data Science and Machine Learning/Machine-Learning-In-Python-THOROUGH/EXAMPLES/FINITE_ELEMENTS/INTRO/EXERCICES/27_U_XX_F_SYMPY_CLASS.ipynb | okara83/Becoming-a-Data-Scientist | f09a15f7f239b96b77a2f080c403b2f3e95c9650 | [
"MIT"
] | 2 | 2022-02-09T15:41:33.000Z | 2022-02-11T07:47:40.000Z | 34.55 | 98 | 0.446454 | true | 1,268 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.810479 | 0.719061 | __label__eng_Latn | 0.437995 | 0.50895 |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W1D5_DimensionalityReduction/student/W1D5_Tutorial1.ipynb" target="_parent"></a>
# Neuromatch Academy: Week 1, Day 5, Tutorial 1
# Dimensionality Reduction: Geometric view of data
---
Tutorial objectives
In thi... | c5bcbaef8f255135721a0464f140922323571515 | 353,246 | ipynb | Jupyter Notebook | tutorials/W1D5_DimensionalityReduction/student/W1D5_Tutorial1.ipynb | liuxiaomiao123/NeuroMathAcademy | 16a7969604a300bf9fbb86f8a5b26050ebd14c65 | [
"CC-BY-4.0"
] | 2 | 2020-07-03T04:39:09.000Z | 2020-07-12T02:08:31.000Z | tutorials/W1D5_DimensionalityReduction/student/W1D5_Tutorial1.ipynb | NinaHKivanani/course-content | 3c91dd1a669cebce892486ba4f8086b1ef2e1e49 | [
"CC-BY-4.0"
] | 1 | 2020-06-22T22:57:03.000Z | 2020-06-22T22:57:03.000Z | tutorials/W1D5_DimensionalityReduction/student/W1D5_Tutorial1.ipynb | NinaHKivanani/course-content | 3c91dd1a669cebce892486ba4f8086b1ef2e1e49 | [
"CC-BY-4.0"
] | 1 | 2021-08-06T08:05:01.000Z | 2021-08-06T08:05:01.000Z | 285.10573 | 128,820 | 0.919037 | true | 4,467 | Qwen/Qwen-72B | 1. YES
2. YES | 0.760651 | 0.815232 | 0.620107 | __label__eng_Latn | 0.934982 | 0.279047 |
# Optimizer tweaks
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
```
```
#export
from exp.nb_08 import *
```
## Imagenette data
We grab the data from the previous notebook.
```
path = datasets.untar_data(datasets.URLs.IMAGENETTE_160)
```
```
tfms = [make_rgb, ResizeFixed(128), to_byte_tensor, to_... | b4052e532d6250a1a265d51f1a61eaa7a9e682ea | 410,968 | ipynb | Jupyter Notebook | dev_course/dl2/09_optimizers.ipynb | rohitgr7/fastai_docs | 531139ac17dd2e0cf08a99b6f894dbca5028e436 | [
"Apache-2.0"
] | null | null | null | dev_course/dl2/09_optimizers.ipynb | rohitgr7/fastai_docs | 531139ac17dd2e0cf08a99b6f894dbca5028e436 | [
"Apache-2.0"
] | null | null | null | dev_course/dl2/09_optimizers.ipynb | rohitgr7/fastai_docs | 531139ac17dd2e0cf08a99b6f894dbca5028e436 | [
"Apache-2.0"
] | null | null | null | 324.363062 | 114,928 | 0.93098 | true | 6,065 | Qwen/Qwen-72B | 1. YES
2. YES | 0.743168 | 0.721743 | 0.536376 | __label__eng_Latn | 0.910782 | 0.084512 |
# La méthode des multiplicateurs de Lagrange
**TODO**:
* https://www.google.fr/webhp?ie=utf-8&oe=utf-8&client=firefox-b&gfe_rd=cr&ei=kutIWYeiKoXS8Afc25yQBQ#safe=active&q=m%C3%A9thode+des+multiplicateurs+de+lagrange
## À quoi ça sert ?
À trouver les extremums (minimums, maximums) d'une fonction $f$ d'une ou plusieurs... | 258987577b72b7043ccba2f0bf0b725447132fa3 | 23,984 | ipynb | Jupyter Notebook | nb_sci_maths/maths_analysis_method_of_lagrange_multipliers_fr.ipynb | jdhp-docs/python-notebooks | 91a97ea5cf374337efa7409e4992ea3f26b99179 | [
"MIT"
] | 3 | 2017-05-03T12:23:36.000Z | 2020-10-26T17:30:56.000Z | nb_sci_maths/maths_analysis_method_of_lagrange_multipliers_fr.ipynb | jdhp-docs/python-notebooks | 91a97ea5cf374337efa7409e4992ea3f26b99179 | [
"MIT"
] | null | null | null | nb_sci_maths/maths_analysis_method_of_lagrange_multipliers_fr.ipynb | jdhp-docs/python-notebooks | 91a97ea5cf374337efa7409e4992ea3f26b99179 | [
"MIT"
] | 1 | 2020-10-26T17:30:57.000Z | 2020-10-26T17:30:57.000Z | 31.188557 | 278 | 0.472565 | true | 5,498 | Qwen/Qwen-72B | 1. YES
2. YES | 0.841826 | 0.824462 | 0.694053 | __label__fra_Latn | 0.38063 | 0.450849 |
最初に必要なライブラリを読み込みます。
```python
from sympy import *
from sympy.physics.quantum import *
from sympy.physics.quantum.qubit import Qubit, QubitBra, measure_all, measure_all_oneshot,measure_partial
from sympy.physics.quantum.gate import H,X,Y,Z,S,T,CPHASE,CNOT,SWAP,UGate,CGateS,gate_simp
from sympy.physics.quantum.gate imp... | 244347152194429d97d83a61b6be49ae2213c7f8 | 292,758 | ipynb | Jupyter Notebook | docs/20190614/sympy_programming_4a_handout.ipynb | kyamaz/openql-notes | 03ad81b595e4ad24b3130bfc0d999fe8ee0d6c70 | [
"Apache-2.0"
] | 4 | 2018-02-19T10:01:43.000Z | 2022-01-12T12:32:34.000Z | docs/20190614/sympy_programming_4a_handout.ipynb | kyamaz/openql-notes | 03ad81b595e4ad24b3130bfc0d999fe8ee0d6c70 | [
"Apache-2.0"
] | null | null | null | docs/20190614/sympy_programming_4a_handout.ipynb | kyamaz/openql-notes | 03ad81b595e4ad24b3130bfc0d999fe8ee0d6c70 | [
"Apache-2.0"
] | 4 | 2018-02-19T10:06:37.000Z | 2022-01-12T12:42:38.000Z | 186.58891 | 25,608 | 0.855488 | true | 4,995 | Qwen/Qwen-72B | 1. YES
2. YES | 0.919643 | 0.824462 | 0.75821 | __label__yue_Hant | 0.50701 | 0.599909 |
# Introduction to Graph Matching
```python
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
```
The graph matching problem (GMP), is meant to find an alignment of nodes between two graphs that minimizes the number of edge disagreements between those two graphs. Therefore, the GMP can be formally... | fa0aed45832dadc3771c7338cfefd896513e665c | 5,456 | ipynb | Jupyter Notebook | docs/tutorials/matching/faq.ipynb | spencer-loggia/graspologic | cf7ae59289faa8f5538e335e2859cc2a843f2839 | [
"MIT"
] | null | null | null | docs/tutorials/matching/faq.ipynb | spencer-loggia/graspologic | cf7ae59289faa8f5538e335e2859cc2a843f2839 | [
"MIT"
] | null | null | null | docs/tutorials/matching/faq.ipynb | spencer-loggia/graspologic | cf7ae59289faa8f5538e335e2859cc2a843f2839 | [
"MIT"
] | null | null | null | 32.094118 | 418 | 0.599707 | true | 953 | Qwen/Qwen-72B | 1. YES
2. YES | 0.936285 | 0.882428 | 0.826204 | __label__eng_Latn | 0.98885 | 0.757882 |
<!-- dom:TITLE: Solving Differential Equations with Deep Learning -->
# Solving Differential Equations with Deep Learning
<!-- dom:AUTHOR: Morten Hjorth-Jensen at Department of Physics, University of Oslo & Department of Physics and Astronomy and Facility for Rare ion Beams, Michigan State University -->
<!-- Author: -... | 8040f3d744ceaabef82a9fb383b22f4d0bcaab66 | 58,116 | ipynb | Jupyter Notebook | doc/src/week43/odenn.ipynb | marlgryd/MachineLearning | e07439cee1f9e3042aec765754116dccdf8bcf01 | [
"CC0-1.0"
] | null | null | null | doc/src/week43/odenn.ipynb | marlgryd/MachineLearning | e07439cee1f9e3042aec765754116dccdf8bcf01 | [
"CC0-1.0"
] | null | null | null | doc/src/week43/odenn.ipynb | marlgryd/MachineLearning | e07439cee1f9e3042aec765754116dccdf8bcf01 | [
"CC0-1.0"
] | 1 | 2021-09-04T16:21:16.000Z | 2021-09-04T16:21:16.000Z | 35.328875 | 350 | 0.552258 | true | 11,558 | Qwen/Qwen-72B | 1. YES
2. YES
| 0.826712 | 0.874077 | 0.72261 | __label__eng_Latn | 0.978416 | 0.517197 |
# Financial Network
**Author**: [Erika Fille Legara](http://www.erikalegara.net/)
You are free to use (or change) this notebook for any purpose you'd like. However, please respect the MIT License that governs its use, and for copying permission.
Copyright © 2016 Erika Fille Legara
---
## Description
I have been rec... | a580ef1e41ca9332c9093da00bdd1b1a9c3a00f7 | 680,015 | ipynb | Jupyter Notebook | Financial Network.ipynb | eflegara/FinancialNetwork | 0ba785a26f20cdae66fa265e1c8df502fc481a79 | [
"MIT"
] | null | null | null | Financial Network.ipynb | eflegara/FinancialNetwork | 0ba785a26f20cdae66fa265e1c8df502fc481a79 | [
"MIT"
] | null | null | null | Financial Network.ipynb | eflegara/FinancialNetwork | 0ba785a26f20cdae66fa265e1c8df502fc481a79 | [
"MIT"
] | 1 | 2020-03-19T00:39:57.000Z | 2020-03-19T00:39:57.000Z | 540.552464 | 221,744 | 0.922803 | true | 8,634 | Qwen/Qwen-72B | 1. YES
2. YES | 0.782662 | 0.817574 | 0.639885 | __label__eng_Latn | 0.540165 | 0.324998 |
# Vertical Line Test
```
import matplotlib.pyplot as plt
import numpy as np
```
## 1.1 Create two graphs, one that passes the vertical line test and one that does not.
```
plt.axhline(y=2)
plt.title("passes the vertical line test")
plt.show()
```
```
plt.axvline(x=2)
plt.title("fails the vertical line test")
plt... | 6fc2da77ce806062bfb92010b10ada73a2eec82d | 118,156 | ipynb | Jupyter Notebook | curriculum/unit-1-statistics-fundamentals/sprint-3-linear-algebra/module4-clustering/module-3.ipynb | BrianThomasRoss/lambda-school | 6140db5cb5a43d0a367e9a08dc216e8bec9fb323 | [
"MIT"
] | null | null | null | curriculum/unit-1-statistics-fundamentals/sprint-3-linear-algebra/module4-clustering/module-3.ipynb | BrianThomasRoss/lambda-school | 6140db5cb5a43d0a367e9a08dc216e8bec9fb323 | [
"MIT"
] | null | null | null | curriculum/unit-1-statistics-fundamentals/sprint-3-linear-algebra/module4-clustering/module-3.ipynb | BrianThomasRoss/lambda-school | 6140db5cb5a43d0a367e9a08dc216e8bec9fb323 | [
"MIT"
] | null | null | null | 54.24977 | 19,750 | 0.531932 | true | 14,599 | Qwen/Qwen-72B | 1. YES
2. YES | 0.803174 | 0.90599 | 0.727667 | __label__kor_Hang | 0.334323 | 0.528947 |
```python
%matplotlib inline
import matplotlib.pyplot as p
```
```python
from sympy import *
import scipy as sc
init_printing()
```
```python
x=var('x')
```
```python
a, b, c = var("a, b, c")
```
```python
x = var('x', real=True)
```
## bio cal
```python
r_m, N, t = var("r_m N t", real=True)
```
```python
... | ffb373f420139b9ee2bf90ab9c25a5fc998c7ce3 | 8,859 | ipynb | Jupyter Notebook | Week7/Code/simply_trial.ipynb | ph-u/CMEECourseWork_pmH | 8d52d4dcc3a643da7d55874e350c18f3bf377138 | [
"Apache-2.0"
] | null | null | null | Week7/Code/simply_trial.ipynb | ph-u/CMEECourseWork_pmH | 8d52d4dcc3a643da7d55874e350c18f3bf377138 | [
"Apache-2.0"
] | null | null | null | Week7/Code/simply_trial.ipynb | ph-u/CMEECourseWork_pmH | 8d52d4dcc3a643da7d55874e350c18f3bf377138 | [
"Apache-2.0"
] | null | null | null | 36.307377 | 2,072 | 0.707755 | true | 159 | Qwen/Qwen-72B | 1. YES
2. YES | 0.899121 | 0.76908 | 0.691496 | __label__eng_Latn | 0.332017 | 0.444909 |
# Investigating the Re-use of subsamples from previous iterations
Context: a multi-fidelity optimization procedure where an Error Grid is created after every evaluation to determine the next best fidelity for evaluation. Each Error Grid is made up of 'pixels' $(n_h, n_l)$ where $n_h < N_H$ and $n_l < N_L$, where $N_H,... | 3bcd7d9fdb1c4e01bee03251e7ec1dbd4295108f | 53,255 | ipynb | Jupyter Notebook | notebooks/subsample_reuse.ipynb | sjvrijn/multi-level-co-surrogates | 04a071eb4360bed6f1a517531690beec7857e3e5 | [
"MIT"
] | null | null | null | notebooks/subsample_reuse.ipynb | sjvrijn/multi-level-co-surrogates | 04a071eb4360bed6f1a517531690beec7857e3e5 | [
"MIT"
] | 2 | 2021-02-25T14:07:50.000Z | 2021-02-25T14:12:35.000Z | notebooks/subsample_reuse.ipynb | sjvrijn/multi-level-co-surrogates | 04a071eb4360bed6f1a517531690beec7857e3e5 | [
"MIT"
] | null | null | null | 123.561485 | 32,040 | 0.862849 | true | 1,683 | Qwen/Qwen-72B | 1. YES
2. YES | 0.861538 | 0.822189 | 0.708347 | __label__eng_Latn | 0.993434 | 0.48406 |
# Astroinformatics "Machine Learning Basics"
## Class 3:
In this tutorial, we'll see basics concepts of machine learning. (We will not see classification yet, but these concepts applies to those problems too). All this concepts are very well explained in the [Deep Learning Book, Chapter 5](http://www.deeplearningbook.... | 1858e7f7a0b78d615d70aac326448d4c9fd41570 | 532,272 | ipynb | Jupyter Notebook | auxiliar3.ipynb | rodrigcd/Astroinformatics_AS4501 | 4ac614ff5cfc15922df8592562e5fdad3151abe1 | [
"MIT"
] | null | null | null | auxiliar3.ipynb | rodrigcd/Astroinformatics_AS4501 | 4ac614ff5cfc15922df8592562e5fdad3151abe1 | [
"MIT"
] | null | null | null | auxiliar3.ipynb | rodrigcd/Astroinformatics_AS4501 | 4ac614ff5cfc15922df8592562e5fdad3151abe1 | [
"MIT"
] | null | null | null | 666.172716 | 101,916 | 0.931687 | true | 7,258 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91848 | 0.903294 | 0.829658 | __label__eng_Latn | 0.96774 | 0.765906 |
# Session 3: Unsupervised and Supervised Learning
<p class="lead">
Parag K. Mital<br />
<a href="https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info">Creative Applications of Deep Learning w/ Tensorflow</a><br />
<a href="https://www.kadenze.com/partners/kadenze-academy">Kadenze... | a0763f2873a01d53cb2f15ba88b201d857dcc132 | 469,282 | ipynb | Jupyter Notebook | session-3/lecture-3.ipynb | axsauze/deep-learning-creative-tensorflow | b427398702fd7ade03a2f873493fbeb202e75726 | [
"Apache-2.0"
] | 2 | 2018-04-20T02:08:17.000Z | 2018-04-20T11:43:00.000Z | session-3/lecture-3.ipynb | axsauze/deep-learning-creative-tensorflow | b427398702fd7ade03a2f873493fbeb202e75726 | [
"Apache-2.0"
] | null | null | null | session-3/lecture-3.ipynb | axsauze/deep-learning-creative-tensorflow | b427398702fd7ade03a2f873493fbeb202e75726 | [
"Apache-2.0"
] | 2 | 2017-06-23T22:51:21.000Z | 2018-08-05T15:12:09.000Z | 166.176346 | 156,314 | 0.875265 | true | 18,518 | Qwen/Qwen-72B | 1. YES
2. YES | 0.757794 | 0.705785 | 0.53484 | __label__eng_Latn | 0.992934 | 0.080942 |
```python
### conflits with Deepnote ###
# matplotlib inline plotting
%matplotlib inline
# make inline plotting higher resolution
%config InlineBackend.figure_format = 'svg'
### conflits with Deepnote ###
```
```python
# imports
import pandas as pd
import numpy as np
import statsmodels.api as sm
import matplotlib.p... | 9a72854d9a1c1d0f2b7dd58ecea47b434150409f | 615,352 | ipynb | Jupyter Notebook | Problem Set 3 - Time-Varying Risk Primea/My Solution/Problem Set 3 - Time-Varying Risk Premia.ipynb | ismand95/QFE | e80a902bc2d0147a604ee86414a7f7e9df92b5c9 | [
"MIT"
] | null | null | null | Problem Set 3 - Time-Varying Risk Primea/My Solution/Problem Set 3 - Time-Varying Risk Premia.ipynb | ismand95/QFE | e80a902bc2d0147a604ee86414a7f7e9df92b5c9 | [
"MIT"
] | null | null | null | Problem Set 3 - Time-Varying Risk Primea/My Solution/Problem Set 3 - Time-Varying Risk Premia.ipynb | ismand95/QFE | e80a902bc2d0147a604ee86414a7f7e9df92b5c9 | [
"MIT"
] | 1 | 2022-02-05T13:29:40.000Z | 2022-02-05T13:29:40.000Z | 61.937796 | 574 | 0.524019 | true | 20,458 | Qwen/Qwen-72B | 1. YES
2. YES | 0.651355 | 0.826712 | 0.538483 | __label__eng_Latn | 0.312877 | 0.089405 |
<a href="https://colab.research.google.com/github/john-s-butler-dit/Numerical-Analysis-Python/blob/master/Chapter%2006%20-%20Boundary%20Value%20Problems/.ipynb_checkpoints/601_Linear%20Shooting%20Method-checkpoint.ipynb" target="_parent"></a>
# Linear Shooting Method
#### John S Butler john.s.butler@tudublin.ie [Cou... | fe8aebfd3867c1d715eb4e29632c8d4f393cba69 | 71,221 | ipynb | Jupyter Notebook | Chapter 06 - Boundary Value Problems/.ipynb_checkpoints/601_Linear Shooting Method-checkpoint.ipynb | jjcrofts77/Numerical-Analysis-Python | 97e4b9274397f969810581ff95f4026f361a56a2 | [
"MIT"
] | 69 | 2019-09-05T21:39:12.000Z | 2022-03-26T14:00:25.000Z | Chapter 06 - Boundary Value Problems/.ipynb_checkpoints/601_Linear Shooting Method-checkpoint.ipynb | jjcrofts77/Numerical-Analysis-Python | 97e4b9274397f969810581ff95f4026f361a56a2 | [
"MIT"
] | null | null | null | Chapter 06 - Boundary Value Problems/.ipynb_checkpoints/601_Linear Shooting Method-checkpoint.ipynb | jjcrofts77/Numerical-Analysis-Python | 97e4b9274397f969810581ff95f4026f361a56a2 | [
"MIT"
] | 13 | 2021-06-17T15:34:04.000Z | 2022-01-14T14:53:43.000Z | 121.745299 | 15,598 | 0.820306 | true | 3,777 | Qwen/Qwen-72B | 1. YES
2. YES | 0.774583 | 0.79053 | 0.612332 | __label__eng_Latn | 0.643457 | 0.260982 |
## Surfinpy
#### Tutorial 1 - Bulk Phase diagrams
In this tutorial we learn how to generate a basic bulk phase diagram from DFT energies. This enables the comparison of the thermodynamic stability of various different bulk phases under different chemical potentials giving valuable insight in to the syntheis of solid... | 473efcc3b88b756d1bb73106697b1bc077d831d8 | 352,147 | ipynb | Jupyter Notebook | examples/Notebooks/Bulk/Tutorial_1.ipynb | jstse/SurfinPy | ff3a79f9415c170885e109ab881368271f3dcc19 | [
"MIT"
] | null | null | null | examples/Notebooks/Bulk/Tutorial_1.ipynb | jstse/SurfinPy | ff3a79f9415c170885e109ab881368271f3dcc19 | [
"MIT"
] | null | null | null | examples/Notebooks/Bulk/Tutorial_1.ipynb | jstse/SurfinPy | ff3a79f9415c170885e109ab881368271f3dcc19 | [
"MIT"
] | null | null | null | 1,443.22541 | 333,913 | 0.729812 | true | 1,809 | Qwen/Qwen-72B | 1. YES
2. YES | 0.72487 | 0.715424 | 0.51859 | __label__eng_Latn | 0.978202 | 0.043187 |
# Probability
A trial, experiment or observation is an event with an unknown outcome. All
possible outcomes of the trial are called the sample space, and the particular
outcomes being looked for are known as events. For example, if the trial is
flipping a coin the sample space is heads or tails. If the trial is rol... | 7c2b39a8e251e54b0453b7be89b4ba416f9459c5 | 4,413 | ipynb | Jupyter Notebook | statistics/probability.ipynb | mostlyoxygen/braindump | 6ef57bbb0444b2bd78ff408af4fdc58a9ade46fc | [
"CC0-1.0"
] | null | null | null | statistics/probability.ipynb | mostlyoxygen/braindump | 6ef57bbb0444b2bd78ff408af4fdc58a9ade46fc | [
"CC0-1.0"
] | null | null | null | statistics/probability.ipynb | mostlyoxygen/braindump | 6ef57bbb0444b2bd78ff408af4fdc58a9ade46fc | [
"CC0-1.0"
] | null | null | null | 32.688889 | 89 | 0.549739 | true | 818 | Qwen/Qwen-72B | 1. YES
2. YES | 0.952574 | 0.833325 | 0.793803 | __label__eng_Latn | 0.999672 | 0.682604 |
# Solve equation systems with SymPy
Once an a while you need to solve simple equation systems, I have found that using SymPy for this is a much better option than using pen and paper, where I usually make mistakes. Here is some short examples...
```python
# This Python 3 environment comes with many helpful analytics ... | d41450a8092d350f579b02e2d7ee253c28a6a459 | 5,184 | ipynb | Jupyter Notebook | kernels/sympy-solve/sympy-solve.ipynb | martinlarsalbert/kaggle | 5f75b0b7bf6adf1f5c9c20c2c3d4e1f6670716ac | [
"MIT"
] | null | null | null | kernels/sympy-solve/sympy-solve.ipynb | martinlarsalbert/kaggle | 5f75b0b7bf6adf1f5c9c20c2c3d4e1f6670716ac | [
"MIT"
] | null | null | null | kernels/sympy-solve/sympy-solve.ipynb | martinlarsalbert/kaggle | 5f75b0b7bf6adf1f5c9c20c2c3d4e1f6670716ac | [
"MIT"
] | null | null | null | 5,184 | 5,184 | 0.685764 | true | 774 | Qwen/Qwen-72B | 1. YES
2. YES | 0.941654 | 0.937211 | 0.882528 | __label__eng_Latn | 0.994206 | 0.888743 |
# "Social network Graph Link Prediction - Facebook Challenge"
> "Given records of people's unique Id's, Our task is to find out wether they are friends or not and suggest any of the user with his probable top 5 friend recommendations."
- toc: false
- branch: master
- badges: true
- comments: true
- author: Sai Kumar R... | 2f87032344f3f7f2fca1a04ab1d5f538f3ec711e | 664,449 | ipynb | Jupyter Notebook | _notebooks/2021-11-12-Facebook Case Study.ipynb | saikumarpochireddygari/dsgrad-projects-articles | 3988293bcbdb44b55ba9b88c8500481a309b2eaa | [
"Apache-2.0"
] | null | null | null | _notebooks/2021-11-12-Facebook Case Study.ipynb | saikumarpochireddygari/dsgrad-projects-articles | 3988293bcbdb44b55ba9b88c8500481a309b2eaa | [
"Apache-2.0"
] | null | null | null | _notebooks/2021-11-12-Facebook Case Study.ipynb | saikumarpochireddygari/dsgrad-projects-articles | 3988293bcbdb44b55ba9b88c8500481a309b2eaa | [
"Apache-2.0"
] | null | null | null | 104.015185 | 124,344 | 0.823711 | true | 34,234 | Qwen/Qwen-72B | 1. YES
2. YES | 0.771844 | 0.699254 | 0.539715 | __label__eng_Latn | 0.474131 | 0.092268 |
```python
# First check the Python version
import sys
if sys.version_info < (3,4):
print('You are running an older version of Python!\n\n' \
'You should consider updating to Python 3.4.0 or ' \
'higher as the libraries built for this course ' \
'have only been tested in Python 3.4 and ... | a099fd4eddc2410221d64f51f1769327931d8f07 | 65,450 | ipynb | Jupyter Notebook | session-1/.ipynb_checkpoints/Inquidia Day Prez-checkpoint.ipynb | arkansasred/CADL | 5fe4141124c19c5f331cf5b49970313612a47c4e | [
"Apache-2.0"
] | 1 | 2018-06-10T06:06:27.000Z | 2018-06-10T06:06:27.000Z | session-1/.ipynb_checkpoints/Inquidia Day Prez-checkpoint.ipynb | joshoberman/CADL | 5fe4141124c19c5f331cf5b49970313612a47c4e | [
"Apache-2.0"
] | null | null | null | session-1/.ipynb_checkpoints/Inquidia Day Prez-checkpoint.ipynb | joshoberman/CADL | 5fe4141124c19c5f331cf5b49970313612a47c4e | [
"Apache-2.0"
] | null | null | null | 98.717949 | 21,472 | 0.803453 | true | 6,390 | Qwen/Qwen-72B | 1. YES
2. YES | 0.699254 | 0.851953 | 0.595732 | __label__eng_Latn | 0.986836 | 0.222415 |
<center>
## [mlcourse.ai](mlcourse.ai) – Open Machine Learning Course
### <center> Author: Ilya Larchenko, ODS Slack ilya_l
## <center> Individual data analysis project
## 1. Data description
__I will analyse California Housing Data (1990). It can be downloaded from Kaggle [https://www.kaggle.com/harrywan... | e8a004e169c8d6f20ec28d7701afdab140613644 | 63,185 | ipynb | Jupyter Notebook | jupyter_english/projects_indiv/California_housing_value_prediction_Ilya_Larchenko.ipynb | salman394/AI-ml--course | 2ed3a1382614dd00184e5179026623714ccc9e8c | [
"Unlicense"
] | null | null | null | jupyter_english/projects_indiv/California_housing_value_prediction_Ilya_Larchenko.ipynb | salman394/AI-ml--course | 2ed3a1382614dd00184e5179026623714ccc9e8c | [
"Unlicense"
] | null | null | null | jupyter_english/projects_indiv/California_housing_value_prediction_Ilya_Larchenko.ipynb | salman394/AI-ml--course | 2ed3a1382614dd00184e5179026623714ccc9e8c | [
"Unlicense"
] | null | null | null | 32.319693 | 426 | 0.607866 | true | 9,922 | Qwen/Qwen-72B | 1. YES
2. YES | 0.749087 | 0.760651 | 0.569794 | __label__eng_Latn | 0.955475 | 0.162151 |
# Second Law Efficiency
A power plant receives two heat inputs, 25 kW at 825°C and 50 kW at 240°C, rejects heat to the environment at 20°C, and produces power of 12 kW. Calculate the second-law efficiency of the power plant.
```python
from pint import UnitRegistry
ureg = UnitRegistry()
Q_ = ureg.Quantity
```
The se... | b1ee773257d5c44a7a7fb73ffebf043b5e55b521 | 3,002 | ipynb | Jupyter Notebook | book/content/exergy/second-law-efficiency.ipynb | kyleniemeyer/computational-thermo | 3f0d1d4a6d4247ac3bf3b74867411f2090c70cbd | [
"CC-BY-4.0",
"BSD-3-Clause"
] | 13 | 2020-04-01T05:52:06.000Z | 2022-03-27T20:25:59.000Z | book/content/exergy/second-law-efficiency.ipynb | kyleniemeyer/computational-thermo | 3f0d1d4a6d4247ac3bf3b74867411f2090c70cbd | [
"CC-BY-4.0",
"BSD-3-Clause"
] | 1 | 2020-04-28T04:02:05.000Z | 2020-04-29T17:49:52.000Z | book/content/exergy/second-law-efficiency.ipynb | kyleniemeyer/computational-thermo | 3f0d1d4a6d4247ac3bf3b74867411f2090c70cbd | [
"CC-BY-4.0",
"BSD-3-Clause"
] | 6 | 2020-04-03T14:52:24.000Z | 2022-03-29T02:29:43.000Z | 22.916031 | 206 | 0.509327 | true | 474 | Qwen/Qwen-72B | 1. YES
2. YES | 0.937211 | 0.817574 | 0.76624 | __label__eng_Latn | 0.8543 | 0.618564 |
# Case study 1: Diffusion of fluid pressure and seismicity below Mt. Hood
We will apply our new transient model to study the relation between fluid pressure and seismicity in the crust below an active volcano, Mt. Hood in Oregon, USA. We will follow a publication by Saar and Manga (2003). The central claim of this pap... | ba6579817e6bde161a293f35d135a67b1f146fab | 70,472 | ipynb | Jupyter Notebook | exercises/exercise_3_transient_flow/.ipynb_checkpoints/exercise_3a_pore_pressure_diffusion_and_seismicity-checkpoint.ipynb | ElcoLuijendijk/fluids_in_the_crust | c2cadb0a91e9f9ed62094ac5e796168fef0d1a3e | [
"CC-BY-4.0"
] | 2 | 2021-01-12T19:08:16.000Z | 2021-01-13T14:27:42.000Z | exercises/exercise_3_transient_flow/.ipynb_checkpoints/exercise_3a_pore_pressure_diffusion_and_seismicity-checkpoint.ipynb | ElcoLuijendijk/fluids_in_the_crust | c2cadb0a91e9f9ed62094ac5e796168fef0d1a3e | [
"CC-BY-4.0"
] | null | null | null | exercises/exercise_3_transient_flow/.ipynb_checkpoints/exercise_3a_pore_pressure_diffusion_and_seismicity-checkpoint.ipynb | ElcoLuijendijk/fluids_in_the_crust | c2cadb0a91e9f9ed62094ac5e796168fef0d1a3e | [
"CC-BY-4.0"
] | null | null | null | 111.860317 | 26,784 | 0.818297 | true | 7,940 | Qwen/Qwen-72B | 1. YES
2. YES | 0.934395 | 0.812867 | 0.759539 | __label__eng_Latn | 0.976158 | 0.602996 |
# Chi-Squared Distribution
***
## Definition
>The Chi-Squared distribution is a continous probability distribution focused on sample standard deviations and can (e.g.) "let you know whether two groups have significantly different opinions, which makes it a very useful statistic for survey research" $ ^{[1]}$.
## Form... | 25206169a08d878b6056258e4285d6ab1c506bd3 | 346,947 | ipynb | Jupyter Notebook | Mathematics/Statistics/Statistics and Probability Python Notebooks/Important-Statistics-Distributions-py-notebooks/Chi-Squared Distribution.ipynb | okara83/Becoming-a-Data-Scientist | f09a15f7f239b96b77a2f080c403b2f3e95c9650 | [
"MIT"
] | null | null | null | Mathematics/Statistics/Statistics and Probability Python Notebooks/Important-Statistics-Distributions-py-notebooks/Chi-Squared Distribution.ipynb | okara83/Becoming-a-Data-Scientist | f09a15f7f239b96b77a2f080c403b2f3e95c9650 | [
"MIT"
] | null | null | null | Mathematics/Statistics/Statistics and Probability Python Notebooks/Important-Statistics-Distributions-py-notebooks/Chi-Squared Distribution.ipynb | okara83/Becoming-a-Data-Scientist | f09a15f7f239b96b77a2f080c403b2f3e95c9650 | [
"MIT"
] | 2 | 2022-02-09T15:41:33.000Z | 2022-02-11T07:47:40.000Z | 631.961749 | 132,248 | 0.94076 | true | 3,675 | Qwen/Qwen-72B | 1. YES
2. YES | 0.879147 | 0.763484 | 0.671214 | __label__eng_Latn | 0.559435 | 0.397787 |
```python
import numpy as np
import numpy.linalg as la
import sympy as sp
```
```python
def gradient(formula, symbols, values=None):
'''
Given a SymPy formula and variables
Find its analytic gradient without substituion
as a list of SymPy formulae or numerical gradient
if values specified
'''
... | b80fc7e2eb5099b521be47ad0edb3509b97baa4e | 8,030 | ipynb | Jupyter Notebook | newton_nd_optimization_crude.ipynb | Racso-3141/uiuc-cs357-fa21-scripts | e44f0a1ea4eb657cb77253f1db464d52961bbe5e | [
"MIT"
] | 10 | 2021-11-02T05:56:10.000Z | 2022-03-03T19:25:19.000Z | newton_nd_optimization_crude.ipynb | Racso-3141/uiuc-cs357-fa21-scripts | e44f0a1ea4eb657cb77253f1db464d52961bbe5e | [
"MIT"
] | null | null | null | newton_nd_optimization_crude.ipynb | Racso-3141/uiuc-cs357-fa21-scripts | e44f0a1ea4eb657cb77253f1db464d52961bbe5e | [
"MIT"
] | 3 | 2021-10-30T15:18:01.000Z | 2021-12-10T11:26:43.000Z | 30.074906 | 114 | 0.509838 | true | 1,531 | Qwen/Qwen-72B | 1. YES
2. YES | 0.897695 | 0.843895 | 0.757561 | __label__eng_Latn | 0.522653 | 0.598399 |
## Cosmological constraints on quantum fluctuations in modified teleparallel gravity
The Friedmann equations' modified by quantum fluctuations can be written as
\begin{equation}
3 H^2=\cdots ,
\end{equation}
and
\begin{equation}
2 \dot{H}+3 H^2=\cdots ,
\end{equation}
whereas the modified Klein-Gordon equation can be ... | 7a821dd6cff518e98e2de91090314c7f7a5958ed | 433,108 | ipynb | Jupyter Notebook | supp_ntbks_arxiv.2111.11761/tg_quant_sample.ipynb | reggiebernardo/notebooks | b54efe619e600679a5c84de689461e26cf1f82af | [
"MIT"
] | null | null | null | supp_ntbks_arxiv.2111.11761/tg_quant_sample.ipynb | reggiebernardo/notebooks | b54efe619e600679a5c84de689461e26cf1f82af | [
"MIT"
] | null | null | null | supp_ntbks_arxiv.2111.11761/tg_quant_sample.ipynb | reggiebernardo/notebooks | b54efe619e600679a5c84de689461e26cf1f82af | [
"MIT"
] | null | null | null | 315.216885 | 84,084 | 0.915391 | true | 11,713 | Qwen/Qwen-72B | 1. YES
2. YES | 0.7773 | 0.699254 | 0.54353 | __label__eng_Latn | 0.491746 | 0.101133 |
# juliaのSymbolicsでやってみる
```julia
using Symbolics
```
```julia
include("./kinematics.jl")
using .Kinematics
```
WARNING: replacing module Kinematics.
WARNING: using Kinematics.locals in module Main conflicts with an existing identifier.
```julia
@variables l1_1, l1_2, l1_3, l2_1, l2_2, l2_3
@variables ξ1... | 95d396a70b6cb24f2946773fcce21497a12a49e5 | 6,379 | ipynb | Jupyter Notebook | o/soft_robot/derivation_of_kinematics/jacobian_jl.ipynb | YoshimitsuMatsutaIe/ctrlab2021_soudan | 7841c981e6804cc92d34715a00e7c3efce41d1d0 | [
"MIT"
] | null | null | null | o/soft_robot/derivation_of_kinematics/jacobian_jl.ipynb | YoshimitsuMatsutaIe/ctrlab2021_soudan | 7841c981e6804cc92d34715a00e7c3efce41d1d0 | [
"MIT"
] | null | null | null | o/soft_robot/derivation_of_kinematics/jacobian_jl.ipynb | YoshimitsuMatsutaIe/ctrlab2021_soudan | 7841c981e6804cc92d34715a00e7c3efce41d1d0 | [
"MIT"
] | null | null | null | 35.243094 | 613 | 0.591472 | true | 299 | Qwen/Qwen-72B | 1. YES
2. YES | 0.884039 | 0.76908 | 0.679897 | __label__eng_Latn | 0.206058 | 0.41796 |
# Lecture 20: Classification of Astronomical Images with Deep Learning
#### This notebook was developed by [Zeljko Ivezic](http://faculty.washington.edu/ivezic/) for the 2021 data science class at the University of Sao Paulo and it is available from [github](https://github.com/ivezic/SaoPaulo2021/blob/main/notebooks/L... | 8892e6c1a81e81ec5391c4a04dfd98a6082065c0 | 846,586 | ipynb | Jupyter Notebook | lectures/notes/Lecture13-deep-learning-cnn.ipynb | uw-astro/astr-598a-win22 | 65e0f366e164c276f1dfc06873741c6f6c94b300 | [
"BSD-3-Clause"
] | 7 | 2021-06-16T00:46:26.000Z | 2021-08-05T18:55:39.000Z | notebooks/Lecture20.ipynb | ivezic/SaoPaulo2021 | 6e88724fd07eab711fef1c1fc4c94decb20fc315 | [
"BSD-2-Clause"
] | null | null | null | notebooks/Lecture20.ipynb | ivezic/SaoPaulo2021 | 6e88724fd07eab711fef1c1fc4c94decb20fc315 | [
"BSD-2-Clause"
] | 2 | 2021-07-19T16:28:16.000Z | 2021-08-23T01:39:45.000Z | 459.851168 | 194,348 | 0.914826 | true | 23,563 | Qwen/Qwen-72B | 1. YES
2. YES | 0.746139 | 0.712232 | 0.531424 | __label__yue_Hant | 0.984556 | 0.073006 |
# Chapter 2
> Linear Algebra and Machine Learning
## Lecture 9
___
### Review of Linear Algebra
Reference Books: Matrix Cookbook by Kaare Brandt Petersen & Michael Syskind Pedersen, 2012
$A \in \mathbb{R}^{n \times m}, n\text{ rows and } m\text{ columns}$
range($A$):=span{\underline{a}$_1$,...,\underline{a}$_m... | 8266bbc8300edcc12b57efbf52ed7806fc99b3b3 | 188,902 | ipynb | Jupyter Notebook | course_notes/.ipynb_checkpoints/Chapter2-checkpoint.ipynb | raph651/Amath-582-Data-Analysis | c1d72d897b7611652c7fe1f71c5439062b8bdf9e | [
"MIT"
] | null | null | null | course_notes/.ipynb_checkpoints/Chapter2-checkpoint.ipynb | raph651/Amath-582-Data-Analysis | c1d72d897b7611652c7fe1f71c5439062b8bdf9e | [
"MIT"
] | null | null | null | course_notes/.ipynb_checkpoints/Chapter2-checkpoint.ipynb | raph651/Amath-582-Data-Analysis | c1d72d897b7611652c7fe1f71c5439062b8bdf9e | [
"MIT"
] | null | null | null | 190.617558 | 71,180 | 0.890954 | true | 6,878 | Qwen/Qwen-72B | 1. YES
2. YES | 0.843895 | 0.849971 | 0.717287 | __label__eng_Latn | 0.812803 | 0.504829 |
```python
import numpy as np
import pandas as pd
import sympy as sym
from sympy import init_printing
from lgbayes.models import LinearGaussianBN
init_printing(use_latex=True)
%matplotlib inline
%load_ext autoreload
%autoreload 2
```
The autoreload extension is already loaded. To reload it, use:
%reload_ext ... | 641627ba15d0eef78c7c84186b46e442d57099ab | 19,855 | ipynb | Jupyter Notebook | Multivariate Gaussians.ipynb | finnhacks42/linear-gaussian-bn | 63e3355bbdcb0c7218e41b1c33858b7d9917177e | [
"MIT"
] | null | null | null | Multivariate Gaussians.ipynb | finnhacks42/linear-gaussian-bn | 63e3355bbdcb0c7218e41b1c33858b7d9917177e | [
"MIT"
] | null | null | null | Multivariate Gaussians.ipynb | finnhacks42/linear-gaussian-bn | 63e3355bbdcb0c7218e41b1c33858b7d9917177e | [
"MIT"
] | null | null | null | 48.664216 | 3,348 | 0.675246 | true | 1,373 | Qwen/Qwen-72B | 1. YES
2. YES | 0.896251 | 0.785309 | 0.703834 | __label__kor_Hang | 0.187666 | 0.473573 |
# Understanding the SVD
```python
import numpy as np
```
### Useful reference
- [A Singularly Valuable Decomposition](https://datajobs.com/data-science-repo/SVD-[Dan-Kalman].pdf)
## Sketch of lecture
### Singular value decomposition
Our goal is to understand the following forms of the SVD.
$$
A = U \Sigma V^T
$... | 7489bbf026167bdc8a9195deaddbbc6e141dd6cf | 217,923 | ipynb | Jupyter Notebook | notebook/S08E_SVD.ipynb | ashnair1/sta-663-2019 | 17eb85b644c52978c2ef3a53a80b7fb031360e3d | [
"BSD-3-Clause"
] | 68 | 2019-01-09T21:53:55.000Z | 2022-02-16T17:14:22.000Z | notebook/S08E_SVD.ipynb | ashnair1/sta-663-2019 | 17eb85b644c52978c2ef3a53a80b7fb031360e3d | [
"BSD-3-Clause"
] | null | null | null | notebook/S08E_SVD.ipynb | ashnair1/sta-663-2019 | 17eb85b644c52978c2ef3a53a80b7fb031360e3d | [
"BSD-3-Clause"
] | 62 | 2019-01-09T21:43:48.000Z | 2021-11-15T04:26:25.000Z | 203.286381 | 42,060 | 0.900988 | true | 4,611 | Qwen/Qwen-72B | 1. YES
2. YES | 0.815232 | 0.893309 | 0.728255 | __label__eng_Latn | 0.978073 | 0.530312 |
# Stereo Geometry
This notebook visualizes the geometry between two views called epipolar geometry.
**Subjects are covered:**
1. **Definitions of epipolar geometry, the Fundamental Matrix, and the Essential Matrix.**
2. **Visualizing epipolar geometry.**
3. **8 point algorithm for computing the Fundamental matrix.**
... | d637eab16ba361b5ecbd119185e0710dfccedeff | 35,507 | ipynb | Jupyter Notebook | 3_stereo_geometry.ipynb | maxcrous/multiview_notebooks | bea2f87b8c78c5819337a496a0d330c255b492d1 | [
"MIT"
] | 47 | 2021-12-05T16:12:01.000Z | 2022-03-28T12:18:23.000Z | 3_stereo_geometry.ipynb | maxcrous/multiview_notebooks | bea2f87b8c78c5819337a496a0d330c255b492d1 | [
"MIT"
] | null | null | null | 3_stereo_geometry.ipynb | maxcrous/multiview_notebooks | bea2f87b8c78c5819337a496a0d330c255b492d1 | [
"MIT"
] | 7 | 2021-12-05T18:48:06.000Z | 2022-03-26T02:19:43.000Z | 51.015805 | 1,081 | 0.592447 | true | 8,127 | Qwen/Qwen-72B | 1. YES
2. YES | 0.835484 | 0.843895 | 0.70506 | __label__eng_Latn | 0.960091 | 0.476423 |
```python
%matplotlib inline
```
序列模型和长短时记忆网络(LSTM)
===================================================
到目前为止，我们已经看到了各种各样的前馈网络(feed-forward networks)。
也就是说，根本不存在由网络维护的状态(state)。
这可能不是我们想要的行为。序列模型(Sequence models)是NLP的核心：
它们是在输入之间通过时间存在某种依赖关系的模型。
序列模型的经典例子是用于词性标注(part-of-speech tagging)的
隐马尔可夫模型(Hidden Markov Model)。... | a649d85626beb8aedd9aec5bcdbf425341c7fbd2 | 15,820 | ipynb | Jupyter Notebook | build/_downloads/56409bf15ae7b72b139b998779f82a23/sequence_models_tutorial.ipynb | ScorpioDoctor/antares02 | 631b817d2e98f351d1173b620d15c4a5efed11da | [
"BSD-3-Clause"
] | null | null | null | build/_downloads/56409bf15ae7b72b139b998779f82a23/sequence_models_tutorial.ipynb | ScorpioDoctor/antares02 | 631b817d2e98f351d1173b620d15c4a5efed11da | [
"BSD-3-Clause"
] | null | null | null | build/_downloads/56409bf15ae7b72b139b998779f82a23/sequence_models_tutorial.ipynb | ScorpioDoctor/antares02 | 631b817d2e98f351d1173b620d15c4a5efed11da | [
"BSD-3-Clause"
] | null | null | null | 125.555556 | 3,549 | 0.693552 | true | 3,031 | Qwen/Qwen-72B | 1. YES
2. YES | 0.76908 | 0.79053 | 0.607981 | __label__eng_Latn | 0.739999 | 0.250874 |
```
%load_ext autoreload
%autoreload 2
```
```
import numpy as np
import matplotlib.pyplot as plt
import common
import sympy as sp
%matplotlib inline
%config InlineBackend.figure_format='retina'
fault_depth = 0.5
def fault_fnc(q):
return 0 * q, q - 1 - fault_depth, -np.ones_like(q), 0 * q, np.ones_like(q)
s... | 3bc33e2a61d2d3f5d6e4da08d994494a530726b3 | 9,254 | ipynb | Jupyter Notebook | tutorials/volumetric/gravity.ipynb | tbenthompson/BIE_tutorials | 02cd56ab7e63e36afc4a10db17072076541aab77 | [
"MIT"
] | 1 | 2021-06-18T18:02:55.000Z | 2021-06-18T18:02:55.000Z | tutorials/volumetric/gravity.ipynb | tbenthompson/BIE_tutorials | 02cd56ab7e63e36afc4a10db17072076541aab77 | [
"MIT"
] | null | null | null | tutorials/volumetric/gravity.ipynb | tbenthompson/BIE_tutorials | 02cd56ab7e63e36afc4a10db17072076541aab77 | [
"MIT"
] | 1 | 2021-07-14T19:47:00.000Z | 2021-07-14T19:47:00.000Z | 27.78979 | 98 | 0.484007 | true | 1,884 | Qwen/Qwen-72B | 1. YES
2. YES | 0.885631 | 0.721743 | 0.639198 | __label__eng_Latn | 0.182349 | 0.323403 |
```python
# HIDDEN
from datascience import *
from prob140 import *
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
%matplotlib inline
import math
from scipy import stats
from sympy import *
init_printing()
```
## Independence ##
Jointly distributed random variables $X$ and $Y$ are ... | ab84e57141bdd1a77b7eb754234da6ff3885be1e | 232,131 | ipynb | Jupyter Notebook | content/Chapter_17/02_Independence.ipynb | dcroce/jupyter-book | 9ac4b502af8e8c5c3b96f5ec138602a0d3d8a624 | [
"MIT"
] | null | null | null | content/Chapter_17/02_Independence.ipynb | dcroce/jupyter-book | 9ac4b502af8e8c5c3b96f5ec138602a0d3d8a624 | [
"MIT"
] | null | null | null | content/Chapter_17/02_Independence.ipynb | dcroce/jupyter-book | 9ac4b502af8e8c5c3b96f5ec138602a0d3d8a624 | [
"MIT"
] | null | null | null | 578.880299 | 104,836 | 0.938113 | true | 1,804 | Qwen/Qwen-72B | 1. YES
2. YES | 0.843895 | 0.90053 | 0.759953 | __label__eng_Latn | 0.978797 | 0.603957 |
<a href="https://colab.research.google.com/github/HenriqueCCdA/ElementosFinitosCurso/blob/main/notebooks/Elemento_finitos_Exercicios_ex1.ipynb" target="_parent"></a>
```python
import numpy as np
from scipy.linalg import lu_factor, lu_solve
import matplotlib.pyplot as plt
import matplotlib as mpl
```
# Paramentros de... | 3a155dbd5a8df0a949dbe0a26bdb02523af3ba96 | 118,637 | ipynb | Jupyter Notebook | notebooks/Elemento_finitos_Exercicios_ex1.ipynb | HenriqueCCdA/ElementosFinitosCurso | 5cd37d3d3d77a5b6234fad5fca871d907558dff4 | [
"MIT"
] | 2 | 2021-09-28T00:31:07.000Z | 2021-09-28T00:31:25.000Z | notebooks/Elemento_finitos_Exercicios_ex1.ipynb | HenriqueCCdA/ElementosFinitosCurso | 5cd37d3d3d77a5b6234fad5fca871d907558dff4 | [
"MIT"
] | null | null | null | notebooks/Elemento_finitos_Exercicios_ex1.ipynb | HenriqueCCdA/ElementosFinitosCurso | 5cd37d3d3d77a5b6234fad5fca871d907558dff4 | [
"MIT"
] | null | null | null | 188.312698 | 91,014 | 0.88308 | true | 2,714 | Qwen/Qwen-72B | 1. YES
2. YES | 0.901921 | 0.785309 | 0.708286 | __label__yue_Hant | 0.321136 | 0.483917 |
```python
# Header starts here.
from sympy.physics.units import *
from sympy import *
# Rounding:
import decimal
from decimal import Decimal as DX
from copy import deepcopy
def iso_round(obj, pv, rounding=decimal.ROUND_HALF_EVEN):
import sympy
"""
Rounding acc. to DIN EN ISO 80000-1:2013-08
place value... | c867936a8abf4e57a836aa55552ad967f0f61c32 | 11,005 | ipynb | Jupyter Notebook | ipynb/TM_2/4_BB/2_BL/2.4.2.G-FEM_cc.ipynb | kassbohm/tm-snippets | 5e0621ba2470116e54643b740d1b68b9f28bff12 | [
"MIT"
] | null | null | null | ipynb/TM_2/4_BB/2_BL/2.4.2.G-FEM_cc.ipynb | kassbohm/tm-snippets | 5e0621ba2470116e54643b740d1b68b9f28bff12 | [
"MIT"
] | null | null | null | ipynb/TM_2/4_BB/2_BL/2.4.2.G-FEM_cc.ipynb | kassbohm/tm-snippets | 5e0621ba2470116e54643b740d1b68b9f28bff12 | [
"MIT"
] | null | null | null | 33.551829 | 130 | 0.367742 | true | 2,788 | Qwen/Qwen-72B | 1. YES
2. YES | 0.785309 | 0.73412 | 0.57651 | __label__eng_Latn | 0.143314 | 0.177757 |
<div style = "font-family:Georgia;
font-size:2.5vw;
color:lightblue;
font-style:bold;
text-align:center;
background:url('./Animations/Title Background.gif') no-repeat center;
background-size:cover)">
<br><br>
Hi... | e6609f2a02c8c6ba3cadf7ba8e99786b9a78516c | 457,124 | ipynb | Jupyter Notebook | Feature vectors/1. HOG.ipynb | IllgamhoDuck/CVND | 06f9530b79c977d33c6220a9bba38cbcf8d164b9 | [
"MIT"
] | null | null | null | Feature vectors/1. HOG.ipynb | IllgamhoDuck/CVND | 06f9530b79c977d33c6220a9bba38cbcf8d164b9 | [
"MIT"
] | null | null | null | Feature vectors/1. HOG.ipynb | IllgamhoDuck/CVND | 06f9530b79c977d33c6220a9bba38cbcf8d164b9 | [
"MIT"
] | 1 | 2020-03-29T00:40:55.000Z | 2020-03-29T00:40:55.000Z | 307.000672 | 380,981 | 0.900609 | true | 7,909 | Qwen/Qwen-72B | 1. YES
2. YES | 0.672332 | 0.760651 | 0.51141 | __label__eng_Latn | 0.99732 | 0.026505 |
# Project 3: Percolation - FYS4460
Author: Øyvind Sigmundson Schøyen
In this project we'll explore _percolation_ from the project shown here: https://www.uio.no/studier/emner/matnat/fys/FYS4460/v19/notes/project2017-ob3.pdf
```python
import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage as spi
impo... | 726aef6fedee3fcb6e34f9206eb6c6d2f797452a | 318,338 | ipynb | Jupyter Notebook | project-3/generating-percolation-clusters.ipynb | Schoyen/FYS4460 | 0c6ba1deefbfd5e9d1657910243afc2297c695a3 | [
"MIT"
] | 1 | 2019-08-29T16:29:18.000Z | 2019-08-29T16:29:18.000Z | project-3/generating-percolation-clusters.ipynb | Schoyen/FYS4460 | 0c6ba1deefbfd5e9d1657910243afc2297c695a3 | [
"MIT"
] | null | null | null | project-3/generating-percolation-clusters.ipynb | Schoyen/FYS4460 | 0c6ba1deefbfd5e9d1657910243afc2297c695a3 | [
"MIT"
] | 1 | 2020-05-27T14:01:36.000Z | 2020-05-27T14:01:36.000Z | 425.017356 | 92,334 | 0.927241 | true | 2,311 | Qwen/Qwen-72B | 1. YES
2. YES | 0.831143 | 0.822189 | 0.683357 | __label__eng_Latn | 0.961272 | 0.425998 |
<a href="https://colab.research.google.com/github/hBar2013/DS-Unit-1-Sprint-4-Statistical-Tests-and-Experiments/blob/master/module2-intermediate-linear-algebra/Kim_Lowry_Intermediate_Linear_Algebra_Assignment.ipynb" target="_parent"></a>
# Statistics
```
import numpy as np
```
## 1.1 Sales for the past week was the... | 4a2bebf0afe61ee4febaef23c37e55d78d9341aa | 104,445 | ipynb | Jupyter Notebook | module2-intermediate-linear-algebra/Kim_Lowry_Intermediate_Linear_Algebra_Assignment.ipynb | hBar2013/DS-Unit-1-Sprint-4-Statistical-Tests-and-Experiments | 21e773e2e657fca9f3d8509ae4caaa170d536406 | [
"MIT"
] | null | null | null | module2-intermediate-linear-algebra/Kim_Lowry_Intermediate_Linear_Algebra_Assignment.ipynb | hBar2013/DS-Unit-1-Sprint-4-Statistical-Tests-and-Experiments | 21e773e2e657fca9f3d8509ae4caaa170d536406 | [
"MIT"
] | null | null | null | module2-intermediate-linear-algebra/Kim_Lowry_Intermediate_Linear_Algebra_Assignment.ipynb | hBar2013/DS-Unit-1-Sprint-4-Statistical-Tests-and-Experiments | 21e773e2e657fca9f3d8509ae4caaa170d536406 | [
"MIT"
] | null | null | null | 55.320445 | 9,914 | 0.633922 | true | 6,741 | Qwen/Qwen-72B | 1. YES
2. YES | 0.812867 | 0.83762 | 0.680874 | __label__eng_Latn | 0.495062 | 0.420229 |
```python
from sympy import *
import numpy as np
import matplotlib.pyplot as plt
from PlottingSpectrum import generate_SED
def weighted_fitting(x_s, y_s, errs):
list_Y = []
list_A = []
list_C = []
for i in range(len(x_s)):
list_Y.append([y_s[i]])
list_A.append([1, x_s[i]])
... | e9d6d8c4e51dd8b112a40bbb3e81388bf023a291 | 6,591 | ipynb | Jupyter Notebook | Final Project/.ipynb_checkpoints/PhysicalProperties-checkpoint.ipynb | CalebLammers/CTA200 | 2b8e442f10479b8f82a9b8c4558a45aa9e791118 | [
"MIT"
] | null | null | null | Final Project/.ipynb_checkpoints/PhysicalProperties-checkpoint.ipynb | CalebLammers/CTA200 | 2b8e442f10479b8f82a9b8c4558a45aa9e791118 | [
"MIT"
] | null | null | null | Final Project/.ipynb_checkpoints/PhysicalProperties-checkpoint.ipynb | CalebLammers/CTA200 | 2b8e442f10479b8f82a9b8c4558a45aa9e791118 | [
"MIT"
] | null | null | null | 31.6875 | 1,106 | 0.499166 | true | 854 | Qwen/Qwen-72B | 1. YES
2. YES | 0.935347 | 0.779993 | 0.729564 | __label__eng_Latn | 0.215772 | 0.533353 |
# Lecture 02 Elimination with Matrices
Today's lecture contains:
1. Elimination <br/>
2. Explaination of elimination <br/>
3. Permutation <br/>
4. Inverse Matrix <br/>
## 1. Elimination
Suppose we have equations with 3 unknown:
\begin{align}
\begin{cases}x&+2y&+z&=2\\3x&+8y&+z&=12\\&4y&+z&=2\end{cases}
\end{align}
... | 82a83ff6488e5dcfbcb658797a11c37a1d9c0c20 | 10,853 | ipynb | Jupyter Notebook | Lecture 02 Elimination with Matrices.ipynb | XingxinHE/Linear_Algebra | 7d6b78699f8653ece60e07765fd485dd36b26194 | [
"MIT"
] | 3 | 2021-04-24T17:23:50.000Z | 2021-11-27T11:00:04.000Z | Lecture 02 Elimination with Matrices.ipynb | XingxinHE/Linear_Algebra | 7d6b78699f8653ece60e07765fd485dd36b26194 | [
"MIT"
] | null | null | null | Lecture 02 Elimination with Matrices.ipynb | XingxinHE/Linear_Algebra | 7d6b78699f8653ece60e07765fd485dd36b26194 | [
"MIT"
] | null | null | null | 41.903475 | 305 | 0.547959 | true | 3,091 | Qwen/Qwen-72B | 1. YES
2. YES | 0.891811 | 0.914901 | 0.815919 | __label__eng_Latn | 0.802924 | 0.733986 |
# Mass-spring-damper
In this tutorial, we will describe the mechanics and control of the one degree of freedom translational mass-spring-damper system subject to a control input force. We will first derive the dynamic equations by hand. Then, we will derive them using the `sympy.mechanics` python package.
The system ... | 3004917c36d3d9173a492457c866edeaecbf9a5d | 7,253 | ipynb | Jupyter Notebook | tutorials/robotics/mass-spring-damper.ipynb | Pandinosaurus/pyrobolearn | 9cd7c060723fda7d2779fa255ac998c2c82b8436 | [
"Apache-2.0"
] | 2 | 2021-01-21T21:08:30.000Z | 2022-03-29T16:45:49.000Z | tutorials/robotics/mass-spring-damper.ipynb | Pandinosaurus/pyrobolearn | 9cd7c060723fda7d2779fa255ac998c2c82b8436 | [
"Apache-2.0"
] | null | null | null | tutorials/robotics/mass-spring-damper.ipynb | Pandinosaurus/pyrobolearn | 9cd7c060723fda7d2779fa255ac998c2c82b8436 | [
"Apache-2.0"
] | 1 | 2020-09-29T21:25:39.000Z | 2020-09-29T21:25:39.000Z | 33.578704 | 393 | 0.511099 | true | 1,569 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91611 | 0.880797 | 0.806907 | __label__eng_Latn | 0.887428 | 0.713047 |
# Supply Network Design 2
## Objective and Prerequisites
Take your supply chain network design skills to the next level in this example. We’ll show you how – given a set of factories, depots, and customers – you can use mathematical optimization to determine which depots to open or close in order to minimize overall ... | 072e7ede6084f374dd436f7b291c84ec7bb868a3 | 24,632 | ipynb | Jupyter Notebook | supply_network_design_1_2/supply_network_design_2_gcl.ipynb | gglockner/modeling-examples | 51575a453d28e1e9435abd865432955b182ba577 | [
"Apache-2.0"
] | 1 | 2021-12-22T06:17:22.000Z | 2021-12-22T06:17:22.000Z | supply_network_design_1_2/supply_network_design_2_gcl.ipynb | Maninaa/modeling-examples | 51575a453d28e1e9435abd865432955b182ba577 | [
"Apache-2.0"
] | null | null | null | supply_network_design_1_2/supply_network_design_2_gcl.ipynb | Maninaa/modeling-examples | 51575a453d28e1e9435abd865432955b182ba577 | [
"Apache-2.0"
] | 1 | 2021-11-29T07:41:53.000Z | 2021-11-29T07:41:53.000Z | 34.84017 | 654 | 0.497808 | true | 5,057 | Qwen/Qwen-72B | 1. YES
2. YES | 0.727975 | 0.855851 | 0.623039 | __label__eng_Latn | 0.916432 | 0.285858 |
# PharmSci 175/275 (UCI)
## What is this??
The material below is a supplement to the quantum mechanics (QM) lecture from Drug Discovery Computing Techniques, PharmSci 175/275 at UC Irvine.
Extensive materials for this course, as well as extensive background and related materials, are available on the course GitHub re... | 3d552e36a5f9245b43bb739b353434d8f4241763 | 18,416 | ipynb | Jupyter Notebook | uci-pharmsci/lectures/QM/psi4_example.ipynb | aakankschit/drug-computing | 3ea4bd12f3b56cbffa8ea43396f3a32c009985a9 | [
"CC-BY-4.0",
"MIT"
] | null | null | null | uci-pharmsci/lectures/QM/psi4_example.ipynb | aakankschit/drug-computing | 3ea4bd12f3b56cbffa8ea43396f3a32c009985a9 | [
"CC-BY-4.0",
"MIT"
] | null | null | null | uci-pharmsci/lectures/QM/psi4_example.ipynb | aakankschit/drug-computing | 3ea4bd12f3b56cbffa8ea43396f3a32c009985a9 | [
"CC-BY-4.0",
"MIT"
] | null | null | null | 31.861592 | 348 | 0.600348 | true | 4,098 | Qwen/Qwen-72B | 1. YES
2. YES | 0.847968 | 0.752013 | 0.637682 | __label__eng_Latn | 0.702688 | 0.319881 |
# Restricted Boltzmann Machine
The restricted Boltzman Machine model is the Joint Probability Distribution which is specified by the Energy Function :
\begin{equation}
P(v,h) = \frac{1}{Z} e^{-E(v,h)}
\end{equation}
The energy function for the RBM is stated as follows:
\begin{equation}
E(v,h) = -b^{T} v - c^{T} h -... | e649070de19651ba88ec1d9a022fab89db5816f7 | 19,705 | ipynb | Jupyter Notebook | Assignment 5.ipynb | Mgosi/Pattern-Recognition | e4a51b41e3ac0e64456adb629da2e8d8825c6b12 | [
"MIT"
] | null | null | null | Assignment 5.ipynb | Mgosi/Pattern-Recognition | e4a51b41e3ac0e64456adb629da2e8d8825c6b12 | [
"MIT"
] | null | null | null | Assignment 5.ipynb | Mgosi/Pattern-Recognition | e4a51b41e3ac0e64456adb629da2e8d8825c6b12 | [
"MIT"
] | null | null | null | 34.937943 | 394 | 0.547577 | true | 3,900 | Qwen/Qwen-72B | 1. YES
2. YES | 0.934395 | 0.826712 | 0.772476 | __label__eng_Latn | 0.612755 | 0.633052 |
```python
# File Contains: Python code containing closed-form solutions for the valuation of European Options,
# American Options, Asian Options, Spread Options, Heat Rate Options, and Implied Volatility
#
# This document demonstrates a Python implementation of some option models described in books written by Davis
# E... | 5db6c867039996d0976d1801cdf552278f5748af | 125,001 | ipynb | Jupyter Notebook | GBS.ipynb | SolitonScientific/Option_Pricing | 8e1ba226583f3f03a2d978d332696129bafa83cc | [
"MIT"
] | null | null | null | GBS.ipynb | SolitonScientific/Option_Pricing | 8e1ba226583f3f03a2d978d332696129bafa83cc | [
"MIT"
] | null | null | null | GBS.ipynb | SolitonScientific/Option_Pricing | 8e1ba226583f3f03a2d978d332696129bafa83cc | [
"MIT"
] | null | null | null | 50.607692 | 1,117 | 0.549836 | true | 29,582 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.831143 | 0.737394 | __label__eng_Latn | 0.930563 | 0.551545 |
# Denmark - Infer parameters
```python
%%capture
## compile PyRoss for this notebook
import os
owd = os.getcwd()
os.chdir('../../')
%run setup.py install
os.chdir(owd)
```
```python
%matplotlib inline
import numpy as np
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
import pyross
import time ... | f3c1c8b737f4cae165006e53aa59cda877ca8136 | 402,360 | ipynb | Jupyter Notebook | examples/inference/SIRinference_Denmark.ipynb | ineskris/pyross | 2ee6deb01b17cdbff19ef89ec6d1e607bceb481c | [
"MIT"
] | null | null | null | examples/inference/SIRinference_Denmark.ipynb | ineskris/pyross | 2ee6deb01b17cdbff19ef89ec6d1e607bceb481c | [
"MIT"
] | null | null | null | examples/inference/SIRinference_Denmark.ipynb | ineskris/pyross | 2ee6deb01b17cdbff19ef89ec6d1e607bceb481c | [
"MIT"
] | null | null | null | 405.604839 | 211,417 | 0.94149 | true | 5,897 | Qwen/Qwen-72B | 1. YES
2. YES | 0.749087 | 0.692642 | 0.518849 | __label__eng_Latn | 0.529158 | 0.04379 |
```python
%%capture
## compile PyRoss for this notebook
import os
owd = os.getcwd()
os.chdir('../../')
%run setup.py install
os.chdir(owd)
%matplotlib inline
```
```python
import numpy as np
import matplotlib.pyplot as plt
import pyross
```
In this notebook we consider a control protocol consisting of a lockdown. Fo... | 87460f04ac6fba7c2c187fb3cf903c8416f25adb | 127,462 | ipynb | Jupyter Notebook | examples/control/ex08 - SEkIkIkR - UK - lockdown.ipynb | ineskris/pyross | 2ee6deb01b17cdbff19ef89ec6d1e607bceb481c | [
"MIT"
] | null | null | null | examples/control/ex08 - SEkIkIkR - UK - lockdown.ipynb | ineskris/pyross | 2ee6deb01b17cdbff19ef89ec6d1e607bceb481c | [
"MIT"
] | null | null | null | examples/control/ex08 - SEkIkIkR - UK - lockdown.ipynb | ineskris/pyross | 2ee6deb01b17cdbff19ef89ec6d1e607bceb481c | [
"MIT"
] | null | null | null | 297.808411 | 37,116 | 0.92142 | true | 2,590 | Qwen/Qwen-72B | 1. YES
2. YES | 0.685949 | 0.7773 | 0.533188 | __label__eng_Latn | 0.925038 | 0.077105 |
We've been working on a [conference paper](https://github.com/gilbertgede/idetc-2013-paper) to demonstrate the ability to do multibody dynamics with Python. We've been calling this work flow [PyDy](http://pydy.org), short for Python Dynamics. Several pieces of the puzzle have come together lately to really demonstrate ... | b422c5de43a5e25c7884c11d21aceb59ad620e17 | 312,307 | ipynb | Jupyter Notebook | examples/npendulum/n-pendulum-control.ipynb | nouiz/pydy | 20c8ca9fc521208ae2144b5b453c14ed4a22a0ec | [
"BSD-3-Clause"
] | 1 | 2019-06-27T05:30:36.000Z | 2019-06-27T05:30:36.000Z | examples/npendulum/n-pendulum-control.ipynb | nouiz/pydy | 20c8ca9fc521208ae2144b5b453c14ed4a22a0ec | [
"BSD-3-Clause"
] | null | null | null | examples/npendulum/n-pendulum-control.ipynb | nouiz/pydy | 20c8ca9fc521208ae2144b5b453c14ed4a22a0ec | [
"BSD-3-Clause"
] | 1 | 2016-10-02T13:43:48.000Z | 2016-10-02T13:43:48.000Z | 224.036585 | 89,808 | 0.806328 | true | 8,524 | Qwen/Qwen-72B | 1. YES
2. YES | 0.787931 | 0.808067 | 0.636701 | __label__eng_Latn | 0.850765 | 0.317601 |
# Week 2
# Lecture 3 - Aug 31
## Least Squares by Gradient Descent
We left off last week needing to minimize a loss function for linear regression, i.e. the minimization problem below.
$$\min\limits_w\,L(w)=\min\limits_w\,\|Xw-y\|^2$$
We will use the method of **gradient descent** to find an approximate solution. ... | 4680baadd352f9eda74b94831707054eaf297c87 | 896,631 | ipynb | Jupyter Notebook | Week-2-Gradient-Descent-Classification/Week2.ipynb | grivasleal/Fall-2021-Neural-Networks | 980d00b28a1733cc298b2a044487a1e45b984326 | [
"MIT"
] | null | null | null | Week-2-Gradient-Descent-Classification/Week2.ipynb | grivasleal/Fall-2021-Neural-Networks | 980d00b28a1733cc298b2a044487a1e45b984326 | [
"MIT"
] | null | null | null | Week-2-Gradient-Descent-Classification/Week2.ipynb | grivasleal/Fall-2021-Neural-Networks | 980d00b28a1733cc298b2a044487a1e45b984326 | [
"MIT"
] | null | null | null | 665.650334 | 816,040 | 0.938343 | true | 12,114 | Qwen/Qwen-72B | 1. YES
2. YES | 0.843895 | 0.83762 | 0.706863 | __label__eng_Latn | 0.992183 | 0.480612 |
# CSE 330 Numerical Analysis Lab
### Lab 8: LU Decomposition
Let a system of equations be,
\begin{equation}
2\boldsymbol{x}_1 - \boldsymbol{x}_{2}+3\boldsymbol{x}_3 = 4
\end{equation}
\begin{equation}
4\boldsymbol{x}_1 + 2\boldsymbol{x}_{2}+\boldsymbol{x}_3 = 1
\end{equation}
\begin{equation}
-6\boldsymbol{x}_1 - ... | b39046a7312bd7d1922200cda8c623c3d0bff989 | 13,581 | ipynb | Jupyter Notebook | LU Decomposition.ipynb | sheikhmishar/Numerical-Analysis-Python | 03a737ba38b372fb52ad773f52cd029f7da2b307 | [
"MIT"
] | null | null | null | LU Decomposition.ipynb | sheikhmishar/Numerical-Analysis-Python | 03a737ba38b372fb52ad773f52cd029f7da2b307 | [
"MIT"
] | null | null | null | LU Decomposition.ipynb | sheikhmishar/Numerical-Analysis-Python | 03a737ba38b372fb52ad773f52cd029f7da2b307 | [
"MIT"
] | null | null | null | 13,581 | 13,581 | 0.593918 | true | 4,156 | Qwen/Qwen-72B | 1. YES
2. YES | 0.939025 | 0.939025 | 0.881768 | __label__eng_Latn | 0.863077 | 0.886975 |
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_datareader.data as web
import datetime as dt
from statsmodels.stats.diagnostic import acorr_ljungbox
from statsmodels.tsa.stattools import acf, pacf, adfuller
```
```python
start_time, end_time = dt.datetime(2016,1,1), dt.... | 22e6c46f813404e661c5da556dd13c4db68c8552 | 129,497 | ipynb | Jupyter Notebook | TimeSeries.ipynb | Hitoshi-Nakanishi/TimeSeries | d97e64d74e45c7db2840e0368a52ae465bd24c2e | [
"MIT"
] | null | null | null | TimeSeries.ipynb | Hitoshi-Nakanishi/TimeSeries | d97e64d74e45c7db2840e0368a52ae465bd24c2e | [
"MIT"
] | null | null | null | TimeSeries.ipynb | Hitoshi-Nakanishi/TimeSeries | d97e64d74e45c7db2840e0368a52ae465bd24c2e | [
"MIT"
] | null | null | null | 403.417445 | 58,046 | 0.92494 | true | 1,383 | Qwen/Qwen-72B | 1. YES
2. YES | 0.817574 | 0.79053 | 0.646317 | __label__eng_Latn | 0.527024 | 0.339943 |
# Supervised Learning: Neural Networks
```python
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='ticks')
import tensorflow as tf
from scipy import optimize
from ipywidgets import interact... | 8f77073df43d493ff2f0388427aa5517bcdfa4cf | 42,672 | ipynb | Jupyter Notebook | notebooks/Day5_2-Neural-Networks.ipynb | fonnesbeck/cqs_machine_learning | 0e82dbde2e09a255d2e6e374db6a3737d2b64e36 | [
"MIT"
] | 5 | 2018-07-26T20:05:02.000Z | 2019-08-14T05:04:36.000Z | notebooks/Day5_2-Neural-Networks.ipynb | noisyoscillator/cqs_machine_learning | 0e82dbde2e09a255d2e6e374db6a3737d2b64e36 | [
"MIT"
] | null | null | null | notebooks/Day5_2-Neural-Networks.ipynb | noisyoscillator/cqs_machine_learning | 0e82dbde2e09a255d2e6e374db6a3737d2b64e36 | [
"MIT"
] | 17 | 2018-08-03T17:08:36.000Z | 2022-03-16T15:03:42.000Z | 39.148624 | 786 | 0.595683 | true | 7,931 | Qwen/Qwen-72B | 1. YES
2. YES | 0.948917 | 0.855851 | 0.812132 | __label__eng_Latn | 0.991118 | 0.725187 |
```
%matplotlib inline
from IPython.display import display
from sympy import *
from sympy.abc import x, a, n
k = Symbol("k", positive=True, integer=True)
init_printing()
```
```
n = 6
tj = [2*pi*j/n for j in range(n)]
display(tj)
```
$$\begin{bmatrix}0, & \frac{\pi}{3}, & \frac{2 \pi}{3}, & \pi, & \frac{4 \pi}{3}, ... | caa622776a3e73d680d12fc53ace120f12546661 | 34,334 | ipynb | Jupyter Notebook | Aufgabe 23).ipynb | bschwb/Numerik | dcd178847104c382474142eae3365b6df76d8dbf | [
"MIT"
] | null | null | null | Aufgabe 23).ipynb | bschwb/Numerik | dcd178847104c382474142eae3365b6df76d8dbf | [
"MIT"
] | null | null | null | Aufgabe 23).ipynb | bschwb/Numerik | dcd178847104c382474142eae3365b6df76d8dbf | [
"MIT"
] | null | null | null | 125.765568 | 24,723 | 0.831275 | true | 978 | Qwen/Qwen-72B | 1. YES
2. YES | 0.914901 | 0.83762 | 0.766339 | __label__kor_Hang | 0.091004 | 0.618795 |
```python
from sympy import *
init_printing(use_latex='mathjax')
Re,theta_r,D,rho,L_x,lam,tau,k,x = symbols('Re theta_r D rho L_x lambda tau k x', positive=True)
C0 = symbols('C0')
```
```python
rho = solve(Re - rho*sqrt(D/rho/theta_r)*L_x/D,rho)[0] # density from Reynolds number Re
V_p = sqrt(D/rho/theta_r) ... | c6c9f411c1bf47625bd7e1f8312eeed8708ba627 | 196,802 | ipynb | Jupyter Notebook | dispersion_analysis/dispersion_analysis_stationary_diffusion1D.ipynb | PTsolvers/PseudoTransientDiffusion.jl | 07b3e2e52d04a3f3f9e8fb724bca740ef57249df | [
"MIT"
] | 1 | 2021-12-06T19:25:10.000Z | 2021-12-06T19:25:10.000Z | dispersion_analysis/dispersion_analysis_stationary_diffusion1D.ipynb | PTsolvers/PseudoTransientDiffusion.jl | 07b3e2e52d04a3f3f9e8fb724bca740ef57249df | [
"MIT"
] | null | null | null | dispersion_analysis/dispersion_analysis_stationary_diffusion1D.ipynb | PTsolvers/PseudoTransientDiffusion.jl | 07b3e2e52d04a3f3f9e8fb724bca740ef57249df | [
"MIT"
] | null | null | null | 172.784899 | 147,871 | 0.847801 | true | 643 | Qwen/Qwen-72B | 1. YES
2. YES | 0.907312 | 0.782662 | 0.710119 | __label__eng_Latn | 0.158003 | 0.488176 |
# Gaussian Processes
In this exercise, you will implement Gaussian process regression and apply it to a toy and a real dataset. We use the notation used in the paper "Rasmussen (2005). Gaussian Processes in Machine Learning" linked on ISIS.
Let us first draw a training set $X = (x_1,\dots,x_n)$ and a test set $X_\sta... | 8f8ff4142ddc13e4215eceb9efcb127afdb3d1b3 | 359,385 | ipynb | Jupyter Notebook | Ex09 - Gaussian Process/sheet09-programming.ipynb | qiaw99/Machine-Learning-1 | ababda51d8fa3cdad1548bf8225991335b912eaf | [
"Apache-2.0"
] | null | null | null | Ex09 - Gaussian Process/sheet09-programming.ipynb | qiaw99/Machine-Learning-1 | ababda51d8fa3cdad1548bf8225991335b912eaf | [
"Apache-2.0"
] | null | null | null | Ex09 - Gaussian Process/sheet09-programming.ipynb | qiaw99/Machine-Learning-1 | ababda51d8fa3cdad1548bf8225991335b912eaf | [
"Apache-2.0"
] | null | null | null | 1,099.036697 | 183,060 | 0.950493 | true | 2,585 | Qwen/Qwen-72B | 1. YES
2. YES | 0.893309 | 0.79053 | 0.706188 | __label__eng_Latn | 0.897972 | 0.479043 |
<a href="https://colab.research.google.com/github/ragnariock/LNU_Ostap_Salo_Kiberg_Arima/blob/master/Arima.ipynb" target="_parent"></a>
```python
import math
import matplotlib.pyplot as plt
from sympy import symbols, diff
import re
import numpy as np
import pandas as pd
from statsmodels.tsa.arima_model import ARIMA... | 2a3c890640c09837190337d17538da0b5e3ef2c7 | 165,000 | ipynb | Jupyter Notebook | FES-31/Ostap/ArimaColaboratory.ipynb | artiiblack/rcs-research | 02aecbbeccc7559ee8b0c2b1a81567e0f236cd3a | [
"MIT"
] | 2 | 2019-09-18T09:49:41.000Z | 2019-10-01T16:20:46.000Z | FES-31/Ostap/ArimaColaboratory.ipynb | artiiblack/rcs-research | 02aecbbeccc7559ee8b0c2b1a81567e0f236cd3a | [
"MIT"
] | null | null | null | FES-31/Ostap/ArimaColaboratory.ipynb | artiiblack/rcs-research | 02aecbbeccc7559ee8b0c2b1a81567e0f236cd3a | [
"MIT"
] | 8 | 2019-07-16T12:35:18.000Z | 2019-12-04T12:07:53.000Z | 139.593909 | 17,090 | 0.847333 | true | 2,861 | Qwen/Qwen-72B | 1. YES
2. YES | 0.868827 | 0.743168 | 0.645684 | __label__kor_Hang | 0.115236 | 0.338472 |
# Physics 256
## Simple Harmonic Oscillators
```python
import style
style._set_css_style('../include/bootstrap.css')
```
## Last Time
### [Notebook Link: 15_Baseball.ipynb](./15_Baseball.ipynb)
- motion of a pitched ball
- drag and the magnus force
- surface roughness of a projectile
## Today
- The simple harm... | 811b3dac5a0ace19252fe35e35ba81502524a9c0 | 11,004 | ipynb | Jupyter Notebook | 4-assets/BOOKS/Jupyter-Notebooks/Overflow/16_SimpleHarmonicMotion.ipynb | impastasyndrome/Lambda-Resource-Static-Assets | 7070672038620d29844991250f2476d0f1a60b0a | [
"MIT"
] | null | null | null | 4-assets/BOOKS/Jupyter-Notebooks/Overflow/16_SimpleHarmonicMotion.ipynb | impastasyndrome/Lambda-Resource-Static-Assets | 7070672038620d29844991250f2476d0f1a60b0a | [
"MIT"
] | null | null | null | 4-assets/BOOKS/Jupyter-Notebooks/Overflow/16_SimpleHarmonicMotion.ipynb | impastasyndrome/Lambda-Resource-Static-Assets | 7070672038620d29844991250f2476d0f1a60b0a | [
"MIT"
] | 1 | 2021-11-05T07:48:26.000Z | 2021-11-05T07:48:26.000Z | 30.065574 | 270 | 0.514995 | true | 2,289 | Qwen/Qwen-72B | 1. YES
2. YES | 0.857768 | 0.882428 | 0.756918 | __label__eng_Latn | 0.798603 | 0.596907 |
# Funciones de forma unidimensionales
Las funciones de forma unidimensionales sirven para aproximar los desplazamientos:
\begin{equation}
w = \alpha_{0} + \alpha_{1} x + \cdots + \alpha_{n} x^{n} = \sum_{i = 0}^{n} \alpha_{i} x^{i}
\end{equation}
## Elemento viga Euler-Bernoulli
Los elementos viga soportan esfuerz... | 722056b9edbcc72ba8e617d1684b52f173b00d83 | 20,779 | ipynb | Jupyter Notebook | Funciones de forma/funciones forma viga.ipynb | ClaudioVZ/Teoria-FEM-Python | 8a4532f282c38737fb08d1216aa859ecb1e5b209 | [
"Artistic-2.0"
] | 1 | 2021-09-28T00:23:45.000Z | 2021-09-28T00:23:45.000Z | Funciones de forma/funciones forma viga.ipynb | ClaudioVZ/Teoria-FEM-Python | 8a4532f282c38737fb08d1216aa859ecb1e5b209 | [
"Artistic-2.0"
] | null | null | null | Funciones de forma/funciones forma viga.ipynb | ClaudioVZ/Teoria-FEM-Python | 8a4532f282c38737fb08d1216aa859ecb1e5b209 | [
"Artistic-2.0"
] | 3 | 2015-12-04T12:42:00.000Z | 2019-10-31T21:50:32.000Z | 29.183989 | 467 | 0.367486 | true | 5,540 | Qwen/Qwen-72B | 1. YES
2. YES | 0.957278 | 0.882428 | 0.844729 | __label__yue_Hant | 0.311866 | 0.800921 |
<table>
<tr align=left><td>
<td>Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Kyle T. Mandli</td>
</table>
```python
%matplotlib inline
import numpy
import matplotlib.pyplot as plt
```
# Root Finding and Optimization
**GOAL:**... | 2379d90f2d3890ddfd84f1752b81ad70a9ad5ab8 | 47,613 | ipynb | Jupyter Notebook | 05_root_finding_optimization.ipynb | antoniopradom/Intro-numerical-methods | c177ccec215df8c3c6b6bb8df68d2527fb5ef2cc | [
"CC0-1.0"
] | null | null | null | 05_root_finding_optimization.ipynb | antoniopradom/Intro-numerical-methods | c177ccec215df8c3c6b6bb8df68d2527fb5ef2cc | [
"CC0-1.0"
] | null | null | null | 05_root_finding_optimization.ipynb | antoniopradom/Intro-numerical-methods | c177ccec215df8c3c6b6bb8df68d2527fb5ef2cc | [
"CC0-1.0"
] | null | null | null | 26.854484 | 453 | 0.459769 | true | 10,299 | Qwen/Qwen-72B | 1. YES
2. YES | 0.872347 | 0.887205 | 0.773951 | __label__eng_Latn | 0.748359 | 0.636479 |
# Incremental control example
Åström & Wittenmark Problem 5.3
We have plant model
$$ H(z) = \frac{z+0.7}{z^2 - 1.8z + 0.81} $$
and controller
$$ F_b(z) = \frac{s_0z^2 + s_1z + s_2}{(z-1)(z + r_1)} $$
Want closed-loop characteristic polynomial $A_c(z) = z^2 - 1.5z + 0.7$ and observer poles in the range $0<\alpha<a$.
##... | e3c7da65b4e5357e03a9d859f8363eba3485eff9 | 43,143 | ipynb | Jupyter Notebook | polynomial-design/notebooks/A-and-W-5.3.ipynb | kjartan-at-tec/mr2007-computerized-control | 16e35f5007f53870eaf344eea1165507505ab4aa | [
"MIT"
] | 2 | 2020-11-07T05:20:37.000Z | 2020-12-22T09:46:13.000Z | polynomial-design/notebooks/A-and-W-5.3.ipynb | kjartan-at-tec/mr2007-computerized-control | 16e35f5007f53870eaf344eea1165507505ab4aa | [
"MIT"
] | 4 | 2020-06-12T20:44:41.000Z | 2020-06-12T20:49:00.000Z | polynomial-design/notebooks/A-and-W-5.3.ipynb | kjartan-at-tec/mr2007-computerized-control | 16e35f5007f53870eaf344eea1165507505ab4aa | [
"MIT"
] | 1 | 2021-03-14T03:55:27.000Z | 2021-03-14T03:55:27.000Z | 95.238411 | 13,892 | 0.79707 | true | 3,018 | Qwen/Qwen-72B | 1. YES
2. YES | 0.779993 | 0.766294 | 0.597704 | __label__eng_Latn | 0.215352 | 0.226996 |
# 7. Bandit Algorithms
**Recommender systems** are a subclass of information filtering system that seek to predict the 'rating' or 'preference' that a user would give to an item.
**k-armed bandits** are one way to solve this recommendation problem. They can also be used in other similar contexts, such as clinical tri... | 98562941f2d56797f9a964b14c97eb418b72922f | 21,408 | ipynb | Jupyter Notebook | 07-bandits.ipynb | AndreiBarsan/dm-notes | 24e5469c4ba9d6be0c8a5da18b8b99968436e69c | [
"Unlicense"
] | 2 | 2016-01-22T14:36:41.000Z | 2017-10-17T07:17:07.000Z | 07-bandits.ipynb | AndreiBarsan/dm-notes | 24e5469c4ba9d6be0c8a5da18b8b99968436e69c | [
"Unlicense"
] | null | null | null | 07-bandits.ipynb | AndreiBarsan/dm-notes | 24e5469c4ba9d6be0c8a5da18b8b99968436e69c | [
"Unlicense"
] | null | null | null | 41.328185 | 332 | 0.583754 | true | 4,491 | Qwen/Qwen-72B | 1. YES
2. YES | 0.907312 | 0.92523 | 0.839472 | __label__eng_Latn | 0.993502 | 0.788709 |
# Design of Retaining Wall
http://structengblog.com/retaining-wall-analysis-ipython-sympy-possible-bim-integration/
```python
from sympy import *
init_printing()
ka, q, gs, z = symbols('k_a q gamma_s z') # soil properties and depth
gfq, gfg = symbols('gamma_fq gamma_fg') # partial load factors
pa, va, ma = symbols(... | 9c68c94142affc728a3d6c20c940faca02684600 | 49,668 | ipynb | Jupyter Notebook | ret_wall.ipynb | satish-annigeri/Notebooks | 92a7dc1d4cf4aebf73bba159d735a2e912fc88bb | [
"CC0-1.0"
] | null | null | null | ret_wall.ipynb | satish-annigeri/Notebooks | 92a7dc1d4cf4aebf73bba159d735a2e912fc88bb | [
"CC0-1.0"
] | null | null | null | ret_wall.ipynb | satish-annigeri/Notebooks | 92a7dc1d4cf4aebf73bba159d735a2e912fc88bb | [
"CC0-1.0"
] | null | null | null | 77.124224 | 7,302 | 0.783804 | true | 1,352 | Qwen/Qwen-72B | 1. YES
2. YES | 0.907312 | 0.817574 | 0.741795 | __label__eng_Latn | 0.593274 | 0.561771 |
# Content:
1. [Simple example](#1.-Simple-example)
2. [Parametric equations](#2.-Parametric-equations)
3. [Polishing the plot](#3.-Polishing-the-plot)
4. [Contour plot](#4.-Contour-plot)
5. [Beginner-level animation](#5.-Beginner-level-animation)
6. [Intermediate-level animation](#6.-Intermediate-level-animation)
## 1... | 2be3803bd0222e8334ab058a30767bc83b6fce5c | 275,619 | ipynb | Jupyter Notebook | notebooks/nm_02_Plotting.ipynb | raghurama123/NumericalMethods | b31737b97e155b0b9b38b0c8bc7a20e90e9c5401 | [
"MIT"
] | 1 | 2022-01-01T01:12:51.000Z | 2022-01-01T01:12:51.000Z | notebooks/nm_02_Plotting.ipynb | raghurama123/NumericalMethods | b31737b97e155b0b9b38b0c8bc7a20e90e9c5401 | [
"MIT"
] | null | null | null | notebooks/nm_02_Plotting.ipynb | raghurama123/NumericalMethods | b31737b97e155b0b9b38b0c8bc7a20e90e9c5401 | [
"MIT"
] | 5 | 2022-01-25T03:40:30.000Z | 2022-02-22T05:38:21.000Z | 475.205172 | 77,208 | 0.943037 | true | 1,985 | Qwen/Qwen-72B | 1. YES
2. YES | 0.879147 | 0.913677 | 0.803256 | __label__eng_Latn | 0.461787 | 0.704565 |
# Baseline Model
Prior to any machine learning, it is prudent to establish a baseline model with which to compare any trained models against. If none of the trained models can beat this "naive" model, then the conclusion is that either machine learning is not suitable for the predictive task or a different learning ap... | e863071e68a06d96121e7443aa1e396c7f078e02 | 13,102 | ipynb | Jupyter Notebook | nobel_physics_prizes/notebooks/5.0-baseline-model.ipynb | covuworie/nobel-physics-prizes | f89a32cd6eb9bbc9119a231bffee89b177ae847a | [
"MIT"
] | 3 | 2019-08-21T05:35:42.000Z | 2020-10-08T21:28:51.000Z | nobel_physics_prizes/notebooks/5.0-baseline-model.ipynb | covuworie/nobel-physics-prizes | f89a32cd6eb9bbc9119a231bffee89b177ae847a | [
"MIT"
] | 139 | 2018-09-01T23:15:59.000Z | 2021-02-02T22:01:39.000Z | nobel_physics_prizes/notebooks/5.0-baseline-model.ipynb | covuworie/nobel-physics-prizes | f89a32cd6eb9bbc9119a231bffee89b177ae847a | [
"MIT"
] | null | null | null | 47.129496 | 1,060 | 0.676996 | true | 2,011 | Qwen/Qwen-72B | 1. YES
2. YES | 0.870597 | 0.875787 | 0.762458 | __label__eng_Latn | 0.960626 | 0.609777 |
# Quantization of Signals
*This jupyter notebook is part of a [collection of notebooks](../index.ipynb) on various topics of Digital Signal Processing.
## Introduction
[Digital signal processors](https://en.wikipedia.org/wiki/Digital_signal_processor) and general purpose processors can only perform arithmetic operat... | 2c48144b04275ccbe7787d6ba8705d06f80bf974 | 49,410 | ipynb | Jupyter Notebook | Lectures_Advanced-DSP/quantization/introduction.ipynb | lev1khachatryan/ASDS_DSP | 9059d737f6934b81a740c79b33756f7ec9ededb3 | [
"MIT"
] | 1 | 2020-12-29T18:02:13.000Z | 2020-12-29T18:02:13.000Z | Lectures_Advanced-DSP/quantization/introduction.ipynb | lev1khachatryan/ASDS_DSP | 9059d737f6934b81a740c79b33756f7ec9ededb3 | [
"MIT"
] | null | null | null | Lectures_Advanced-DSP/quantization/introduction.ipynb | lev1khachatryan/ASDS_DSP | 9059d737f6934b81a740c79b33756f7ec9ededb3 | [
"MIT"
] | null | null | null | 257.34375 | 41,520 | 0.911799 | true | 1,394 | Qwen/Qwen-72B | 1. YES
2. YES | 0.865224 | 0.845942 | 0.73193 | __label__eng_Latn | 0.990379 | 0.53885 |
# Characterization of Systems in the Time Domain
*This Jupyter notebook is part of a [collection of notebooks](../index.ipynb) in the bachelors module Signals and Systems, Communications Engineering, Universität Rostock. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-r... | 1c8ffe90b56fe8cc46d9d670b24922e6ac5c5e9a | 173,348 | ipynb | Jupyter Notebook | systems_time_domain/impulse_response.ipynb | spatialaudio/signals-and-systems-lecture | 93e2f3488dc8f7ae111a34732bd4d13116763c5d | [
"MIT"
] | 243 | 2016-04-01T14:21:00.000Z | 2022-03-28T20:35:09.000Z | systems_time_domain/impulse_response.ipynb | bagustris/signals-and-systems-lecture | 08a8c7ea21f88c20b457daffe77fcca021c53137 | [
"MIT"
] | 6 | 2016-04-11T06:28:17.000Z | 2021-11-10T10:59:35.000Z | systems_time_domain/impulse_response.ipynb | bagustris/signals-and-systems-lecture | 08a8c7ea21f88c20b457daffe77fcca021c53137 | [
"MIT"
] | 63 | 2017-04-20T00:46:03.000Z | 2022-03-30T14:07:09.000Z | 64.827225 | 13,754 | 0.636488 | true | 1,836 | Qwen/Qwen-72B | 1. YES
2. YES | 0.805632 | 0.803174 | 0.647063 | __label__eng_Latn | 0.978205 | 0.341674 |
**It would be nice to do a Judea Pearl-type DAG**
Let's say we're interested in predicting a college-football game. What are all the things that influence the outcome? Here's a list of things that come to mind:
* Team A's offensive strength ($A_o$).
* Team B's offensive strength ($B_o$).
* Team A's defensive strength... | 8ad7f57b780d4c98f72e61ca9cf9793144dbe64b | 48,914 | ipynb | Jupyter Notebook | Introduction.ipynb | jtwalsh0/methods | a7d862c02260fcdf12b5ed08a3e0d9f22aff6624 | [
"MIT"
] | null | null | null | Introduction.ipynb | jtwalsh0/methods | a7d862c02260fcdf12b5ed08a3e0d9f22aff6624 | [
"MIT"
] | null | null | null | Introduction.ipynb | jtwalsh0/methods | a7d862c02260fcdf12b5ed08a3e0d9f22aff6624 | [
"MIT"
] | null | null | null | 151.906832 | 15,574 | 0.852558 | true | 2,913 | Qwen/Qwen-72B | 1. YES
2. YES | 0.872347 | 0.79053 | 0.689617 | __label__eng_Latn | 0.998265 | 0.440543 |
<!-- dom:TITLE: Demo - Sparse Chebyshev-Petrov-Galerkin methods for differentiation -->
# Demo - Sparse Chebyshev-Petrov-Galerkin methods for differentiation
<!-- dom:AUTHOR: Mikael Mortensen Email:mikaem@math.uio.no at Department of Mathematics, University of Oslo. -->
<!-- Author: -->
**Mikael Mortensen** (email: `... | c9663b3d7ab82ee13e1e0ad50c2fad9496cb57a5 | 23,508 | ipynb | Jupyter Notebook | content/sparsity.ipynb | mikaem/shenfun-demos | c2ad13d62866e0812068673fdb6a7ef68ecfb7f2 | [
"BSD-2-Clause"
] | null | null | null | content/sparsity.ipynb | mikaem/shenfun-demos | c2ad13d62866e0812068673fdb6a7ef68ecfb7f2 | [
"BSD-2-Clause"
] | 1 | 2021-09-21T16:10:01.000Z | 2021-09-21T16:10:01.000Z | content/sparsity.ipynb | mikaem/shenfun-demos | c2ad13d62866e0812068673fdb6a7ef68ecfb7f2 | [
"BSD-2-Clause"
] | null | null | null | 28.322892 | 173 | 0.511528 | true | 5,018 | Qwen/Qwen-72B | 1. YES
2. YES | 0.76908 | 0.766294 | 0.589341 | __label__eng_Latn | 0.929198 | 0.207567 |
<p style="font-size:32px;text-align:center"> <b>Social network Graph Link Prediction - Facebook Challenge</b> </p>
```python
#Importing Libraries
# please do go through this python notebook:
import warnings
warnings.filterwarnings("ignore")
import csv
import pandas as pd#pandas to create small dataframes
import da... | 59658ce05c2039dc1b32750ebac74e4c2e7824da | 454,113 | ipynb | Jupyter Notebook | suny.sn1@gmail.com FB_featurization and Modeling.ipynb | sunneysood/appliedai | 62770d57bc4bb30a0e4ed19b915ebb1888cf962c | [
"Apache-2.0"
] | 1 | 2020-04-21T14:31:35.000Z | 2020-04-21T14:31:35.000Z | suny.sn1@gmail.com FB_featurization and Modeling.ipynb | sunneysood/appliedai | 62770d57bc4bb30a0e4ed19b915ebb1888cf962c | [
"Apache-2.0"
] | null | null | null | suny.sn1@gmail.com FB_featurization and Modeling.ipynb | sunneysood/appliedai | 62770d57bc4bb30a0e4ed19b915ebb1888cf962c | [
"Apache-2.0"
] | null | null | null | 150.717889 | 86,356 | 0.877436 | true | 15,335 | Qwen/Qwen-72B | 1. YES
2. YES | 0.763484 | 0.675765 | 0.515935 | __label__eng_Latn | 0.441085 | 0.03702 |
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