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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
```python
#In this program, we calculate the largest water cluster on Earth in the presence of different levels of water
#Libraries
from scipy.ndimage.filters import gaussian_filter
import matplotlib.pyplot as plt
from netCDF4 import Dataset
import numpy as np
import os
from tqdm import tqdm
from scipy.ndimage import m... | e82dc011355473063756f79c4b3f77898baf9aa9 | 9,248 | ipynb | Jupyter Notebook | Water_Clusters.ipynb | eurusebr/CMB_Percolation | 6d11cdec20da8b632cbb95a7927d26f3aa19e1b3 | [
"MIT"
] | null | null | null | Water_Clusters.ipynb | eurusebr/CMB_Percolation | 6d11cdec20da8b632cbb95a7927d26f3aa19e1b3 | [
"MIT"
] | null | null | null | Water_Clusters.ipynb | eurusebr/CMB_Percolation | 6d11cdec20da8b632cbb95a7927d26f3aa19e1b3 | [
"MIT"
] | 2 | 2021-12-22T09:24:23.000Z | 2022-01-03T11:22:07.000Z | 34 | 130 | 0.515679 | true | 1,776 | Qwen/Qwen-72B | 1. YES
2. YES | 0.83762 | 0.709019 | 0.593889 | __label__eng_Latn | 0.527837 | 0.218132 |
# Polynomial Unconstrained Boolean Optimization Problem in MBQC
<em> Copyright (c) 2021 Institute for Quantum Computing, Baidu Inc. All Rights Reserved. </em>
In the tutorial [Measurement-based Quantum Approximate Optimization Algorithm](QAOA_EN.ipynb), we give a brief introduction to the **polynomial unconstrained b... | 27324ef22ca19997b5d7cf9fc7182cb2e148112c | 20,808 | ipynb | Jupyter Notebook | tutorial/mbqc/PUBO_EN.ipynb | gsq7474741/Quantum | 16e7d3bf2dba7e94e6faf5c853faf0e913e1f268 | [
"Apache-2.0"
] | 1 | 2020-07-14T14:10:23.000Z | 2020-07-14T14:10:23.000Z | tutorial/mbqc/PUBO_EN.ipynb | gsq7474741/Quantum | 16e7d3bf2dba7e94e6faf5c853faf0e913e1f268 | [
"Apache-2.0"
] | null | null | null | tutorial/mbqc/PUBO_EN.ipynb | gsq7474741/Quantum | 16e7d3bf2dba7e94e6faf5c853faf0e913e1f268 | [
"Apache-2.0"
] | null | null | null | 38.391144 | 619 | 0.58194 | true | 3,987 | Qwen/Qwen-72B | 1. YES
2. YES | 0.817574 | 0.699254 | 0.571693 | __label__eng_Latn | 0.969564 | 0.166563 |
# Numerical Solution of the Helmholtz Equation using the Finite Element Method
This notebook illustrates the numerical solution of the wave equation for harmonic excitation using the so called [Finite Element Method](https://en.wikipedia.org/wiki/Finite_element_method) (FEM). The method aims at an approximate solution... | fc955065cb6069ff098dcf7dbecee02aa945e870 | 849,767 | ipynb | Jupyter Notebook | FEM_Helmholtz_equation.ipynb | wgddd/computational_acoustics | 6e1f249b562a73b3bb295981bea9d80d218a213f | [
"MIT"
] | 80 | 2018-10-17T09:51:06.000Z | 2022-02-09T02:09:48.000Z | FEM_Helmholtz_equation.ipynb | Zhengyu-Huang/computational_acoustics | 6e1f249b562a73b3bb295981bea9d80d218a213f | [
"MIT"
] | null | null | null | FEM_Helmholtz_equation.ipynb | Zhengyu-Huang/computational_acoustics | 6e1f249b562a73b3bb295981bea9d80d218a213f | [
"MIT"
] | 21 | 2018-10-19T11:55:53.000Z | 2022-02-10T15:24:20.000Z | 2,434.862464 | 225,992 | 0.962826 | true | 2,867 | Qwen/Qwen-72B | 1. YES
2. YES | 0.952574 | 0.826712 | 0.787504 | __label__eng_Latn | 0.923359 | 0.667969 |
# Free Body Diagram for particles
Renato Naville Watanabe
```python
import numpy as np
import matplotlib.pyplot as plt
%matplotlib notebook
```
### Steps to draw a free-body diagram
1 - Draw separately each object considered in the problem. How you separate depends on what questions you want to answer.
2 - Ident... | bb4ef9feecc63c866fd214ea860ea42f35a7ece8 | 138,295 | ipynb | Jupyter Notebook | notebooks/FBDParticles.ipynb | ahmadhassan01/bmc | 3114b7d3ecd1f7c678fac0c04e8e139ac2898992 | [
"MIT"
] | null | null | null | notebooks/FBDParticles.ipynb | ahmadhassan01/bmc | 3114b7d3ecd1f7c678fac0c04e8e139ac2898992 | [
"MIT"
] | null | null | null | notebooks/FBDParticles.ipynb | ahmadhassan01/bmc | 3114b7d3ecd1f7c678fac0c04e8e139ac2898992 | [
"MIT"
] | null | null | null | 151.639254 | 26,696 | 0.840428 | true | 11,823 | Qwen/Qwen-72B | 1. YES
2. YES | 0.763484 | 0.822189 | 0.627728 | __label__eng_Latn | 0.618575 | 0.296753 |
# Hakwes Process w/ tensorflow
See [`arxiv:1507.02822`](https://arxiv.org/pdf/1507.02822.pdf).
The conditional intensity function is parametrized as: $\mu(t) = \mu_0 + \alpha \sum_{t_i < t} e^{-\beta(t - t_i)}$
```python
%matplotlib inline
```
```python
import numpy as np
import matplotlib.pyplot as plt
import te... | 44c2fb7e5161b418269fc98f3e9976250389f401 | 36,869 | ipynb | Jupyter Notebook | BuildingBlocks/PoissonPointProcess.ipynb | chmp/misc-exp | 2edc2ed598eb59f4ccb426e7a5c1a23343a6974b | [
"MIT"
] | 6 | 2017-10-31T20:54:37.000Z | 2020-10-23T19:03:00.000Z | BuildingBlocks/PoissonPointProcess.ipynb | chmp/misc-exp | 2edc2ed598eb59f4ccb426e7a5c1a23343a6974b | [
"MIT"
] | 7 | 2020-03-24T16:14:34.000Z | 2021-03-18T20:51:37.000Z | BuildingBlocks/PoissonPointProcess.ipynb | chmp/misc-exp | 2edc2ed598eb59f4ccb426e7a5c1a23343a6974b | [
"MIT"
] | 1 | 2019-07-29T07:55:49.000Z | 2019-07-29T07:55:49.000Z | 90.587224 | 26,066 | 0.820228 | true | 2,068 | Qwen/Qwen-72B | 1. YES
2. YES | 0.874077 | 0.737158 | 0.644333 | __label__eng_Latn | 0.205158 | 0.335333 |
<a href="https://colab.research.google.com/github/knazari/Advanced_Robotic_2022/blob/main/Week5/AR_w5_ProMP.ipynb" target="_parent"></a>
```python
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
import numpy.matlib as mat
```
**Probabilistic Movement Primitives**
==================... | 525fd00d2405218e19e94a5b524d4c2c7bf7a999 | 30,156 | ipynb | Jupyter Notebook | Week5/AR_w5_ProMP.ipynb | knazari/Advanced_Robotic_2022 | 17b21c917dd9d7522b867ca8ef4c21c142bb05e3 | [
"Apache-2.0"
] | null | null | null | Week5/AR_w5_ProMP.ipynb | knazari/Advanced_Robotic_2022 | 17b21c917dd9d7522b867ca8ef4c21c142bb05e3 | [
"Apache-2.0"
] | null | null | null | Week5/AR_w5_ProMP.ipynb | knazari/Advanced_Robotic_2022 | 17b21c917dd9d7522b867ca8ef4c21c142bb05e3 | [
"Apache-2.0"
] | null | null | null | 46.465331 | 892 | 0.51794 | true | 6,468 | Qwen/Qwen-72B | 1. YES
2. YES | 0.863392 | 0.7773 | 0.671114 | __label__eng_Latn | 0.94611 | 0.397554 |
# SymPy
`SymPy` is a package for symbolic calculations in python, similar to *Mathematica*. It works with expressions containing symbols.
```python
from sympy import *
init_printing()
```
Symbols are basic bricks used to construct expressions.
Each symbol has a name used for printing expressions. Objects of the c... | 183af3da10348f4e367d312987ef698f255c7c88 | 712,436 | ipynb | Jupyter Notebook | Gallery/sympy.ipynb | RonSheely/jupyter-notebooks | 8d9b37503e59675d392f9b7c3a5a71a1c73a5373 | [
"MIT"
] | null | null | null | Gallery/sympy.ipynb | RonSheely/jupyter-notebooks | 8d9b37503e59675d392f9b7c3a5a71a1c73a5373 | [
"MIT"
] | null | null | null | Gallery/sympy.ipynb | RonSheely/jupyter-notebooks | 8d9b37503e59675d392f9b7c3a5a71a1c73a5373 | [
"MIT"
] | null | null | null | 132.570897 | 114,478 | 0.863905 | true | 5,422 | Qwen/Qwen-72B | 1. YES
2. YES | 0.921922 | 0.853913 | 0.787241 | __label__eng_Latn | 0.955014 | 0.667357 |
<a id='top'></a>
# Complex vibration modes
Complex vibration modes arise in experimental research and numerical simulations when non proportional damping is adopted. In such cases a state space formulation of the second order differential dynamic equilibrium equation is the preferred way to adress the problem.
This ... | 5873ed68b54a069907da3f19238679802104a6ce | 192,412 | ipynb | Jupyter Notebook | complex_vibration_modes_SS.ipynb | pxcandeias/py-notebooks | 1557bfeb61f301c075be88fa35936b9aa3964862 | [
"MIT"
] | 3 | 2016-11-12T10:35:42.000Z | 2021-04-09T00:20:37.000Z | complex_vibration_modes_SS.ipynb | pxcandeias/py-notebooks | 1557bfeb61f301c075be88fa35936b9aa3964862 | [
"MIT"
] | null | null | null | complex_vibration_modes_SS.ipynb | pxcandeias/py-notebooks | 1557bfeb61f301c075be88fa35936b9aa3964862 | [
"MIT"
] | null | null | null | 175.398359 | 55,000 | 0.890064 | true | 5,636 | Qwen/Qwen-72B | 1. YES
2. YES | 0.884039 | 0.810479 | 0.716495 | __label__eng_Latn | 0.839009 | 0.50299 |
$\nabla ^2 \mathbf{E} + k^2 \mathbf{E} = 0$
$\nabla ^2 \mathbf{H} + k^2 \mathbf{H} = 0$
$ k = \sqrt{\omega ^2 \mu \varepsilon - i \omega \mu \sigma } $
$k_{ground} \simeq (1-i) \sqrt{ \frac{\omega \mu \sigma}{2} }$
$k_{air} \simeq \omega \sqrt{ \mu_0 \varepsilon_0}$
$\begin{split}\left(\begin{matrix} E_{x} \\ E_{... | d96da85269151a311e94904a95d49599115a9e09 | 3,065 | ipynb | Jupyter Notebook | SciPy2016/MTwork/Poster/.ipynb_checkpoints/Equations-checkpoint.ipynb | simpeg/simpegExamples | 38b8064fb854d809f72b7f1ca8b8096bca696af1 | [
"MIT"
] | 1 | 2021-08-07T13:46:54.000Z | 2021-08-07T13:46:54.000Z | SciPy2016/MTwork/Poster/.ipynb_checkpoints/Equations-checkpoint.ipynb | simpeg/simpegExamples | 38b8064fb854d809f72b7f1ca8b8096bca696af1 | [
"MIT"
] | 1 | 2016-07-27T22:20:36.000Z | 2016-07-27T22:20:36.000Z | SciPy2016/MTwork/Poster/.ipynb_checkpoints/Equations-checkpoint.ipynb | simpeg/simpegExamples | 38b8064fb854d809f72b7f1ca8b8096bca696af1 | [
"MIT"
] | null | null | null | 22.210145 | 268 | 0.460685 | true | 507 | Qwen/Qwen-72B | 1. YES
2. YES | 0.92079 | 0.760651 | 0.700399 | __label__kor_Hang | 0.069015 | 0.465594 |
_Note: This Python notebook assumes that this is your first time to see/work on a Python code. It also assumes that you are new to recommendation systems, but familiar with it as a high-level concept._
# VANILLA COLLABORTIVE FILTERING
_Prepared for EMBA 2022 by EF Legara_
---
Here, we use Python libraries to help ... | 797245e37b40c5282f962700b440627ea98fadb5 | 189,058 | ipynb | Jupyter Notebook | Collaborative Filtering.ipynb | eflegara/Business-Analytics | 15a56027925fbb310cb434fb9a866aaf08a8ac34 | [
"MIT"
] | 1 | 2021-04-25T13:46:14.000Z | 2021-04-25T13:46:14.000Z | Collaborative Filtering.ipynb | eflegara/Business-Analytics | 15a56027925fbb310cb434fb9a866aaf08a8ac34 | [
"MIT"
] | null | null | null | Collaborative Filtering.ipynb | eflegara/Business-Analytics | 15a56027925fbb310cb434fb9a866aaf08a8ac34 | [
"MIT"
] | null | null | null | 88.262372 | 54,624 | 0.748881 | true | 15,036 | Qwen/Qwen-72B | 1. YES
2. YES | 0.760651 | 0.746139 | 0.567551 | __label__eng_Latn | 0.314381 | 0.156941 |
```python
from sympy import Symbol, Matrix, symbols, sin, lambdify
import time
import numpy as np
from collections import OrderedDict
```
```python
id_wind=False
bo=False
```
R = Matrix([[1, 0,0],[0,np.cos(10*np.pi/180),-np.sin(10*np.pi/180)],[0,np.sin(10*np.pi/180), np.cos(10*np.pi/180)]])
c=Matrix([0,0,0])
v_B=... | 116f8cd4be700920a5c1a8826e7559e55831901f | 23,256 | ipynb | Jupyter Notebook | 2_5_model_avion_generator.ipynb | altlnt/id_modele_reel | f67fdc66a207108b1fb6af0a7197bf590997cfbd | [
"MIT"
] | null | null | null | 2_5_model_avion_generator.ipynb | altlnt/id_modele_reel | f67fdc66a207108b1fb6af0a7197bf590997cfbd | [
"MIT"
] | null | null | null | 2_5_model_avion_generator.ipynb | altlnt/id_modele_reel | f67fdc66a207108b1fb6af0a7197bf590997cfbd | [
"MIT"
] | null | null | null | 55.636364 | 237 | 0.537754 | true | 6,057 | Qwen/Qwen-72B | 1. YES
2. YES | 0.879147 | 0.689306 | 0.606001 | __label__kor_Hang | 0.157907 | 0.246273 |
```python
%matplotlib inline
```
Neural Transfer with PyTorch
============================
**Author**: `Alexis Jacq <https://alexis-jacq.github.io>`_
Introduction
------------
Welcome! This tutorial explains how to impletment the
`Neural-Style <https://arxiv.org/abs/1508.06576>`__ algorithm developed
by Leon A. Gat... | ce13b4ebb39147c64e7e2b1bd22d792d34a852f4 | 26,797 | ipynb | Jupyter Notebook | neural_style_tutorial.ipynb | bazitur/dlschl_project | c05b70f4262819389d46d3e16a70fe9dac4ae644 | [
"Apache-2.0"
] | null | null | null | neural_style_tutorial.ipynb | bazitur/dlschl_project | c05b70f4262819389d46d3e16a70fe9dac4ae644 | [
"Apache-2.0"
] | null | null | null | neural_style_tutorial.ipynb | bazitur/dlschl_project | c05b70f4262819389d46d3e16a70fe9dac4ae644 | [
"Apache-2.0"
] | null | null | null | 96.740072 | 5,359 | 0.640333 | true | 5,467 | Qwen/Qwen-72B | 1. YES
2. YES | 0.808067 | 0.815232 | 0.658763 | __label__eng_Latn | 0.990601 | 0.368857 |
```python
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
#sns.set_theme(style="whitegrid")
sns.set(rc={'figure.figsize':(16,13)})
```
```python
# Absolute error
def AE(y, y_hat, t, T):
resid = np.abs(y - y_hat)
return resid
#############################... | 4e7e6acb1c89cb712a92f851e32f3126d6293361 | 128,787 | ipynb | Jupyter Notebook | analysis/Temporal loss parameterized version.ipynb | Mikeriess/ML4PM_Temp_losses | 6b31c33c942c2473f237d5e29153aebc2921be47 | [
"MIT"
] | null | null | null | analysis/Temporal loss parameterized version.ipynb | Mikeriess/ML4PM_Temp_losses | 6b31c33c942c2473f237d5e29153aebc2921be47 | [
"MIT"
] | null | null | null | analysis/Temporal loss parameterized version.ipynb | Mikeriess/ML4PM_Temp_losses | 6b31c33c942c2473f237d5e29153aebc2921be47 | [
"MIT"
] | null | null | null | 103.693237 | 42,764 | 0.794614 | true | 7,014 | Qwen/Qwen-72B | 1. YES
2. YES | 0.835484 | 0.721743 | 0.603005 | __label__kor_Hang | 0.207284 | 0.239312 |
<a href="https://colab.research.google.com/github/leehanchung/cs224w/blob/main/notebooks/XCS224W_Colab3.ipynb" target="_parent"></a>
# **CS224W - Colab 3**
In Colab 2 we constructed GNN models by using PyTorch Geometric's built in GCN layer, `GCNConv`. In this Colab we will go a step deeper and implement the **GraphS... | 4af3f88bf7ae95a7870f7e40323bdc078ed824b1 | 344,974 | ipynb | Jupyter Notebook | notebooks/XCS224W_Colab3.ipynb | leehanchung/cs224w | 4e7bba7a2769c5ed016c53e165535a2bd34f7b22 | [
"MIT"
] | 10 | 2021-09-15T06:52:47.000Z | 2022-03-10T16:11:30.000Z | notebooks/XCS224W_Colab3.ipynb | leehanchung/cs224w | 4e7bba7a2769c5ed016c53e165535a2bd34f7b22 | [
"MIT"
] | null | null | null | notebooks/XCS224W_Colab3.ipynb | leehanchung/cs224w | 4e7bba7a2769c5ed016c53e165535a2bd34f7b22 | [
"MIT"
] | null | null | null | 123.161014 | 162,342 | 0.795538 | true | 28,338 | Qwen/Qwen-72B | 1. YES
2. YES | 0.709019 | 0.819893 | 0.58132 | __label__eng_Latn | 0.832028 | 0.188931 |
# Discrete Choice
Discrete choice models are models that model a single (mutually exclusive) choice, in contrast to the standard models where a quantity is estimated.
In this notebook we will try to get you familiarized with discrete choice, the difference between logit and probit, and how to implement them (and mor... | 52f732cb896b09cc4122c5f28b957c1166610015 | 89,747 | ipynb | Jupyter Notebook | Notebooks/Assignment 3/.ipynb_checkpoints/discretechoice-checkpoint.ipynb | JRMfer/ABM_individual | e1ec9dd5301d69a61e40ac0c1d9bee694433780f | [
"MIT"
] | 1 | 2020-02-28T10:29:35.000Z | 2020-02-28T10:29:35.000Z | Notebooks/Assignment 3/discretechoice.ipynb | JRMfer/ABM_individual | e1ec9dd5301d69a61e40ac0c1d9bee694433780f | [
"MIT"
] | null | null | null | Notebooks/Assignment 3/discretechoice.ipynb | JRMfer/ABM_individual | e1ec9dd5301d69a61e40ac0c1d9bee694433780f | [
"MIT"
] | null | null | null | 128.027104 | 30,332 | 0.842702 | true | 3,973 | Qwen/Qwen-72B | 1. YES
2. YES | 0.855851 | 0.749087 | 0.641107 | __label__eng_Latn | 0.981237 | 0.327837 |
# Neural Nets v2
`nn_v2`
Should do [Working efficiently with jupyter lab](https://florianwilhelm.info/2018/11/working_efficiently_with_jupyter_lab/)
When this was a notebook with integrated tests, we did: \
`
%load_ext autoreload
%autoreload 2
%matplotlib widget
#%matplotlib inline`
```python
# import Importing_Not... | 9663912238322971e6d9618b8dffd70a37cf3c40 | 10,184 | ipynb | Jupyter Notebook | nbs/OLD/nn_v2.ipynb | pramasoul/aix | 98333b875f6c6cda6dee86e6eab02c5ddc622543 | [
"MIT"
] | null | null | null | nbs/OLD/nn_v2.ipynb | pramasoul/aix | 98333b875f6c6cda6dee86e6eab02c5ddc622543 | [
"MIT"
] | 1 | 2021-11-29T03:44:00.000Z | 2021-12-19T05:34:04.000Z | nbs/OLD/nn_v2.ipynb | pramasoul/aix | 98333b875f6c6cda6dee86e6eab02c5ddc622543 | [
"MIT"
] | null | null | null | 29.433526 | 298 | 0.508739 | true | 1,721 | Qwen/Qwen-72B | 1. YES
2. YES | 0.868827 | 0.754915 | 0.65589 | __label__eng_Latn | 0.849812 | 0.362184 |
# Tetracycline Resistance Model
(c) 2021 Tom Röschinger. This work is licensed under a
[Creative Commons Attribution License CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/).
All code contained herein is licensed under an
[MIT license](https://opensource.org/licenses/MIT).
```julia
using SymPy, Polynomial... | b5b14cad0185ba7fd682ca7b6d5a9da312948fcd | 84,394 | ipynb | Jupyter Notebook | code/exploratory/old/Untitled1-Copy1.ipynb | RPGroup-PBoC/fit_seq | 7cc7931fd2edf421591de293354348fb940a1a32 | [
"MIT"
] | null | null | null | code/exploratory/old/Untitled1-Copy1.ipynb | RPGroup-PBoC/fit_seq | 7cc7931fd2edf421591de293354348fb940a1a32 | [
"MIT"
] | null | null | null | code/exploratory/old/Untitled1-Copy1.ipynb | RPGroup-PBoC/fit_seq | 7cc7931fd2edf421591de293354348fb940a1a32 | [
"MIT"
] | null | null | null | 49.585194 | 5,184 | 0.569851 | true | 5,377 | Qwen/Qwen-72B | 1. YES
2. YES | 0.891811 | 0.600188 | 0.535255 | __label__eng_Latn | 0.216076 | 0.081905 |
# 多元高斯分布
一个向量形式的随机变量$X=\left[X_1\cdots X_n\right]^T$，期望为$\mu\in\mathbb R^n$，协方差矩阵为$\varSigma\in\mathbb S_{++}^n$（在[线性代数](sn01.ipynb)笔记中$\mathbb S_{++}^n$为$n\times n$正定对称矩阵空间，具体定义为$\mathbb S_{++}^n=\left\{A\in\mathbb R^{n\times n}: A=A^T,\ \forall x\in\mathbb R^n\land x\neq0\to x^TAx\gt0\right\}$），如果随机变量的概率密度函数（这篇笔记中我们... | 19e5906e34b3f3898fb7beb651c9b165c4a20913 | 17,195 | ipynb | Jupyter Notebook | 0-BasicConcept/note/sn07.ipynb | PeterChenYijie/MachineLearningZeroToALL | b14005c3e0b5a39a0ba82db5c9791f682b5effd5 | [
"MIT"
] | 8 | 2018-04-20T09:10:20.000Z | 2019-02-16T07:50:32.000Z | 0-BasicConcept/note/sn07.ipynb | DeepInDeeper/MachineLearningZeroToALL | b14005c3e0b5a39a0ba82db5c9791f682b5effd5 | [
"MIT"
] | null | null | null | 0-BasicConcept/note/sn07.ipynb | DeepInDeeper/MachineLearningZeroToALL | b14005c3e0b5a39a0ba82db5c9791f682b5effd5 | [
"MIT"
] | 4 | 2020-01-27T00:55:59.000Z | 2021-03-25T00:07:56.000Z | 57.316667 | 524 | 0.566967 | true | 8,316 | Qwen/Qwen-72B | 1. YES
2. YES | 0.817574 | 0.63341 | 0.51786 | __label__yue_Hant | 0.115062 | 0.041492 |
```python
from epipack import SymbolicEpiModel, DeterministicEpiModel, StochasticEpiModel
import sympy as sy
%matplotlib notebook
```
```python
%matplotlib notebook
```
```python
S, I, R, eta, rho, omega = sy.symbols("S I R eta rho omega")
```
```python
SIRS = SymbolicEpiModel([S,I,R])
SIRS.set_processes([
... | fffb70aa17f69cf9b27d7d90af4427809d9caf39 | 248,315 | ipynb | Jupyter Notebook | cookbook/notebooks/epipack_SIRS_example.ipynb | PaPeK/epipack | 52fb26ba35672fbce1f2f598eac2ed71e6fcb90f | [
"MIT"
] | null | null | null | cookbook/notebooks/epipack_SIRS_example.ipynb | PaPeK/epipack | 52fb26ba35672fbce1f2f598eac2ed71e6fcb90f | [
"MIT"
] | null | null | null | cookbook/notebooks/epipack_SIRS_example.ipynb | PaPeK/epipack | 52fb26ba35672fbce1f2f598eac2ed71e6fcb90f | [
"MIT"
] | null | null | null | 129.939822 | 87,055 | 0.812037 | true | 1,079 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91611 | 0.800692 | 0.733522 | __label__eng_Latn | 0.196617 | 0.542548 |
### Simplification - *Polynomial*
```python
from sympy import *
x,y,z = symbols('x y z')
init_printing(use_unicode=False)
```
```python
simplify(sin(x)**2+cos(x)**2)
simplify(x**3-y**3)
simplify((x-y)*(x**2+x*y+y**2))
# Note: this func doesn't fit every expr, also maybe too slow.
simplify(x**2+2*x+1)
```
```py... | c3ed13f278dc5c8a9474729a853468e37b667fa4 | 108,084 | ipynb | Jupyter Notebook | sympy-part02-simplify.ipynb | codingEzio/code_python_learn_math | bd7869d05e1b4ec250cc5fa13470a960b299654e | [
"Unlicense"
] | null | null | null | sympy-part02-simplify.ipynb | codingEzio/code_python_learn_math | bd7869d05e1b4ec250cc5fa13470a960b299654e | [
"Unlicense"
] | null | null | null | sympy-part02-simplify.ipynb | codingEzio/code_python_learn_math | bd7869d05e1b4ec250cc5fa13470a960b299654e | [
"Unlicense"
] | null | null | null | 78.208394 | 3,276 | 0.817457 | true | 1,111 | Qwen/Qwen-72B | 1. YES
2. YES | 0.960952 | 0.847968 | 0.814856 | __label__eng_Latn | 0.30154 | 0.731516 |
# 微积分
SymPy支持微分和积分操作,也支持推导极限
```python
from sympy import init_printing
init_printing(use_unicode=True)
```
```python
from sympy import symbols
x, y, z = symbols('x y z')
```
## `diff()`微分(求导)
```python
from sympy import diff
```
```python
diff(x**3+x**2+x+1)
```
`diff(exp,var,level)`可以求多阶导数,需要指定变量和阶数
```py... | 1d044a2a5206fcae0ba2dda264bab9c77c7e9ab8 | 61,142 | ipynb | Jupyter Notebook | src/数据分析篇/工具介绍/SymPy/符号计算/.ipynb_checkpoints/微积分-checkpoint.ipynb | hsz1273327/TutorialForDataScience | 1d8e72c033a264297e80f43612cd44765365b09e | [
"MIT"
] | null | null | null | src/数据分析篇/工具介绍/SymPy/符号计算/.ipynb_checkpoints/微积分-checkpoint.ipynb | hsz1273327/TutorialForDataScience | 1d8e72c033a264297e80f43612cd44765365b09e | [
"MIT"
] | 3 | 2020-03-31T03:36:05.000Z | 2020-03-31T03:36:21.000Z | src/数据分析篇/工具介绍/SymPy/符号计算/.ipynb_checkpoints/微积分-checkpoint.ipynb | hsz1273327/TutorialForDataScience | 1d8e72c033a264297e80f43612cd44765365b09e | [
"MIT"
] | null | null | null | 61.080919 | 8,060 | 0.787102 | true | 1,597 | Qwen/Qwen-72B | 1. YES
2. YES | 0.9659 | 0.890294 | 0.859935 | __label__yue_Hant | 0.724225 | 0.83625 |
```python
from neural_odes import *
%load_ext autoreload
%autoreload 2
```
## Solve the Discrete Lotka-Volterra Eqtn for general non-symmetric $A$
\begin{equation}
\begin{aligned}
p_i(t+1) &= p_i(t)\big[1+r_i\big(1-\frac{\sum_{j=1}^dA_{ij}p_j(t)}{k_i}\big)\big], i = 1, \dots d\\
&= p_i(t)\big[1+r_i\big(1-\... | ad08f3182de7189596aea4079507d2793a41617c | 83,732 | ipynb | Jupyter Notebook | src/gluonts/nursery/auto_ode/auto-ode-lv-discrete-time-nonsymmetric.ipynb | Xiaoxiong-Liu/gluon-ts | 097c492769258dd70b7f223f826b17b0051ceee9 | [
"Apache-2.0"
] | 2,648 | 2019-06-03T17:18:27.000Z | 2022-03-31T08:29:22.000Z | src/gluonts/nursery/auto_ode/auto-ode-lv-discrete-time-nonsymmetric.ipynb | Xiaoxiong-Liu/gluon-ts | 097c492769258dd70b7f223f826b17b0051ceee9 | [
"Apache-2.0"
] | 1,220 | 2019-06-04T09:00:14.000Z | 2022-03-31T10:45:43.000Z | src/gluonts/nursery/auto_ode/auto-ode-lv-discrete-time-nonsymmetric.ipynb | Xiaoxiong-Liu/gluon-ts | 097c492769258dd70b7f223f826b17b0051ceee9 | [
"Apache-2.0"
] | 595 | 2019-06-04T01:04:31.000Z | 2022-03-30T10:40:26.000Z | 261.6625 | 75,572 | 0.922455 | true | 1,385 | Qwen/Qwen-72B | 1. YES
2. YES | 0.853913 | 0.843895 | 0.720613 | __label__eng_Latn | 0.791511 | 0.512557 |
# Characterization of Systems in the Spectral 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@u... | 0947b373e37dd9e17c56527db7c74cd33a443a3f | 132,209 | ipynb | Jupyter Notebook | systems_spectral_domain/phase_group_delay.ipynb | spatialaudio/signals-and-systems-lecture | 93e2f3488dc8f7ae111a34732bd4d13116763c5d | [
"MIT"
] | 243 | 2016-04-01T14:21:00.000Z | 2022-03-28T20:35:09.000Z | systems_spectral_domain/phase_group_delay.ipynb | iamzhd1977/signals-and-systems-lecture | b134608d336ceb94d83cdb66bc11c6d4d035f99c | [
"MIT"
] | 6 | 2016-04-11T06:28:17.000Z | 2021-11-10T10:59:35.000Z | systems_spectral_domain/phase_group_delay.ipynb | iamzhd1977/signals-and-systems-lecture | b134608d336ceb94d83cdb66bc11c6d4d035f99c | [
"MIT"
] | 63 | 2017-04-20T00:46:03.000Z | 2022-03-30T14:07:09.000Z | 64.871933 | 15,978 | 0.633368 | true | 1,015 | Qwen/Qwen-72B | 1. YES
2. YES | 0.782662 | 0.757794 | 0.593097 | __label__eng_Latn | 0.979769 | 0.216294 |
### Deep Neural Network for Bound-Virtual Classfication
In this notebook we compile all the functions that are used to generate the training dataset for the bound-virtual enhancement classification. The dataset is prepared such that the input is the single-channel s-wave cross-section with enhancement at the threshold... | fe851bf9ba3780f84478d3ece5ec4e6343745142 | 14,239 | ipynb | Jupyter Notebook | generate_dataset.ipynb | sombillo/DNN-for-bound-virtual-classification | cb7e42ac0c6111cd3f3796504bf09015938795fd | [
"MIT"
] | null | null | null | generate_dataset.ipynb | sombillo/DNN-for-bound-virtual-classification | cb7e42ac0c6111cd3f3796504bf09015938795fd | [
"MIT"
] | null | null | null | generate_dataset.ipynb | sombillo/DNN-for-bound-virtual-classification | cb7e42ac0c6111cd3f3796504bf09015938795fd | [
"MIT"
] | null | null | null | 38.692935 | 380 | 0.520542 | true | 2,707 | Qwen/Qwen-72B | 1. YES
2. YES | 0.907312 | 0.712232 | 0.646217 | __label__eng_Latn | 0.867641 | 0.339709 |
```python
import torch
import pandas as pd
import numpy as np
import torch.nn as nn
```
```python
a = np.eye(5, 3)[np.array([0, 1, 0, 2, 1])]
a.reshape(-1, 3)
```
array([[1., 0., 0.],
[0., 1., 0.],
[1., 0., 0.],
[0., 0., 1.],
[0., 1., 0.]])
```python
a = np.zer... | bbf3e757c51e00e0934fab77ac7fc8561f32835e | 60,411 | ipynb | Jupyter Notebook | trivial.ipynb | DataCanvasIO/YLearn | d65b5afb83deed154c710de9096317165d95014a | [
"Apache-2.0"
] | 3 | 2022-03-28T07:41:28.000Z | 2022-03-29T06:24:52.000Z | trivial.ipynb | DataCanvasIO/YLearn | d65b5afb83deed154c710de9096317165d95014a | [
"Apache-2.0"
] | null | null | null | trivial.ipynb | DataCanvasIO/YLearn | d65b5afb83deed154c710de9096317165d95014a | [
"Apache-2.0"
] | null | null | null | 21.575357 | 1,057 | 0.427091 | true | 8,906 | Qwen/Qwen-72B | 1. YES
2. YES | 0.894789 | 0.812867 | 0.727345 | __label__krc_Cyrl | 0.380402 | 0.528198 |
$$ \newcommand{\pd}[2]{ \frac{\partial #1}{\partial #2} }
\newcommand{\od}[2]{\frac{d #1}{d #2}}
\newcommand{\td}[2]{\frac{D #1}{D #2}}
\newcommand{\ab}[1]{\langle #1 \rangle}
\newcommand{\bss}[1]{\textsf{\textbf{#1}}}
\newcommand{\ol}{\overline}
\newcommand{\olx}[1]{\overline{#1}^x}
$$
# Homework 4: Equation Derivat... | da25fa4ed6cf3a8c916d7ef84de0183c5b7d4b92 | 3,285 | ipynb | Jupyter Notebook | homework-2018/po-hw-4.ipynb | dgumustel/intro_to_physical_oceanography | 45d21e1642038bfc93ebe645f16f55dba72901db | [
"MIT"
] | 82 | 2015-09-18T02:01:53.000Z | 2022-02-28T01:43:48.000Z | homework-2018/po-hw-4.ipynb | Sumanshekhar17/intro_to_physical_oceanography | e624bbbd6d67235b3fcf2764e256dd2ed024481e | [
"MIT"
] | 5 | 2015-09-19T01:35:28.000Z | 2022-02-28T17:23:53.000Z | homework-2018/po-hw-4.ipynb | Sumanshekhar17/intro_to_physical_oceanography | e624bbbd6d67235b3fcf2764e256dd2ed024481e | [
"MIT"
] | 51 | 2015-09-12T00:30:33.000Z | 2022-02-08T19:37:51.000Z | 27.838983 | 191 | 0.505023 | true | 647 | Qwen/Qwen-72B | 1. YES
2. YES | 0.907312 | 0.847968 | 0.769372 | __label__eng_Latn | 0.938557 | 0.62584 |
# Recurrent networks
- Once we learn the general equation and properties of the line / hyperplane we can turn around and perform various learning tasks - like regression and classification - using it as a model.
- By the same token now that we have a basic understanding of how to model general ordered data, we can... | cdd2259ec92ab12e040cf544ab8063d871ce3717 | 481,665 | ipynb | Jupyter Notebook | presentations/recurrent_networks/recurrent_networks.ipynb | jermwatt/blog | 3dd0d464d7a17c1c7a6508f714edc938dc3c03e9 | [
"MIT"
] | 14 | 2019-04-17T23:55:14.000Z | 2021-08-08T02:18:49.000Z | presentations/recurrent_networks/recurrent_networks.ipynb | jermwatt/blog | 3dd0d464d7a17c1c7a6508f714edc938dc3c03e9 | [
"MIT"
] | null | null | null | presentations/recurrent_networks/recurrent_networks.ipynb | jermwatt/blog | 3dd0d464d7a17c1c7a6508f714edc938dc3c03e9 | [
"MIT"
] | 3 | 2019-04-10T22:46:27.000Z | 2020-11-06T09:16:30.000Z | 132.252883 | 110,695 | 0.808236 | true | 2,166 | Qwen/Qwen-72B | 1. YES
2. YES | 0.637031 | 0.787931 | 0.501936 | __label__eng_Latn | 0.972702 | 0.004496 |
# ACSE-3 (Numerical Methods) <a class="tocSkip">
# Coursework 2 <a class="tocSkip">
## Coursework 2A - Advection-diffusion of a Gaussian
This question involves the solution of unsteady advection-diffusion in one spatial dimension using central finite difference schemes in space and explicit and implicit scheme... | 6246c47307ab77e1e559cd09052a176774f14c6b | 700,148 | ipynb | Jupyter Notebook | .ipynb_checkpoints/ACSE-3-Coursework-2-checkpoint.ipynb | mattiaguerri/NumericalMethods | c496ae74f92b7c2a0d01a4318e14b74c902cfc5c | [
"MIT"
] | 1 | 2021-03-04T12:07:32.000Z | 2021-03-04T12:07:32.000Z | ACSE-3-Coursework-2.ipynb | mattiaguerri/NumericalMethods | c496ae74f92b7c2a0d01a4318e14b74c902cfc5c | [
"MIT"
] | null | null | null | ACSE-3-Coursework-2.ipynb | mattiaguerri/NumericalMethods | c496ae74f92b7c2a0d01a4318e14b74c902cfc5c | [
"MIT"
] | null | null | null | 292.459482 | 97,380 | 0.913545 | true | 18,109 | Qwen/Qwen-72B | 1. YES
2. YES | 0.812867 | 0.914901 | 0.743693 | __label__eng_Latn | 0.913287 | 0.56618 |
```python
from sympy.physics.units import *
from sympy import *
# Rounding:
import decimal
from decimal import Decimal as DX
def iso_round(obj, pv, rounding=decimal.ROUND_HALF_EVEN):
import sympy
"""
Rounding acc. to DIN EN ISO 80000-1:2013-08
place value = Rundestellenwert
"""
assert pv in set... | 6ff3c1677ed8765e605d5ee60931fdfb4aec38a3 | 5,155 | ipynb | Jupyter Notebook | ipynb/WB-Klein/5/stallkamp_cc.ipynb | kassbohm/wb-snippets | f1ac5194e9f60a9260d096ba5ed1ce40b844a3fe | [
"MIT"
] | null | null | null | ipynb/WB-Klein/5/stallkamp_cc.ipynb | kassbohm/wb-snippets | f1ac5194e9f60a9260d096ba5ed1ce40b844a3fe | [
"MIT"
] | null | null | null | ipynb/WB-Klein/5/stallkamp_cc.ipynb | kassbohm/wb-snippets | f1ac5194e9f60a9260d096ba5ed1ce40b844a3fe | [
"MIT"
] | null | null | null | 32.421384 | 82 | 0.342968 | true | 1,119 | Qwen/Qwen-72B | 1. YES
2. YES | 0.859664 | 0.7773 | 0.668217 | __label__eng_Latn | 0.125927 | 0.390822 |
<!-- dom:TITLE: Learning from data: Bayesian Parameter Estimation -->
# Learning from data: Bayesian Parameter Estimation
<!-- dom:AUTHOR: Christian Forssén at Department of Physics, Chalmers University of Technology, Sweden -->
<!-- Author: -->
**Christian Forssén**, Department of Physics, Chalmers University of Tec... | 6359eae48da6580c12ade0a4116cc05aeabdd707 | 40,461 | ipynb | Jupyter Notebook | doc/pub/BayesianParameterEstimation/ipynb/BayesianParameterEstimation.ipynb | fraidowolf/tif285-project1 | 16724bf233ce20aba0de02655cb9072dd5c7098e | [
"CC0-1.0"
] | null | null | null | doc/pub/BayesianParameterEstimation/ipynb/BayesianParameterEstimation.ipynb | fraidowolf/tif285-project1 | 16724bf233ce20aba0de02655cb9072dd5c7098e | [
"CC0-1.0"
] | null | null | null | doc/pub/BayesianParameterEstimation/ipynb/BayesianParameterEstimation.ipynb | fraidowolf/tif285-project1 | 16724bf233ce20aba0de02655cb9072dd5c7098e | [
"CC0-1.0"
] | null | null | null | 42.861229 | 996 | 0.619906 | true | 7,657 | Qwen/Qwen-72B | 1. YES
2. YES | 0.771843 | 0.800692 | 0.618009 | __label__eng_Latn | 0.995409 | 0.274172 |
# Structured prediction
In this example$\newcommand{\reals}{\mathbf{R}}$$\newcommand{\ones}{\mathbf{1}}$, we fit a regression model to structured data, using an LLCP.
The training dataset $\mathcal D$ contains $N$ input-output pairs $(x, y)$,
where $x \in \reals^{n}_{++}$ is an input and $y \in \reals^{m}_{++}$ is a... | 4c8a5919cc7442ee17c947867014f40cfcb3a68a | 48,722 | ipynb | Jupyter Notebook | examples/notebooks/derivatives/structured_prediction.ipynb | jasondark/cvxpy | 56aaa01b0e9d98ae5a91a923708129a7b37a6f18 | [
"ECL-2.0",
"Apache-2.0"
] | 3,285 | 2015-01-03T04:02:29.000Z | 2021-04-19T14:51:29.000Z | examples/notebooks/derivatives/structured_prediction.ipynb | h-vetinari/cvxpy | 86307f271819bb78fcdf64a9c3a424773e8269fa | [
"ECL-2.0",
"Apache-2.0"
] | 1,138 | 2015-01-01T19:40:14.000Z | 2021-04-18T23:37:31.000Z | examples/notebooks/derivatives/structured_prediction.ipynb | h-vetinari/cvxpy | 86307f271819bb78fcdf64a9c3a424773e8269fa | [
"ECL-2.0",
"Apache-2.0"
] | 765 | 2015-01-02T19:29:39.000Z | 2021-04-20T00:50:43.000Z | 91.582707 | 15,184 | 0.821518 | true | 3,034 | Qwen/Qwen-72B | 1. YES
2. YES | 0.909907 | 0.822189 | 0.748116 | __label__eng_Latn | 0.737427 | 0.576455 |
```python
# https://colab.research.google.com/github/kassbohm/tm-snippets/blob/master/ipynb/TM_A/TM_2/lagrange.ipynb
from sympy.physics.units import *
from sympy import *
a0, a1, a2 = var("a0, a1, a2")
b0, b1, b2 = var("b0, b1, b2")
c0, c1, c2 = var("c0, c1, c2")
xi = var("xi")
L0 = a0 + a1*xi + a2*xi*xi
L1 = b0 + ... | 75c31fb01bc0cf9d15b3614cb0e9e4f4aa45078b | 7,599 | ipynb | Jupyter Notebook | ipynb/TM_A/TM_2/lagrange.ipynb | kassbohm/tm-snippets | 5e0621ba2470116e54643b740d1b68b9f28bff12 | [
"MIT"
] | null | null | null | ipynb/TM_A/TM_2/lagrange.ipynb | kassbohm/tm-snippets | 5e0621ba2470116e54643b740d1b68b9f28bff12 | [
"MIT"
] | null | null | null | ipynb/TM_A/TM_2/lagrange.ipynb | kassbohm/tm-snippets | 5e0621ba2470116e54643b740d1b68b9f28bff12 | [
"MIT"
] | null | null | null | 32.063291 | 119 | 0.389788 | true | 1,815 | Qwen/Qwen-72B | 1. YES
2. YES | 0.92523 | 0.7773 | 0.719181 | __label__yue_Hant | 0.078552 | 0.50923 |
# Chapter 4
`Original content created by Cam Davidson-Pilon`
`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) at Quantopian (@quantopian)`
______
## The greatest theorem never told
This chapter focuses on an idea that is always bouncing around our minds, but is rarely ma... | 7458cedeeb79754a223c1763770fd50e2b7e8474 | 475,551 | ipynb | Jupyter Notebook | Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC3.ipynb | gjcooper/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | 0082bb1183c114c5f99d88e743150a9612dc65de | [
"MIT"
] | null | null | null | Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC3.ipynb | gjcooper/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | 0082bb1183c114c5f99d88e743150a9612dc65de | [
"MIT"
] | null | null | null | Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC3.ipynb | gjcooper/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | 0082bb1183c114c5f99d88e743150a9612dc65de | [
"MIT"
] | null | null | null | 373.567164 | 87,532 | 0.917147 | true | 12,717 | Qwen/Qwen-72B | 1. YES
2. YES | 0.853913 | 0.914901 | 0.781246 | __label__eng_Latn | 0.994378 | 0.653428 |
```
# default_exp assignment_part_1_solution
```
# Assignment Part 1 Solution
--------------------
## Laplace Transforms
1. Calculate the Laplace Transform $X(s)$ of the signal $x(t)$:
$$
x(t) = (4t-3cos(5t))e^{-2t}
$$
$$
x(t)=4te^{-2t} - 3cos(5t)e^{-2t} \rightarrow X(s) = 4 \frac{1}{(s+2)^2} - 3 \frac{(s+2)}{(s+... | c9d41d01f9926622cf6b2159f258903e4a5748e8 | 148,858 | ipynb | Jupyter Notebook | A_assignment_part_1_solution.ipynb | andreamunafo/classical_control_theory | 5e1bef562e32fb9efcde83891cb19ce5825a6a7f | [
"Apache-2.0"
] | null | null | null | A_assignment_part_1_solution.ipynb | andreamunafo/classical_control_theory | 5e1bef562e32fb9efcde83891cb19ce5825a6a7f | [
"Apache-2.0"
] | null | null | null | A_assignment_part_1_solution.ipynb | andreamunafo/classical_control_theory | 5e1bef562e32fb9efcde83891cb19ce5825a6a7f | [
"Apache-2.0"
] | null | null | null | 122.014754 | 39,920 | 0.87308 | true | 4,831 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91611 | 0.805632 | 0.738047 | __label__eng_Latn | 0.834858 | 0.553063 |
```python
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data_utils
import operator
import numpy as np
```
## RNN intuition
Let us assume that we have an input $x = [x_1, x_2, ..., x_N]$ and we need to learn the mapping for some outpu... | 332d760a4a25b6e16996407d9f0203a223095ff8 | 59,525 | ipynb | Jupyter Notebook | Pytorch RNN sequence tagging.ipynb | Anou9531/Pytorch-Implementation | 6ce3d5123852a77ca565b4acb0efe12b68fd803c | [
"Apache-2.0"
] | 182 | 2017-01-25T13:08:18.000Z | 2022-03-02T13:27:27.000Z | Pytorch RNN sequence tagging.ipynb | Anou9531/Pytorch-Implementation | 6ce3d5123852a77ca565b4acb0efe12b68fd803c | [
"Apache-2.0"
] | 1 | 2018-07-11T07:45:46.000Z | 2018-07-11T07:45:46.000Z | Pytorch RNN sequence tagging.ipynb | Anou9531/Pytorch-Implementation | 6ce3d5123852a77ca565b4acb0efe12b68fd803c | [
"Apache-2.0"
] | 48 | 2017-01-26T14:50:42.000Z | 2022-03-12T02:40:04.000Z | 35.880048 | 1,206 | 0.5474 | true | 11,595 | Qwen/Qwen-72B | 1. YES
2. YES | 0.841826 | 0.815232 | 0.686284 | __label__eng_Latn | 0.819407 | 0.432798 |
# Linearized Dynamics Near Steady-State
We start from the linearized state-equations of the passively mode-locked laser [ref1] [ref1]
[ref1]: https://github.com/adrianschlatter/notebooks/blob/master/Dynamics%20of%20Passively%20Mode-Locked%20Lasers.ipynb
```
import sympy as sym
sym.init_printing(use_latex='mathjax')... | df0a3cf344d2c3d773803ceacf2c58c22783ab70 | 17,663 | ipynb | Jupyter Notebook | Passively Mode-Locked Laser - Steady-State Dynamics.ipynb | adrianschlatter/notebooks | 997ce44b68998959cf486d5ba77f2a9b5336c28f | [
"BSD-2-Clause"
] | null | null | null | Passively Mode-Locked Laser - Steady-State Dynamics.ipynb | adrianschlatter/notebooks | 997ce44b68998959cf486d5ba77f2a9b5336c28f | [
"BSD-2-Clause"
] | 2 | 2018-09-22T19:08:41.000Z | 2018-09-22T19:11:34.000Z | Passively Mode-Locked Laser - Steady-State Dynamics.ipynb | adrianschlatter/notebooks | 997ce44b68998959cf486d5ba77f2a9b5336c28f | [
"BSD-2-Clause"
] | null | null | null | 40.326484 | 456 | 0.463455 | true | 2,354 | Qwen/Qwen-72B | 1. YES
2. YES | 0.917303 | 0.757794 | 0.695127 | __label__eng_Latn | 0.245584 | 0.453344 |
$\newcommand{\xbf}{{\bf x}}
\newcommand{\ybf}{{\bf y}}
\newcommand{\wbf}{{\bf w}}
\newcommand{\Ibf}{\mathbf{I}}
\newcommand{\Xbf}{\mathbf{X}}
\newcommand{\Rbb}{\mathbb{R}}
\newcommand{\vec}[1]{\left[\begin{array}{c}#1\end{array}\right]}
$
# Introduction aux réseaux de neurones
Matériel de cours rédigé par Pascal Germ... | e93312ba3171a9bd74a977a29895d5d6264e1e69 | 904,472 | ipynb | Jupyter Notebook | notebooks/01 - Commencons simplement.ipynb | pgermain/cours2018-Intro_aux_r-seaux_de_neurones | bec6bb9e9ff40fbac0e29469fe038b3c10beebf9 | [
"CC-BY-4.0"
] | null | null | null | notebooks/01 - Commencons simplement.ipynb | pgermain/cours2018-Intro_aux_r-seaux_de_neurones | bec6bb9e9ff40fbac0e29469fe038b3c10beebf9 | [
"CC-BY-4.0"
] | null | null | null | notebooks/01 - Commencons simplement.ipynb | pgermain/cours2018-Intro_aux_r-seaux_de_neurones | bec6bb9e9ff40fbac0e29469fe038b3c10beebf9 | [
"CC-BY-4.0"
] | 2 | 2018-10-23T14:22:25.000Z | 2020-11-19T23:36:33.000Z | 711.062893 | 366,840 | 0.947666 | true | 4,647 | Qwen/Qwen-72B | 1. YES
2. YES | 0.845942 | 0.808067 | 0.683578 | __label__fra_Latn | 0.799172 | 0.426513 |
<a href="https://colab.research.google.com/github/AI-odyssey/AI-odyssey.github.io/blob/master/%5BHW3%5D_Practice_1_answer.ipynb" target="_parent"></a>
# **[HW3] Practice_1**
1. Install packages
2. Matrix operations
3. Linear system
4. Inverse matrix
5. Linear combination
선형대수 실습은, exercise 혹은 coding 문제풀이에 집중하기 보다는, 수... | 086478b5cf57fba005e5f520d89e86fee970f863 | 681,987 | ipynb | Jupyter Notebook | [HW3]_Practice_1_answer.ipynb | AI-odyssey/AI-odyssey.github.io | 31aa670933b7768751250321a77c79726174f805 | [
"MIT"
] | null | null | null | [HW3]_Practice_1_answer.ipynb | AI-odyssey/AI-odyssey.github.io | 31aa670933b7768751250321a77c79726174f805 | [
"MIT"
] | null | null | null | [HW3]_Practice_1_answer.ipynb | AI-odyssey/AI-odyssey.github.io | 31aa670933b7768751250321a77c79726174f805 | [
"MIT"
] | null | null | null | 450.751487 | 174,350 | 0.931887 | true | 7,724 | Qwen/Qwen-72B | 1. YES
2. YES | 0.808067 | 0.743168 | 0.60053 | __label__kor_Hang | 0.997295 | 0.233562 |
# Taylor integration example: $\dot x=x^2$
Here, we will integrate the initial-value problem (IVP) defined by
$$
\begin{align}
\dot x &= x^2 \\
x(0)&=x_0
\end{align}
$$
Given a real number $A>0$, the restriction of the function $f(x)=x^2$ over the interval $I_A=(-A,A)$ satisfies the Lipschitz condition $|f(x_1)-f(x_... | 2c3c3fde6b2146687aab2b79f1468fdf34e05065 | 290,785 | ipynb | Jupyter Notebook | examples/x-dot-equals-x-squared.ipynb | JuliaPackageMirrors/TaylorIntegration.jl | 0b095efa8dbe54913d01628ca24b83eae579908c | [
"MIT"
] | 1 | 2022-01-22T13:06:53.000Z | 2022-01-22T13:06:53.000Z | examples/x-dot-equals-x-squared.ipynb | JuliaPackageMirrors/TaylorIntegration.jl | 0b095efa8dbe54913d01628ca24b83eae579908c | [
"MIT"
] | null | null | null | examples/x-dot-equals-x-squared.ipynb | JuliaPackageMirrors/TaylorIntegration.jl | 0b095efa8dbe54913d01628ca24b83eae579908c | [
"MIT"
] | null | null | null | 284.247312 | 47,290 | 0.928432 | true | 3,026 | Qwen/Qwen-72B | 1. YES
2. YES | 0.909907 | 0.882428 | 0.802927 | __label__eng_Latn | 0.933805 | 0.703802 |
[Sebastian Raschka](http://sebastianraschka.com)
- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/python_howtos/scikit_linear_classificationreate=1)
- [Link to this IPython notebook on Github](http://nbviewer.ipython.org/github/rasbt/pattern_classification/b... | de428dd8e0faaaf997ea3d3dda85492a3f21e58d | 238,530 | ipynb | Jupyter Notebook | tests/others/scikit_linear_classification.ipynb | gopala-kr/ds-notebooks | bc35430ecdd851f2ceab8f2437eec4d77cb59423 | [
"MIT"
] | 1 | 2019-05-10T09:16:23.000Z | 2019-05-10T09:16:23.000Z | tests/others/scikit_linear_classification.ipynb | gopala-kr/ds-notebooks | bc35430ecdd851f2ceab8f2437eec4d77cb59423 | [
"MIT"
] | null | null | null | tests/others/scikit_linear_classification.ipynb | gopala-kr/ds-notebooks | bc35430ecdd851f2ceab8f2437eec4d77cb59423 | [
"MIT"
] | 1 | 2019-05-10T09:17:28.000Z | 2019-05-10T09:17:28.000Z | 217.240437 | 119,904 | 0.907337 | true | 6,834 | Qwen/Qwen-72B | 1. YES
2. YES | 0.766294 | 0.70253 | 0.538344 | __label__eng_Latn | 0.87465 | 0.089084 |
# Problema:
Solução da cadeia cinemática do robô antropomorphico mostrado abaixo.
O mecanismo possui 2 elos e 3 juntas.
## Sistema de Coordenadas
Primeiramente, temos que determinar a localização dos referenciais. O primeiro referencial, ```B0```, está fixo e será colocado na intercessão dos eixos das duas primeira... | 93f55717fb947fafee2599caf6f9891deaf222ad | 20,634 | ipynb | Jupyter Notebook | examples/antropomorphic_robot/Notebook_Kinematics_3DoF-Antropomorphic_pt.ipynb | abhikamath/pydy | 0d11df897c40178bb0ffd9caa9e25bccd1d8392a | [
"BSD-3-Clause"
] | 298 | 2015-01-31T11:43:22.000Z | 2022-03-15T02:18:21.000Z | examples/antropomorphic_robot/Notebook_Kinematics_3DoF-Antropomorphic_pt.ipynb | abhikamath/pydy | 0d11df897c40178bb0ffd9caa9e25bccd1d8392a | [
"BSD-3-Clause"
] | 359 | 2015-01-17T16:56:42.000Z | 2022-02-08T05:27:08.000Z | examples/antropomorphic_robot/Notebook_Kinematics_3DoF-Antropomorphic_pt.ipynb | pydy/pydy | 4a2c46faae44d06017b64335e48992ee8c53e1b6 | [
"BSD-3-Clause"
] | 109 | 2015-02-03T13:02:45.000Z | 2021-12-21T12:57:21.000Z | 69.241611 | 6,497 | 0.591887 | true | 5,616 | Qwen/Qwen-72B | 1. YES
2. YES | 0.894789 | 0.817574 | 0.731557 | __label__por_Latn | 0.668361 | 0.537984 |
# Solving ODEs with the Euler integrator
**Ordinary differential equations** ([ODE](http://mathworld.wolfram.com/OrdinaryDifferentialEquation.html)s) describe many phenomena in physics. They describe the changes of a **dependent variable** $y(t)$ as a function of a **single independent variable** (e.g. $t$ or $x$).
-... | ae052ff25ed7549a469f8eb6c88a29b1363b84f4 | 57,018 | ipynb | Jupyter Notebook | Module_5/euler_integrator.ipynb | Py4Phy/PHY202 | ec3a0b0285f2601accfdbf0c30416e1351430342 | [
"MIT"
] | 2 | 2019-10-26T00:39:14.000Z | 2019-10-29T19:35:20.000Z | Module_5/euler_integrator.ipynb | Py4Phy/PHY202 | ec3a0b0285f2601accfdbf0c30416e1351430342 | [
"MIT"
] | null | null | null | Module_5/euler_integrator.ipynb | Py4Phy/PHY202 | ec3a0b0285f2601accfdbf0c30416e1351430342 | [
"MIT"
] | null | null | null | 98.476684 | 30,352 | 0.857764 | true | 1,887 | Qwen/Qwen-72B | 1. YES
2. YES | 0.936285 | 0.888759 | 0.832132 | __label__eng_Latn | 0.958242 | 0.771653 |
disclaimer: To ensure that the notebook can be run from (more or less) any point, I try to load the relevant functions or modules whenever I use them in a cell. This is generally not good practice as it adds unneccesary overhead
# 0. Image representation as numerical arrays
### We start by importing numpy and creatin... | 6a0039d358d69595c412a501ec7b66bad72be0d1 | 65,838 | ipynb | Jupyter Notebook | 20201109/JupyterNotebooks/DIP_AOY_Student.ipynb | alonyan/DIP | 22fc811fc6debc224fca584c133dbc23e627014f | [
"MIT"
] | 5 | 2020-09-27T14:10:28.000Z | 2022-03-21T13:24:33.000Z | 20201109/JupyterNotebooks/DIP_AOY_Student.ipynb | alonyan/DIP | 22fc811fc6debc224fca584c133dbc23e627014f | [
"MIT"
] | null | null | null | 20201109/JupyterNotebooks/DIP_AOY_Student.ipynb | alonyan/DIP | 22fc811fc6debc224fca584c133dbc23e627014f | [
"MIT"
] | 6 | 2019-11-06T07:57:10.000Z | 2021-06-07T20:20:24.000Z | 32.609212 | 494 | 0.597679 | true | 10,989 | Qwen/Qwen-72B | 1. YES
2. YES | 0.877477 | 0.868827 | 0.762375 | __label__eng_Latn | 0.952047 | 0.609586 |
```python
import numpy as np
import sympy as sp
import matplotlib.pyplot as plt
%matplotlib inline
import pi_sequences as p3
import boundary_layer_func1 as p1
import sequence_limits as p2
import diffeq_midpoint as p4
import math
```
## Classwork 3
Michael Seaman, Chinmai Raman, Austin Ayers, Taylor Patti
Organized... | f6380d834c8862443059146f9e92ce9a596c8e7a | 89,750 | ipynb | Jupyter Notebook | cw-3.ipynb | chapman-phys227-2016s/cw-3-classwork-team | 0102dfa4c600804d4488e19ad380f9e8e9b6e954 | [
"MIT"
] | null | null | null | cw-3.ipynb | chapman-phys227-2016s/cw-3-classwork-team | 0102dfa4c600804d4488e19ad380f9e8e9b6e954 | [
"MIT"
] | null | null | null | cw-3.ipynb | chapman-phys227-2016s/cw-3-classwork-team | 0102dfa4c600804d4488e19ad380f9e8e9b6e954 | [
"MIT"
] | null | null | null | 159.982175 | 20,644 | 0.895287 | true | 1,860 | Qwen/Qwen-72B | 1. YES
2. YES | 0.847968 | 0.622459 | 0.527825 | __label__eng_Latn | 0.761441 | 0.064645 |
```python
from sympy import *
init_printing()
```
```python
def skew(l):
l1, l2, l3 = l
return Matrix([
[0, -l3, l2],
[l3, 0, -l1],
[-l2, l1, 0]
])
```
```python
# define state variables
x, y, z, eta0, eps1, eps2, eps3, u, v, w, p, q, r = symbols('x y z et0 eps1 eps2 eps3 u v w p... | db0ced147942087e6a276e63ad5afae51731e87c | 96,999 | ipynb | Jupyter Notebook | sam_dynamics/notebooks/dynamics.ipynb | cisprague/sam_common | f477c8bb4fee3d36fe7ff87847833db3b207cf95 | [
"BSD-3-Clause"
] | null | null | null | sam_dynamics/notebooks/dynamics.ipynb | cisprague/sam_common | f477c8bb4fee3d36fe7ff87847833db3b207cf95 | [
"BSD-3-Clause"
] | null | null | null | sam_dynamics/notebooks/dynamics.ipynb | cisprague/sam_common | f477c8bb4fee3d36fe7ff87847833db3b207cf95 | [
"BSD-3-Clause"
] | 1 | 2021-05-04T09:48:56.000Z | 2021-05-04T09:48:56.000Z | 177.979817 | 67,325 | 0.380478 | true | 38,702 | Qwen/Qwen-72B | 1. YES
2. YES | 0.913677 | 0.785309 | 0.717518 | __label__kor_Hang | 0.109897 | 0.505366 |
Enable Equation Numbering in $\LaTeX$
```javascript
%%javascript
MathJax.Hub.Config({
TeX: { equationNumbers: { autoNumber: "AMS" } }
});
```
<IPython.core.display.Javascript object>
## Math Fundamentals
First we define this definition
\begin{equation}
(\vec{w} \cdot \vec{u} + b) \geq 0 \implie... | e9d1165a43c48e802bb223d3140fd06d0dededc4 | 69,273 | ipynb | Jupyter Notebook | notebooks/Support Vector Machine.ipynb | ELC/ML-Tutorial | 05d4d4e424976b245fa6bf05b60dfc90109e3782 | [
"MIT"
] | null | null | null | notebooks/Support Vector Machine.ipynb | ELC/ML-Tutorial | 05d4d4e424976b245fa6bf05b60dfc90109e3782 | [
"MIT"
] | null | null | null | notebooks/Support Vector Machine.ipynb | ELC/ML-Tutorial | 05d4d4e424976b245fa6bf05b60dfc90109e3782 | [
"MIT"
] | null | null | null | 76.040615 | 19,544 | 0.784793 | true | 3,453 | Qwen/Qwen-72B | 1. YES
2. YES | 0.899121 | 0.79053 | 0.710783 | __label__eng_Latn | 0.395508 | 0.489718 |
```python
from sympy import *
init_printing(use_latex='mathjax')
Re,r,G,rho,eta,v_x,tau_xx,L_x,lam,tau,k,x = symbols('Re r G rho eta v_x tau_xx L_x lambda tau k x', positive=True)
v0,p0,tau_xx0 = symbols('v0 p0 tau_xx0')
```
```python
K = r*G # bulk modulus from modulus... | 0b1bf158e3bd3456f8b44f96bbe93785f93f262b | 588,692 | ipynb | Jupyter Notebook | dispersion_analysis/dispersion_analysis_stokes1D.ipynb | PTsolvers/PseudoTransientStokes.jl | 894f32b5110bcb0c878782465fd6b2c9b0cbbcff | [
"MIT"
] | 1 | 2021-12-06T19:24:50.000Z | 2021-12-06T19:24:50.000Z | dispersion_analysis/dispersion_analysis_stokes1D.ipynb | PTsolvers/PseudoTransientStokes.jl | 894f32b5110bcb0c878782465fd6b2c9b0cbbcff | [
"MIT"
] | null | null | null | dispersion_analysis/dispersion_analysis_stokes1D.ipynb | PTsolvers/PseudoTransientStokes.jl | 894f32b5110bcb0c878782465fd6b2c9b0cbbcff | [
"MIT"
] | null | null | null | 517.304042 | 539,051 | 0.927429 | true | 935 | Qwen/Qwen-72B | 1. YES
2. YES | 0.899121 | 0.715424 | 0.643253 | __label__eng_Latn | 0.131718 | 0.332823 |
# Taylor integration of the Kepler problem
Here, we try to reproduce __exactly__ the [Kepler problem integration example](http://nbviewer.jupyter.org/github/JuliaDiff/TaylorSeries.jl/blob/master/examples/1-KeplerProblem.ipynb) made by Luis Benet in [JuliaDiff/TaylorSeries.jl](https://github.com/JuliaDiff/TaylorSeries.... | dff4432ce359d1fd69fc00eecf9675eecd7c0378 | 295,947 | ipynb | Jupyter Notebook | examples/Kepler-problem.ipynb | SebastianM-C/TaylorIntegration.jl | f3575ee1caba43e21312062d960613ec2ccba325 | [
"MIT"
] | 72 | 2016-09-22T22:32:12.000Z | 2022-03-23T13:35:18.000Z | examples/Kepler-problem.ipynb | SebastianM-C/TaylorIntegration.jl | f3575ee1caba43e21312062d960613ec2ccba325 | [
"MIT"
] | 132 | 2016-09-21T05:43:08.000Z | 2022-03-15T02:55:17.000Z | examples/Kepler-problem.ipynb | SebastianM-C/TaylorIntegration.jl | f3575ee1caba43e21312062d960613ec2ccba325 | [
"MIT"
] | 20 | 2016-09-24T04:37:11.000Z | 2022-03-25T13:48:07.000Z | 382.85511 | 68,967 | 0.936323 | true | 3,290 | Qwen/Qwen-72B | 1. YES
2. YES | 0.92079 | 0.812867 | 0.74848 | __label__eng_Latn | 0.883585 | 0.577301 |
```python
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import beta, gamma, norm, binom, uniform, t
import numdifftools as nd
from sklearn.linear_model import LinearRegression as linreg
import sympy as sym
from scipy.optimize import brentq, minimize
from datetime import datetime as dt
```
* Pleas... | 9efb6f63abd57223cd781977ecfb1371ef6eb208 | 308,919 | ipynb | Jupyter Notebook | STAT6011 Computational Statistics/Tut 2/Tut 2 (with answers).ipynb | IanFla/Teaching-Experience | ed4c414239d223f66bea4a01ffc5e5776743ec64 | [
"MIT"
] | null | null | null | STAT6011 Computational Statistics/Tut 2/Tut 2 (with answers).ipynb | IanFla/Teaching-Experience | ed4c414239d223f66bea4a01ffc5e5776743ec64 | [
"MIT"
] | null | null | null | STAT6011 Computational Statistics/Tut 2/Tut 2 (with answers).ipynb | IanFla/Teaching-Experience | ed4c414239d223f66bea4a01ffc5e5776743ec64 | [
"MIT"
] | null | null | null | 406.472368 | 88,460 | 0.927347 | true | 6,193 | Qwen/Qwen-72B | 1. YES
2. YES | 0.859664 | 0.839734 | 0.721889 | __label__eng_Latn | 0.943621 | 0.515521 |
# Stiff ODEs and implicit methods
In this notebook we look at examples where Runge-Kutta methods require very small steps to be accurate. For these **stiff** ODEs implicit methods are better.
Let's define our explicit methods first.
```python
import numpy as np
import matplotlib.pyplot as plt
```
```python
# The b... | 14da561bdcad80286c9bb81a11c9c60c8c2c061f | 108,518 | ipynb | Jupyter Notebook | OrdinaryDifferentialEquations/StiffODEsAndImplicitMethods.ipynb | CianCoyle/ACM20030-Examples | fb81abf24d066717900657c1de4f2c6f87806413 | [
"MIT"
] | 13 | 2020-02-15T21:30:37.000Z | 2021-09-21T12:03:13.000Z | OrdinaryDifferentialEquations/StiffODEsAndImplicitMethods.ipynb | CianCoyle/ACM20030-Examples | fb81abf24d066717900657c1de4f2c6f87806413 | [
"MIT"
] | null | null | null | OrdinaryDifferentialEquations/StiffODEsAndImplicitMethods.ipynb | CianCoyle/ACM20030-Examples | fb81abf24d066717900657c1de4f2c6f87806413 | [
"MIT"
] | 24 | 2020-02-13T14:27:47.000Z | 2022-02-05T14:17:10.000Z | 332.877301 | 54,828 | 0.92263 | true | 1,712 | Qwen/Qwen-72B | 1. YES
2. YES | 0.841826 | 0.894789 | 0.753257 | __label__eng_Latn | 0.935681 | 0.5884 |
# Notes on saturation Vapor Pressure #
There are a large number of expressions for the saturation vapor pressure in the literature, and many of these, even recent ones, seem to reference previous studies in a haphazard way. So how much do these differ, is there a standard, and by what criteria should one judge them b... | 663657903f55d3ffe6b4d326464f2a2b4be9698b | 408,573 | ipynb | Jupyter Notebook | saturation-water-vapor.ipynb | bjorn-stevens/Thermodynamics | f77a97f0908938b3f1f7bbb0e523b13582f436a1 | [
"MIT"
] | 1 | 2020-06-30T11:29:42.000Z | 2020-06-30T11:29:42.000Z | saturation-water-vapor.ipynb | bjorn-stevens/Thermodynamics | f77a97f0908938b3f1f7bbb0e523b13582f436a1 | [
"MIT"
] | null | null | null | saturation-water-vapor.ipynb | bjorn-stevens/Thermodynamics | f77a97f0908938b3f1f7bbb0e523b13582f436a1 | [
"MIT"
] | null | null | null | 459.070787 | 94,088 | 0.927403 | true | 9,277 | Qwen/Qwen-72B | 1. YES
2. YES | 0.793106 | 0.675765 | 0.535953 | __label__eng_Latn | 0.778045 | 0.083528 |
```python
# General import
import numpy as np
import scipy.sparse as sparse
from scipy.integrate import ode
import time
import matplotlib.pyplot as plt
```
```python
# pyMPC and kalman import
from pyMPC.mpc import MPCController
from pyMPC.kalman import kalman_design_simple, LinearStateEstimator
```
## System dynamic... | aedf2a233ebf9530189d0c12a9dfe3b0fa522d7b | 75,947 | ipynb | Jupyter Notebook | examples/example_inverted_pendulum_kalman.ipynb | forgi86/pyMPC | 291db149554767a035fcb01df3fed7a6b3fe60e4 | [
"MIT"
] | 84 | 2019-05-28T09:27:37.000Z | 2022-03-31T08:38:23.000Z | examples/example_inverted_pendulum_kalman.ipynb | passion4energy/pyMPC | 4b004ba707dab49cd36d96a3575b8593c870a904 | [
"MIT"
] | 2 | 2020-04-17T00:03:27.000Z | 2021-01-30T11:35:58.000Z | examples/example_inverted_pendulum_kalman.ipynb | passion4energy/pyMPC | 4b004ba707dab49cd36d96a3575b8593c870a904 | [
"MIT"
] | 20 | 2019-10-13T13:50:16.000Z | 2022-03-31T08:38:25.000Z | 172.606818 | 55,396 | 0.883669 | true | 2,925 | Qwen/Qwen-72B | 1. YES
2. YES | 0.7773 | 0.743168 | 0.577664 | __label__eng_Latn | 0.386615 | 0.180438 |
# Quaternion Triple Products and Distance
by Doug Sweetser, sweetser@alum.mit.edu - please feel free to email
In this IPython notebook, efforts will be made to understand quaternion triple products and how they are related to distances in space and intervals in space-time as seen in special relativity. Rather than fo... | b61f65ba9c33948acf084b31a0b345fe4577c2a8 | 103,888 | ipynb | Jupyter Notebook | Notebooks/triple_products_and_distance.ipynb | dougsweetser/AIG | ce23119bbde41671438fb805dfba4b04b42d84d6 | [
"Apache-2.0"
] | null | null | null | Notebooks/triple_products_and_distance.ipynb | dougsweetser/AIG | ce23119bbde41671438fb805dfba4b04b42d84d6 | [
"Apache-2.0"
] | null | null | null | Notebooks/triple_products_and_distance.ipynb | dougsweetser/AIG | ce23119bbde41671438fb805dfba4b04b42d84d6 | [
"Apache-2.0"
] | null | null | null | 57.683509 | 3,368 | 0.733136 | true | 5,872 | Qwen/Qwen-72B | 1. YES
2. YES | 0.941654 | 0.880797 | 0.829406 | __label__eng_Latn | 0.994684 | 0.765322 |
### Algebraic definition
A set G equipped with operation $\bullet$
> Closure: $\forall a,b \in G, a \bullet b \in G$
>Associativity: $(a \bullet b) \bullet c = a \bullet (b \bullet c)$
>Identity element: $\exists e \in G, such \ that \ \forall a \in G \ a \bullet e = a$
>Inverse element: $\forall a \in G, \exis... | 14163f558b14909503e6c3d2a3149d33b04abbdb | 20,153 | ipynb | Jupyter Notebook | Math/IntroToGroups1.ipynb | gate42qc/seminars | 35ff77b902d9c2ede619fd6e2d9c3e80d20d78de | [
"MIT"
] | 6 | 2018-12-07T10:02:06.000Z | 2019-11-24T19:30:03.000Z | Math/IntroToGroups1.ipynb | gate42qc/seminars | 35ff77b902d9c2ede619fd6e2d9c3e80d20d78de | [
"MIT"
] | null | null | null | Math/IntroToGroups1.ipynb | gate42qc/seminars | 35ff77b902d9c2ede619fd6e2d9c3e80d20d78de | [
"MIT"
] | 1 | 2019-08-22T12:07:40.000Z | 2019-08-22T12:07:40.000Z | 32.039746 | 368 | 0.487421 | true | 4,107 | Qwen/Qwen-72B | 1. YES
2. YES | 0.959762 | 0.928409 | 0.891052 | __label__eng_Latn | 0.931009 | 0.908545 |
# Relações entre fasores para elementos de circuitos
Jupyter Notebook desenvolvido por [Gustavo S.S.](https://github.com/GSimas)
Se a corrente através de um resistor R for i = Im
cos(wt + ϕ), a tensão nele será dada pela lei de Ohm, como segue:
\begin{align}
{\Large v(t) = iR = R I_m cos(\omega t + \phi)}
\\{\Large ... | 277817290d3a19c6a196d1a0b1228ffe6589baf8 | 9,651 | ipynb | Jupyter Notebook | Aula 19 - Fasores e Elementos de Circuitos.ipynb | ofgod2/Circuitos-electricos-Boylestad-12ed-Portugues | 60e815f6904858f3cda8b5c7ead8ea77aa09c7fd | [
"MIT"
] | 7 | 2019-08-13T13:33:15.000Z | 2021-11-16T16:46:06.000Z | Aula 19 - Fasores e Elementos de Circuitos.ipynb | ofgod2/Circuitos-electricos-Boylestad-12ed-Portugues | 60e815f6904858f3cda8b5c7ead8ea77aa09c7fd | [
"MIT"
] | 1 | 2017-08-24T17:36:15.000Z | 2017-08-24T17:36:15.000Z | Aula 19 - Fasores e Elementos de Circuitos.ipynb | ofgod2/Circuitos-electricos-Boylestad-12ed-Portugues | 60e815f6904858f3cda8b5c7ead8ea77aa09c7fd | [
"MIT"
] | 8 | 2019-03-29T14:31:49.000Z | 2021-12-30T17:59:23.000Z | 27.574286 | 274 | 0.488758 | true | 2,136 | Qwen/Qwen-72B | 1. YES
2. YES | 0.805632 | 0.718594 | 0.578923 | __label__por_Latn | 0.966104 | 0.183362 |
# CONTROLLABILITY OF BIOLOGICAL SYSTEMS
This notebook explores the construction and interpretation of transfer functions for more complex networks.
# Preliminaries
```python
!pip -q install controlSBML
import controlSBML as ctl
import control
from controlSBML.util import makeSimulationTimes
import pandas as pd
imp... | 63a70a1547035e93cc4e11ea8339c8fcaf221227 | 40,226 | ipynb | Jupyter Notebook | Lecture_12-Controllability-Of-Biological-Systems/Controllability-Of-Biological-Systems.ipynb | joseph-hellerstein/advanced-controls-lectures | dc43f6c3517616da3b0ea7c93192d911414ee202 | [
"MIT"
] | null | null | null | Lecture_12-Controllability-Of-Biological-Systems/Controllability-Of-Biological-Systems.ipynb | joseph-hellerstein/advanced-controls-lectures | dc43f6c3517616da3b0ea7c93192d911414ee202 | [
"MIT"
] | null | null | null | Lecture_12-Controllability-Of-Biological-Systems/Controllability-Of-Biological-Systems.ipynb | joseph-hellerstein/advanced-controls-lectures | dc43f6c3517616da3b0ea7c93192d911414ee202 | [
"MIT"
] | null | null | null | 44.745273 | 9,036 | 0.715234 | true | 3,085 | Qwen/Qwen-72B | 1. YES
2. YES | 0.893309 | 0.847968 | 0.757498 | __label__eng_Latn | 0.871094 | 0.598253 |
```python
import sympy
x, y, z = sympy.symbols("x y z")
c11, c22, c33, c44, c55, c66 = sympy.symbols("c11 c22 c33 c44 c55 c66")
c12, c13, c16, c22, c23, c26, c36, c45 = sympy.symbols("c12 c13 c16 c22 c23 c26 c36 c45")
s1, s2, s3, p = sympy.symbols("s1 s2 s3 p")
y11, y12, y13, y22, y23, y33, y31, y32, y21 = sympy.symbo... | 6ad838e91351d49872d24670739bfa3f559967f2 | 12,989 | ipynb | Jupyter Notebook | notebooks/Test Sympy.ipynb | kwinkunks/rppy | 91251d51797af79aaec0db16912c069f0fb1f13d | [
"BSD-2-Clause"
] | 24 | 2015-10-08T17:51:54.000Z | 2021-11-04T00:02:02.000Z | notebooks/Test Sympy.ipynb | shear/RPpy | 5f08ca5212686670c3e15565c34a9fd913d15e87 | [
"BSD-2-Clause"
] | 36 | 2015-03-20T23:48:09.000Z | 2015-07-24T04:58:03.000Z | notebooks/Test Sympy.ipynb | shear/RPpy | 5f08ca5212686670c3e15565c34a9fd913d15e87 | [
"BSD-2-Clause"
] | 15 | 2015-10-08T17:51:45.000Z | 2022-01-20T08:02:07.000Z | 81.18125 | 4,999 | 0.513357 | true | 6,487 | Qwen/Qwen-72B | 1. YES
2. YES | 0.953966 | 0.626124 | 0.597301 | __label__yue_Hant | 0.177845 | 0.226061 |
```python
import numpy as np
from skspatial.objects import plane
from sympy import Plane
def perpendicular(a):
b = np.empty_like(a)
b[0] = -a[1]
b[1] = a[0]
return b
def normalize(a):
a = np.array(a)
return a/np.linalg.norm(a)
def get2DProjection(origin, target_point):
points = [origin,... | f229c8bdc4ddbc1736dec3d9774de79ae7b91f50 | 2,673 | ipynb | Jupyter Notebook | Mathematics/Linear Algebra/Python Visualization Notebooks/scikit-spatial/Projecting-3D-points-to-2D-plane.ipynb | okara83/Becoming-a-Data-Scientist | f09a15f7f239b96b77a2f080c403b2f3e95c9650 | [
"MIT"
] | null | null | null | Mathematics/Linear Algebra/Python Visualization Notebooks/scikit-spatial/Projecting-3D-points-to-2D-plane.ipynb | okara83/Becoming-a-Data-Scientist | f09a15f7f239b96b77a2f080c403b2f3e95c9650 | [
"MIT"
] | null | null | null | Mathematics/Linear Algebra/Python Visualization Notebooks/scikit-spatial/Projecting-3D-points-to-2D-plane.ipynb | okara83/Becoming-a-Data-Scientist | f09a15f7f239b96b77a2f080c403b2f3e95c9650 | [
"MIT"
] | 2 | 2022-02-09T15:41:33.000Z | 2022-02-11T07:47:40.000Z | 25.457143 | 150 | 0.505425 | true | 432 | Qwen/Qwen-72B | 1. YES
2. YES | 0.914901 | 0.800692 | 0.732554 | __label__eng_Latn | 0.315742 | 0.5403 |
```python
%matplotlib widget
```
```python
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
import matplotlib.cm as cm
from IPython.display import display, Math, clear_output
import sympy
from sympy import *
from sympy.physics.vector import ReferenceFrame, CoordinateSym
fr... | a46ff9ad30bfc99873e3ca66e67e3fc54f11ae52 | 33,366 | ipynb | Jupyter Notebook | coaxial_cable_challenge.ipynb | lucask07/teaching-notebooks | 732638b3bac528f85e0dc649c4671c005f58b22b | [
"MIT"
] | null | null | null | coaxial_cable_challenge.ipynb | lucask07/teaching-notebooks | 732638b3bac528f85e0dc649c4671c005f58b22b | [
"MIT"
] | null | null | null | coaxial_cable_challenge.ipynb | lucask07/teaching-notebooks | 732638b3bac528f85e0dc649c4671c005f58b22b | [
"MIT"
] | null | null | null | 105.92381 | 23,476 | 0.838848 | true | 1,952 | Qwen/Qwen-72B | 1. YES
2. YES | 0.872347 | 0.896251 | 0.781843 | __label__eng_Latn | 0.822766 | 0.654815 |
# Fourier methods
> Fourier methods using Python
- toc: true
- badges: true
- comments: true
- categories: [jupyter]
The Fourier transform (FT) for a well-behaved functions $f$ is defined as:
$$f(k) = \int e^{-ikx} f(x) ~dx$$
The inverse FT is then
$$f(x) = \frac{1}{2\pi} \int e^{ikx} f(k) ~dk$$
## Discrete F... | c2f5c79f6c957b71e96f8fdb0fa29ac85e16cc72 | 240,699 | ipynb | Jupyter Notebook | notebooks/2014/FourierSeries.ipynb | rajeshrinet/compPhy | cc0ce84ac07efc4b9372c01eba99ebccbc08bb41 | [
"MIT"
] | 47 | 2015-06-05T14:37:39.000Z | 2022-01-06T06:35:30.000Z | notebooks/2014/FourierSeries.ipynb | rajeshrinet/compPhy | cc0ce84ac07efc4b9372c01eba99ebccbc08bb41 | [
"MIT"
] | 3 | 2017-10-23T06:44:38.000Z | 2021-09-23T05:16:31.000Z | notebooks/2014/FourierSeries.ipynb | rajeshrinet/compPhy | cc0ce84ac07efc4b9372c01eba99ebccbc08bb41 | [
"MIT"
] | 46 | 2015-12-09T00:21:53.000Z | 2022-02-03T20:44:38.000Z | 676.120787 | 117,900 | 0.944271 | true | 2,615 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.805632 | 0.714761 | __label__eng_Latn | 0.964473 | 0.49896 |
# Lab 3 Exercises for COMP 432 Machine Learning
In this lab you'll cluster and fit mixture models to data using the popular _scikit-learn_ package. Lab3 requires a good understanding of Numpy and Matplotlib. Please complete Lab1 before attempting Lab3.
**Run the code cell below** to import the required packages.
``... | 92bbabaa8c8a705ef1d112993423e420ece0fcec | 823,246 | ipynb | Jupyter Notebook | Lab 3/lab3-exercises.ipynb | m-triassi/ml-exercises | 92089577c99ed348d9034de7739d089f6e26d257 | [
"MIT"
] | null | null | null | Lab 3/lab3-exercises.ipynb | m-triassi/ml-exercises | 92089577c99ed348d9034de7739d089f6e26d257 | [
"MIT"
] | null | null | null | Lab 3/lab3-exercises.ipynb | m-triassi/ml-exercises | 92089577c99ed348d9034de7739d089f6e26d257 | [
"MIT"
] | null | null | null | 717.11324 | 112,372 | 0.947303 | true | 7,336 | Qwen/Qwen-72B | 1. YES
2. YES | 0.800692 | 0.699254 | 0.559887 | __label__eng_Latn | 0.953278 | 0.139136 |
```python
import numpy as np
import matplotlib.pyplot as plt
from sympy.solvers import solve
from sympy import Symbol
```
```python
x = Symbol('x')
sols = solve(8.99 * x - 6.56 * x - 1312.13, x)
sols
```
[539.971193415638]
```python
xs = np.linspace(0, 1000, 1000)
plt.plot(xs, 2.43 * xs - 1312.13)
plt.a... | ce7e2d635fec44cc3c4f6529f627a456f6bf71a5 | 28,575 | ipynb | Jupyter Notebook | Exercise03/Introduction_to_Break_Even_Analysis.ipynb | Develop-Packt/Using-Functions-and-Algebra-with-Python | 5f5b4c37e40216cb5751687f5bb9d6378652ab14 | [
"MIT"
] | null | null | null | Exercise03/Introduction_to_Break_Even_Analysis.ipynb | Develop-Packt/Using-Functions-and-Algebra-with-Python | 5f5b4c37e40216cb5751687f5bb9d6378652ab14 | [
"MIT"
] | null | null | null | Exercise03/Introduction_to_Break_Even_Analysis.ipynb | Develop-Packt/Using-Functions-and-Algebra-with-Python | 5f5b4c37e40216cb5751687f5bb9d6378652ab14 | [
"MIT"
] | 1 | 2021-02-25T16:24:53.000Z | 2021-02-25T16:24:53.000Z | 201.232394 | 14,012 | 0.924759 | true | 222 | Qwen/Qwen-72B | 1. YES
2. YES | 0.944995 | 0.828939 | 0.783343 | __label__eng_Latn | 0.292514 | 0.6583 |
# Introduction
The next step is to provide some information about the mass and inertia of the bodies involved. Each of the three rigid bodies have both a mass which resists linear accelerations and inertia which resists rotational accelerations. In this notebook we will specify the mass of the three bodies, the inerti... | e14dcf4b37f2aa7a72f9f461d70c54b627b481e6 | 9,368 | ipynb | Jupyter Notebook | notebooks/n04_inertia.ipynb | pydy/pydy-tutorial-human-standing | 72b1d8513e339e9b10e501bd3490caa3fa997bc4 | [
"CC-BY-4.0"
] | 134 | 2015-05-19T15:24:18.000Z | 2022-03-12T09:39:03.000Z | notebooks/n04_inertia.ipynb | pydy/pydy-tutorial-human-standing | 72b1d8513e339e9b10e501bd3490caa3fa997bc4 | [
"CC-BY-4.0"
] | 46 | 2015-05-05T18:08:20.000Z | 2022-01-28T11:12:42.000Z | notebooks/n04_inertia.ipynb | pydy/pydy-tutorial-pycon-2014 | 72b1d8513e339e9b10e501bd3490caa3fa997bc4 | [
"CC-BY-4.0"
] | 62 | 2015-06-16T01:50:51.000Z | 2022-02-26T07:39:41.000Z | 22.357995 | 432 | 0.58497 | true | 1,010 | Qwen/Qwen-72B | 1. YES
2. YES | 0.870597 | 0.839734 | 0.73107 | __label__eng_Latn | 0.993937 | 0.536853 |
# Linear Discriminant Analysis (LDA) tutorial
Notes from: [link 1](https://machinelearningmastery.com/linear-discriminant-analysis-for-machine-learning/), [link 2](https://www.python-course.eu/linear_discriminant_analysis.php)
- Logistic regression classification is hardly applied to two-class problems.
- LDA classi... | 9fdec66d323aa7552569a20e1257add5432c51f4 | 386,558 | ipynb | Jupyter Notebook | LDA.ipynb | bbrighttaer/data_science_nbs | 21c1b088e758b0cf801bc9c8da87dfd916561163 | [
"MIT"
] | null | null | null | LDA.ipynb | bbrighttaer/data_science_nbs | 21c1b088e758b0cf801bc9c8da87dfd916561163 | [
"MIT"
] | null | null | null | LDA.ipynb | bbrighttaer/data_science_nbs | 21c1b088e758b0cf801bc9c8da87dfd916561163 | [
"MIT"
] | null | null | null | 512.676393 | 341,664 | 0.935096 | true | 5,544 | Qwen/Qwen-72B | 1. YES
2. YES | 0.861538 | 0.805632 | 0.694083 | __label__eng_Latn | 0.549115 | 0.450919 |
# POL280 Bayesian Modelling Memo & Codes
## Lecture 1: Introduction (04/13/2017)
### Monte Carlo Simulation
```R
## Monte Carlo Simulation ##
#install.packages("plotrix")
library(plotrix)
library(grid)
## Plot Rectangle and Circle
plot(c(-1, 1), c(-1, 1), type = "n", asp = 1)
rect(-1, -1, 1, 1)
draw.circle(0, 0... | 3c53db73705d73e05d5164eca3e9831d001b8bf4 | 100,814 | ipynb | Jupyter Notebook | notebooks/.ipynb_checkpoints/POL280_Bayes_Memos_Codes-checkpoint.ipynb | gentok/Method_Notes | a7b60e50132fdda764efcfb1e163d1b31b2f99f7 | [
"MIT"
] | null | null | null | notebooks/.ipynb_checkpoints/POL280_Bayes_Memos_Codes-checkpoint.ipynb | gentok/Method_Notes | a7b60e50132fdda764efcfb1e163d1b31b2f99f7 | [
"MIT"
] | null | null | null | notebooks/.ipynb_checkpoints/POL280_Bayes_Memos_Codes-checkpoint.ipynb | gentok/Method_Notes | a7b60e50132fdda764efcfb1e163d1b31b2f99f7 | [
"MIT"
] | null | null | null | 286.403409 | 90,202 | 0.912324 | true | 1,956 | Qwen/Qwen-72B | 1. YES
2. YES | 0.879147 | 0.79053 | 0.694992 | __label__eng_Latn | 0.807779 | 0.453031 |
# Intro to Deep Learning
We're going to continue working with CAP imagery for the second half of this week. Recall the two main guiding questions for this week:
- _What_ is in an image (e.g. debris, buildings, etc.)?
- _Where_ are these things located _in 3D space_ ?
## Motivation
We've already seen how structure fro... | 9ab61acac97c97701ee41271a37d611215571081 | 26,422 | ipynb | Jupyter Notebook | 10-Intro_to_deep_learning.ipynb | bwsi-hadr/10-Intro_to_deep_learning | 571042b59823a57aa08d7cfa808acf53384f86c8 | [
"MIT"
] | null | null | null | 10-Intro_to_deep_learning.ipynb | bwsi-hadr/10-Intro_to_deep_learning | 571042b59823a57aa08d7cfa808acf53384f86c8 | [
"MIT"
] | null | null | null | 10-Intro_to_deep_learning.ipynb | bwsi-hadr/10-Intro_to_deep_learning | 571042b59823a57aa08d7cfa808acf53384f86c8 | [
"MIT"
] | 1 | 2021-06-23T14:13:13.000Z | 2021-06-23T14:13:13.000Z | 55.508403 | 730 | 0.665998 | true | 4,864 | Qwen/Qwen-72B | 1. YES
2. YES | 0.70253 | 0.7773 | 0.546077 | __label__eng_Latn | 0.999087 | 0.107048 |
# Music Machine Learning - Bayesian inference
### Author: Philippe Esling (esling@ircam.fr)
In this course we will cover
1. An introduction to [Bayesian inference](#bayesian)
2. A formal introduction to [Variational Auto-Encoders](#vae) (VAEs)
3. An explanation of the [implementation](#implem) of VAEs
4. Some [modifi... | 51f8687327653df6a7dc5485797d276f1a766524 | 41,233 | ipynb | Jupyter Notebook | 05a_bayesian_inference.ipynb | piptouque/atiam_ml | 9da637eae179237d30a15dd9ce3e95a2a956c385 | [
"MIT"
] | null | null | null | 05a_bayesian_inference.ipynb | piptouque/atiam_ml | 9da637eae179237d30a15dd9ce3e95a2a956c385 | [
"MIT"
] | null | null | null | 05a_bayesian_inference.ipynb | piptouque/atiam_ml | 9da637eae179237d30a15dd9ce3e95a2a956c385 | [
"MIT"
] | null | null | null | 43.725345 | 1,004 | 0.571654 | true | 9,444 | Qwen/Qwen-72B | 1. YES
2. YES | 0.874077 | 0.885631 | 0.77411 | __label__eng_Latn | 0.970605 | 0.63685 |
# On the fundamental differences between quantum states - an overview.
### University of Basel. Department of Physics
#### Quantum Information. Frühjahrssemester 2020
**Professor**: James Wootton.
Bowei Wu - bowei.wu@stud.unibas.ch (Responsible for the first part)
José A. Hernández - ja.hernandezsanchez@stud... | 71b3eaec15f8ce95c2f21843534d7f57b076960e | 65,975 | ipynb | Jupyter Notebook | Final_Projects/Hernandex_Wu/FinalProject_Hernandez_Wu.ipynb | ManuelRosenthaler/Quantum-information-course-Basel | 860af318d296d8ac7c86e56f71d8493461399744 | [
"Apache-2.0"
] | null | null | null | Final_Projects/Hernandex_Wu/FinalProject_Hernandez_Wu.ipynb | ManuelRosenthaler/Quantum-information-course-Basel | 860af318d296d8ac7c86e56f71d8493461399744 | [
"Apache-2.0"
] | null | null | null | Final_Projects/Hernandex_Wu/FinalProject_Hernandez_Wu.ipynb | ManuelRosenthaler/Quantum-information-course-Basel | 860af318d296d8ac7c86e56f71d8493461399744 | [
"Apache-2.0"
] | null | null | null | 112.011885 | 21,108 | 0.799454 | true | 7,153 | Qwen/Qwen-72B | 1. YES
2. YES | 0.822189 | 0.651355 | 0.535537 | __label__eng_Latn | 0.994027 | 0.082561 |
```python
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import control
```
# Efectos del lazo cerrado
## Objetivos
- Determinar la estabilidad de sistemas de lazo abierto y lazo cerrado.
- Verificar el efecto de cerrar un lazo de control sobre sistemas de tiempo continuo.
## Lazo cerrado
... | e08721cfbf501ba68ce3a901e005ab17129faf25 | 111,734 | ipynb | Jupyter Notebook | EfectosLazoCerrado.ipynb | pierrediazp/Control | 2a185eff5b5dc84045115009e62296174d072220 | [
"MIT"
] | null | null | null | EfectosLazoCerrado.ipynb | pierrediazp/Control | 2a185eff5b5dc84045115009e62296174d072220 | [
"MIT"
] | null | null | null | EfectosLazoCerrado.ipynb | pierrediazp/Control | 2a185eff5b5dc84045115009e62296174d072220 | [
"MIT"
] | 1 | 2021-11-18T13:08:36.000Z | 2021-11-18T13:08:36.000Z | 154.970874 | 17,632 | 0.891465 | true | 2,964 | Qwen/Qwen-72B | 1. YES
2. YES | 0.749087 | 0.874077 | 0.65476 | __label__spa_Latn | 0.853854 | 0.359558 |
# Introduction
## Karatsuba Multiplication(卡拉楚巴算法)
一种快速乘法算法，使两个 $n$ 位数字相乘所需的一位数乘法次数减少到至多 $3n^{log_23}\approx3n^{1.585}$ 次。
### 基本步骤
运用递归思想，将位数很多的两个大数 $x$ 和 $y$ 分成位数较少的数，每个数都是原来 $x$ 和 $y$ 位数的一半。这样处理后，简化为做三次乘法，并附带少量的加法操作和移位操作。
$$
\begin{align}
x \cdot y \, & = (10^{\frac n2} \cdot a + b) \times (10^{\frac n2} \cdot ... | 9f652cd4e237ce2db9d7293d47f3fe9cda620b8a | 4,305 | ipynb | Jupyter Notebook | Divide and Conquer, Sorting and Searching, and Randomized Algorithms/Week 1.ipynb | Alice0621/Notes-for-Stanford-Algorithms | 11a46c8e03cbac8e02f4350042c27bfddbc3b43e | [
"MIT"
] | null | null | null | Divide and Conquer, Sorting and Searching, and Randomized Algorithms/Week 1.ipynb | Alice0621/Notes-for-Stanford-Algorithms | 11a46c8e03cbac8e02f4350042c27bfddbc3b43e | [
"MIT"
] | null | null | null | Divide and Conquer, Sorting and Searching, and Randomized Algorithms/Week 1.ipynb | Alice0621/Notes-for-Stanford-Algorithms | 11a46c8e03cbac8e02f4350042c27bfddbc3b43e | [
"MIT"
] | null | null | null | 27.774194 | 107 | 0.416725 | true | 1,144 | Qwen/Qwen-72B | 1. YES
2. YES | 0.861538 | 0.808067 | 0.696181 | __label__yue_Hant | 0.260044 | 0.455793 |
### BEFORE YOU DO ANYTHING...
In the terminal:
1. Navigate to __inside__ your ILAS_Python repository.
2. __COMMIT__ any un-commited work on your personal computer.
3. __PULL__ any changes *you* have made using another computer.
4. __PULL__ textbook updates (including homework answers).
1. __Open Jupyter notebook:__ ... | bdca087b6605a4724effc1ee9931938c7d651f81 | 147,919 | ipynb | Jupyter Notebook | 7_Numerical_computation_with_Numpy.ipynb | michaelnicht/python-class | a614132229f4a70a1ca4eb2c979586177db329f1 | [
"MIT"
] | null | null | null | 7_Numerical_computation_with_Numpy.ipynb | michaelnicht/python-class | a614132229f4a70a1ca4eb2c979586177db329f1 | [
"MIT"
] | null | null | null | 7_Numerical_computation_with_Numpy.ipynb | michaelnicht/python-class | a614132229f4a70a1ca4eb2c979586177db329f1 | [
"MIT"
] | null | null | null | 24.897997 | 878 | 0.503309 | true | 25,459 | Qwen/Qwen-72B | 1. YES
2. YES | 0.672332 | 0.874077 | 0.58767 | __label__eng_Latn | 0.970129 | 0.203684 |
# Hauptkomponentenanalyse
# (Principal Component Analysis, PCA)
# vs.
# Denoising Variational Autoencoders
### _an Hand von Beispielen_
jupyter nbconvert PCAvsDVAEde.ipynb --to slides --post serve
# Eine intuitive Perspektive ...
#### "... realistische, hochdimensionale Daten konzentrieren sich in der Nähe einer n... | 8d03cafef04b067113c85aa246442a653b9287f6 | 33,033 | ipynb | Jupyter Notebook | PCAvsDVAEde.ipynb | caxenie/pca-vs-dvae | 2d9f8529c5b482bb5356f2ae1aa7e7a4af826f87 | [
"MIT"
] | null | null | null | PCAvsDVAEde.ipynb | caxenie/pca-vs-dvae | 2d9f8529c5b482bb5356f2ae1aa7e7a4af826f87 | [
"MIT"
] | null | null | null | PCAvsDVAEde.ipynb | caxenie/pca-vs-dvae | 2d9f8529c5b482bb5356f2ae1aa7e7a4af826f87 | [
"MIT"
] | null | null | null | 29.921196 | 400 | 0.593134 | true | 5,969 | Qwen/Qwen-72B | 1. YES
2. YES | 0.695958 | 0.774583 | 0.539078 | __label__deu_Latn | 0.812926 | 0.090788 |
# 実践データ科学入門 2020年度木曜4限
# 第3回 その1 重線形回帰
```python
%matplotlib inline
#%matplotlib notebook # if necessary to rotate figures in 3D plot
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d import art3d
from ipywidgets ... | 1ec3832b827ef503d3a3cdebfaa697dd977be83d | 28,678 | ipynb | Jupyter Notebook | Intro2PracDS_2020_03-1_MultipleRegression.ipynb | NTNKN/Intro2PracDS | a82631c4d9e31318a85bc41131e9c32d1cd4a2a5 | [
"BSD-3-Clause"
] | 1 | 2020-10-01T07:04:28.000Z | 2020-10-01T07:04:28.000Z | Intro2PracDS_2020_03-1_MultipleRegression.ipynb | NTNKN/Intro2PracDS | a82631c4d9e31318a85bc41131e9c32d1cd4a2a5 | [
"BSD-3-Clause"
] | null | null | null | Intro2PracDS_2020_03-1_MultipleRegression.ipynb | NTNKN/Intro2PracDS | a82631c4d9e31318a85bc41131e9c32d1cd4a2a5 | [
"BSD-3-Clause"
] | null | null | null | 32.887615 | 344 | 0.481066 | true | 10,692 | Qwen/Qwen-72B | 1. YES
2. YES | 0.893309 | 0.66888 | 0.597517 | __label__yue_Hant | 0.553659 | 0.226562 |
# Model Project
We start by importing necessary packages:
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import ipywidgets as widgets
import time
from scipy import linalg
from scipy import optimize
import sympy as sm
```
# Solow model with climate change
Consider the standard S... | d4e2c821fcb8279c6e902b26709f36d825a50324 | 170,112 | ipynb | Jupyter Notebook | modelproject/modelproject.ipynb | NumEconCopenhagen/projects-2019-tba | eea677b973b0205f293272027623ca3c13a3c23e | [
"MIT"
] | null | null | null | modelproject/modelproject.ipynb | NumEconCopenhagen/projects-2019-tba | eea677b973b0205f293272027623ca3c13a3c23e | [
"MIT"
] | 13 | 2019-04-08T17:01:11.000Z | 2019-05-14T18:47:37.000Z | modelproject/modelproject.ipynb | NumEconCopenhagen/projects-2019-tba | eea677b973b0205f293272027623ca3c13a3c23e | [
"MIT"
] | 2 | 2019-03-22T14:44:02.000Z | 2019-03-22T14:44:26.000Z | 155.49543 | 57,960 | 0.888926 | true | 4,028 | Qwen/Qwen-72B | 1. YES
2. YES | 0.917303 | 0.841826 | 0.772209 | __label__eng_Latn | 0.883305 | 0.632432 |
```python
#Import necessary packages
import numpy as np
import matplotlib.pyplot as plt
import scipy as sp
import math
from scipy.stats import linregress
from scipy.optimize import curve_fit
#from sympy import Symbol, Derivative
#from scipy.signal import savgol_filter as sf
import pandas as pd
```
```python
#List of... | ac275ff4cfbb8e77f92b735dd68f7ce9ba76e44b | 216,897 | ipynb | Jupyter Notebook | development_notebooks/ericas_OG_calc.ipynb | SacPec/Route_Dynamics_S-dev | 97214724dd520d3e618304e7516de79e7731bed5 | [
"MIT"
] | 4 | 2019-06-14T20:54:55.000Z | 2021-02-26T03:15:20.000Z | development_notebooks/ericas_OG_calc.ipynb | SacPec/Route_Dynamics_S-dev | 97214724dd520d3e618304e7516de79e7731bed5 | [
"MIT"
] | 9 | 2019-05-13T14:49:42.000Z | 2020-12-17T04:48:33.000Z | development_notebooks/ericas_OG_calc.ipynb | SacPec/Route_Dynamics_S-dev | 97214724dd520d3e618304e7516de79e7731bed5 | [
"MIT"
] | 7 | 2020-02-04T20:12:42.000Z | 2021-11-03T19:27:01.000Z | 248.165904 | 58,012 | 0.901188 | true | 5,153 | Qwen/Qwen-72B | 1. YES
2. YES | 0.845942 | 0.779993 | 0.659829 | __label__eng_Latn | 0.235439 | 0.371335 |
# 3M1 Introduction to optimization
Luca Magri (lm547@cam.ac.uk), office ISO-44, Hopkinson Lab.
(With many thanks to Professor Gábor Csányi.)
[Booklist](https://www.vle.cam.ac.uk/mod/book/view.php?id=364091&chapterid=49051):
- Antoniou, A. & Lu, W.-S. Practical Optimization: Algorithms and Engineering Applications, ... | df6e34f9a6ec6d63350e36e92ebfefeda1ec5bf1 | 623,003 | ipynb | Jupyter Notebook | Lecture_1_Introduction_to_optimization_3M1_LM.ipynb | LukeMagher/3M1 | d3b6f06d8ecde209c405b412dcdcf1af3c9cfb98 | [
"BSD-2-Clause"
] | 2 | 2020-09-23T08:16:18.000Z | 2021-12-28T12:35:26.000Z | Lecture_1_Introduction_to_optimization_3M1_LM.ipynb | LukeMagher/3M1 | d3b6f06d8ecde209c405b412dcdcf1af3c9cfb98 | [
"BSD-2-Clause"
] | null | null | null | Lecture_1_Introduction_to_optimization_3M1_LM.ipynb | LukeMagher/3M1 | d3b6f06d8ecde209c405b412dcdcf1af3c9cfb98 | [
"BSD-2-Clause"
] | null | null | null | 1,263.697769 | 223,935 | 0.947904 | true | 5,190 | Qwen/Qwen-72B | 1. YES
2. YES | 0.831143 | 0.901921 | 0.749625 | __label__eng_Latn | 0.968653 | 0.579962 |
# Simon's Algorithm
**Abstract:** We study a quantum algorithm known as Simon's algorithm, which provided the first example of an exponential speedup over the best known classical algorithm by using a quantum computer to solve a particular problem. Originally published in 1994, Simon's algorithm was a precursor to Sho... | 66deaf946b8e0ab0403f43857f0207ae6d096145 | 154,774 | ipynb | Jupyter Notebook | examples/advanced_circuits_algorithms/Simons_Algorithm/Simons_Algorithm.ipynb | virajvchaudhari/amazon-braket-examples | 4d48555f4aa5cbf86ece8a472b9913f14b22b768 | [
"Apache-2.0"
] | null | null | null | examples/advanced_circuits_algorithms/Simons_Algorithm/Simons_Algorithm.ipynb | virajvchaudhari/amazon-braket-examples | 4d48555f4aa5cbf86ece8a472b9913f14b22b768 | [
"Apache-2.0"
] | null | null | null | examples/advanced_circuits_algorithms/Simons_Algorithm/Simons_Algorithm.ipynb | virajvchaudhari/amazon-braket-examples | 4d48555f4aa5cbf86ece8a472b9913f14b22b768 | [
"Apache-2.0"
] | null | null | null | 192.504975 | 84,964 | 0.875683 | true | 7,936 | Qwen/Qwen-72B | 1. YES
2. YES | 0.749087 | 0.757794 | 0.567654 | __label__eng_Latn | 0.99414 | 0.15718 |
```python
from preamble import *
%matplotlib inline
```
## Model Evaluation and Improvement
```python
from sklearn.datasets import make_blobs
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
# create a synthetic dataset
X, y = make_blobs(random_state=0)
# spli... | 3e17cd2cd6d008b25bedab40db580b803092ac5c | 925,009 | ipynb | Jupyter Notebook | 05-model-evaluation-and-improvement.ipynb | mbooali/introduction-to-machine | 3f75f9897f1f63f07bb6eace312fa35e16786623 | [
"MIT"
] | null | null | null | 05-model-evaluation-and-improvement.ipynb | mbooali/introduction-to-machine | 3f75f9897f1f63f07bb6eace312fa35e16786623 | [
"MIT"
] | null | null | null | 05-model-evaluation-and-improvement.ipynb | mbooali/introduction-to-machine | 3f75f9897f1f63f07bb6eace312fa35e16786623 | [
"MIT"
] | null | null | null | 302.488228 | 109,348 | 0.916648 | true | 14,274 | Qwen/Qwen-72B | 1. YES
2. YES | 0.718594 | 0.782662 | 0.562417 | __label__eng_Latn | 0.238593 | 0.145013 |
# SciPy / Havana
In case I get to go to [SciPy / Havana](http://conf.scipyla.org/), I'm boning up on SymPy, an important component within the SciPy Ecosystem.
Here's the kind of thing one might do with SymPy, a computer algebra system:
```python
import sympy as sym
```
```python
x = sym.symbols('x')
sym.init_prin... | 958a48933b124b0a9a645b2b91d6fb469228b161 | 6,763 | ipynb | Jupyter Notebook | Using SymPy.ipynb | 4dsolutions/Python5 | 8d80753e823441a571b827d24d21577446409b52 | [
"MIT"
] | 11 | 2016-08-17T00:15:26.000Z | 2020-07-17T21:31:10.000Z | Using SymPy.ipynb | 4dsolutions/Python5 | 8d80753e823441a571b827d24d21577446409b52 | [
"MIT"
] | null | null | null | Using SymPy.ipynb | 4dsolutions/Python5 | 8d80753e823441a571b827d24d21577446409b52 | [
"MIT"
] | 5 | 2017-02-22T05:15:52.000Z | 2019-11-08T06:17:34.000Z | 37.994382 | 2,120 | 0.692148 | true | 523 | Qwen/Qwen-72B | 1. YES
2. YES | 0.888759 | 0.849971 | 0.755419 | __label__eng_Latn | 0.966117 | 0.593424 |
# Maximum Mean Discrepancy drift detector on CIFAR-10
### Method
The [Maximum Mean Discrepancy (MMD)](http://jmlr.csail.mit.edu/papers/v13/gretton12a.html) detector is a kernel-based method for multivariate 2 sample testing. The MMD is a distance-based measure between 2 distributions *p* and *q* based on the mean emb... | 5877afd98f0716120a42a49d436dbf6e8a33a6ef | 65,207 | ipynb | Jupyter Notebook | examples/cd_mmd_cifar10.ipynb | jklaise/alibi-detect | fd5f21cb071462f6701761dc13003824a0749ef7 | [
"ECL-2.0",
"Apache-2.0",
"CC0-1.0"
] | null | null | null | examples/cd_mmd_cifar10.ipynb | jklaise/alibi-detect | fd5f21cb071462f6701761dc13003824a0749ef7 | [
"ECL-2.0",
"Apache-2.0",
"CC0-1.0"
] | null | null | null | examples/cd_mmd_cifar10.ipynb | jklaise/alibi-detect | fd5f21cb071462f6701761dc13003824a0749ef7 | [
"ECL-2.0",
"Apache-2.0",
"CC0-1.0"
] | null | null | null | 75.471065 | 10,016 | 0.811876 | true | 4,089 | Qwen/Qwen-72B | 1. YES
2. YES | 0.76908 | 0.675765 | 0.519717 | __label__eng_Latn | 0.863145 | 0.045806 |
# Symbolic Mathematics in Python
There are times when you need to solve a difficult problem symbollically or analytically. If you have ever used Wolfram Alpha, then you have already done this. Sympy is a python library that allows you to do symbolic mathematics in python.
```python
import sympy as sym
```
## 1. I... | 8133f703170455921bdb38533e5e7036ebb7ae53 | 271,365 | ipynb | Jupyter Notebook | symbolic_math.ipynb | sju-chem264-2019/9-26-2019-symbolic-math-NatalieWilliams16 | f081f9c72193138a8f63a52b7d7126daca705b5c | [
"MIT"
] | null | null | null | symbolic_math.ipynb | sju-chem264-2019/9-26-2019-symbolic-math-NatalieWilliams16 | f081f9c72193138a8f63a52b7d7126daca705b5c | [
"MIT"
] | null | null | null | symbolic_math.ipynb | sju-chem264-2019/9-26-2019-symbolic-math-NatalieWilliams16 | f081f9c72193138a8f63a52b7d7126daca705b5c | [
"MIT"
] | null | null | null | 143.731462 | 25,740 | 0.84379 | true | 3,454 | Qwen/Qwen-72B | 1. YES
2. YES | 0.885631 | 0.835484 | 0.739931 | __label__eng_Latn | 0.862993 | 0.557438 |
# From Oliver Durr
## Variational Autoencoder (VAE)
A tutorial with code for a VAE as described in [Kingma and Welling, 2013](http://arxiv.org/abs/1312.6114). A talk with more details was given at the [DataLab Brown Bag Seminar](https://home.zhaw.ch/~dueo/bbs/files/vae.pdf).
Much of the code was taken, from https://j... | 1c8d29fa54dff5c59ec5079935503c71bac46f5e | 5,154 | ipynb | Jupyter Notebook | autoencoder_keras/vae_theory_mardown_only.ipynb | OliverColeman/neuralnets | cf77fe28beda3705f21fd64d072139128d1f3aa6 | [
"MIT"
] | 180 | 2017-01-18T12:29:29.000Z | 2022-03-17T23:36:27.000Z | autoencoder_keras/vae_theory_mardown_only.ipynb | OliverColeman/neuralnets | cf77fe28beda3705f21fd64d072139128d1f3aa6 | [
"MIT"
] | 12 | 2017-03-12T21:09:08.000Z | 2019-04-01T12:14:38.000Z | autoencoder_keras/vae_theory_mardown_only.ipynb | mzaradzki/neuralnets | 84921f770a0413ac1bc829764cbc51065f289e0b | [
"MIT"
] | 117 | 2017-03-19T08:15:09.000Z | 2020-07-14T08:06:19.000Z | 44.431034 | 477 | 0.523671 | true | 1,341 | Qwen/Qwen-72B | 1. YES
2. YES | 0.83762 | 0.795658 | 0.666459 | __label__eng_Latn | 0.864142 | 0.386739 |
<i>Copyright (c) Microsoft Corporation. All rights reserved.</i>
<i>Licensed under the MIT License.</i>
# Bayesian Personalized Ranking (BPR)
This notebook serves as an introduction to Bayesian Personalized Ranking (BPR) model for implicit feedback. In this tutorial, we focus on learning the BPR model using matrix ... | f8fe1ddcff1c1f3f811b209153648f0a823f7ca3 | 21,567 | ipynb | Jupyter Notebook | notebooks/02_model/cornac_bpr_deep_dive.ipynb | elogicaadith/recommenders | 7c0a6a3e23dee047b9afbf8564bd236a8300454e | [
"MIT"
] | 3 | 2019-12-13T22:35:55.000Z | 2020-01-05T22:19:56.000Z | notebooks/02_model/cornac_bpr_deep_dive.ipynb | awesomemachinelearning/recommenders | de3782cce370a446e14e6b47e87686867fb7e069 | [
"MIT"
] | 1 | 2019-06-05T00:24:27.000Z | 2019-06-05T00:24:27.000Z | notebooks/02_model/cornac_bpr_deep_dive.ipynb | awesomemachinelearning/recommenders | de3782cce370a446e14e6b47e87686867fb7e069 | [
"MIT"
] | 4 | 2019-06-05T00:04:11.000Z | 2019-06-08T02:20:35.000Z | 36.186242 | 410 | 0.536839 | true | 4,116 | Qwen/Qwen-72B | 1. YES
2. YES | 0.798187 | 0.721743 | 0.576086 | __label__eng_Latn | 0.860986 | 0.17677 |
# 運動学的方程式の導出 （XYZオイラー角）
吉田勝俊（宇都宮大学）
## 参考情報
- [SymPyで代数演算してみる - Qiita](https://qiita.com/zawawahoge/items/1be137a8147902a5e6cb)
- [Matrices (linear algebra) — SymPy 1.6.2 documentation](https://docs.sympy.org/latest/modules/matrices/matrices.html)
```python
import sympy as sym #数式処理ライブラリ
sym.init_printing() #... | 3e8366359cb78e7158d9bb9e4fa78c2f0b72294a | 8,109 | ipynb | Jupyter Notebook | m3d/Colab/Python_9.2.ipynb | ktysd/_colab_test | 0ffc4a63dce926e21647f4497269ac90f4eaa941 | [
"MIT"
] | null | null | null | m3d/Colab/Python_9.2.ipynb | ktysd/_colab_test | 0ffc4a63dce926e21647f4497269ac90f4eaa941 | [
"MIT"
] | null | null | null | m3d/Colab/Python_9.2.ipynb | ktysd/_colab_test | 0ffc4a63dce926e21647f4497269ac90f4eaa941 | [
"MIT"
] | null | null | null | 8,109 | 8,109 | 0.630657 | true | 1,738 | Qwen/Qwen-72B | 1. YES
2. YES | 0.913677 | 0.831143 | 0.759396 | __label__yue_Hant | 0.387264 | 0.602663 |
# Python _for fun and profit_
###### Juan Luis Cano Rodríguez
###### Madrid, 2016-05-13 @ ETS Asset Management Factory
## Outline
* Introduction
* Python for Data Science
* Python for IT
* General advice
* Conclusions
## Outline
* Introduction
* Python for Data Science
* Interactive computation with Jupyter
... | be60b18c903c897f42af4824fe7e88ed52053399 | 769,437 | ipynb | Jupyter Notebook | Python for fun and profit.ipynb | Juanlu001/python-fun-and-profit | ac9be81e9a151024be7d3123901f031665d9e766 | [
"CC0-1.0"
] | 1 | 2019-02-04T08:59:51.000Z | 2019-02-04T08:59:51.000Z | Python for fun and profit.ipynb | Juanlu001/python-fun-and-profit | ac9be81e9a151024be7d3123901f031665d9e766 | [
"CC0-1.0"
] | null | null | null | Python for fun and profit.ipynb | Juanlu001/python-fun-and-profit | ac9be81e9a151024be7d3123901f031665d9e766 | [
"CC0-1.0"
] | null | null | null | 817.680128 | 622,632 | 0.942076 | true | 2,556 | Qwen/Qwen-72B | 1. YES
2. YES | 0.712232 | 0.800692 | 0.570279 | __label__eng_Latn | 0.926111 | 0.163278 |
```python
from sympy import *
init_printing(use_unicode=True)
```
# Matriz con respecto a la base canónica de la proyección ortogonal sobre un subespacio
$$
\left[P_{gen\{G^{-1}w\}}\right]^{E}_{E}
$$
```python
def matriz_de_proyeccion_ortogonal_en_base_canonica(S, dimension, G=None):
'''
w: es el vecto... | c993f7cd3531f0fca671fdfa772c17b3dd8b2abb | 13,844 | ipynb | Jupyter Notebook | notebooks_varios/Proyecciones Ortogonales y Distancia de un Vector a un Subespacio.ipynb | ilitteri/manim-algebra-notebooks | d57461de498ca574e7d866dae1d091a2c5a6880a | [
"MIT"
] | 2 | 2021-11-12T21:23:35.000Z | 2022-02-27T14:43:48.000Z | notebooks_varios/Proyecciones Ortogonales y Distancia de un Vector a un Subespacio.ipynb | ilitteri/manim-algebra-notebooks | d57461de498ca574e7d866dae1d091a2c5a6880a | [
"MIT"
] | null | null | null | notebooks_varios/Proyecciones Ortogonales y Distancia de un Vector a un Subespacio.ipynb | ilitteri/manim-algebra-notebooks | d57461de498ca574e7d866dae1d091a2c5a6880a | [
"MIT"
] | 1 | 2021-11-12T18:24:56.000Z | 2021-11-12T18:24:56.000Z | 53.451737 | 3,124 | 0.692791 | true | 1,362 | Qwen/Qwen-72B | 1. YES
2. YES | 0.894789 | 0.855851 | 0.765807 | __label__spa_Latn | 0.762062 | 0.617557 |
```python
from sympy import *
```
```python
s=10
```
```python
x = Symbol("x")
```
```python
y = x
#y = (2*x-1)
```
```python
legpols = []
```
```python
for k in range(0,s+1):
legpol = 0
for j in range(0, k//2+1):
legpol += (-1)**j * factorial(2*k - 2*j) / factorial(k-j) / factorial(k-2*j) / f... | 217e20cc4bdf2d2aba015ba5b3dfdd1e8e6d7c23 | 2,665 | ipynb | Jupyter Notebook | prototyping/Legendre_Polynomials_Standard.ipynb | krystophny/GeometricIntegrators.jl | 7855e977b014c8ba119f6bb73c6ed9bf96f04b1d | [
"MIT"
] | 6 | 2020-12-29T10:41:35.000Z | 2022-03-21T11:48:39.000Z | prototyping/Legendre_Polynomials_Standard.ipynb | krystophny/GeometricIntegrators.jl | 7855e977b014c8ba119f6bb73c6ed9bf96f04b1d | [
"MIT"
] | 15 | 2020-11-16T16:45:50.000Z | 2022-03-09T17:51:11.000Z | prototyping/Legendre_Polynomials_Standard.ipynb | krystophny/GeometricIntegrators.jl | 7855e977b014c8ba119f6bb73c6ed9bf96f04b1d | [
"MIT"
] | 2 | 2021-05-05T12:54:38.000Z | 2021-12-17T18:19:13.000Z | 19.595588 | 128 | 0.451032 | true | 444 | Qwen/Qwen-72B | 1. YES
2. YES | 0.872347 | 0.727975 | 0.635047 | __label__yue_Hant | 0.243585 | 0.313759 |
# Developing Quaternions for iPython
In this notebook, tools for working with quaternions for physics issues are developed. The class QH treat quaternions as Hamilton would have done: as a 4-vector over the real numbers.
```python
import math
import numpy as np
import pdb
import random
import sympy as sp
import unit... | d626b00c24f2cf342d0f583d0d0f1a5de0b4d20c | 121,187 | ipynb | Jupyter Notebook | Notebooks/QH.ipynb | dougsweetser/AIG | ce23119bbde41671438fb805dfba4b04b42d84d6 | [
"Apache-2.0"
] | null | null | null | Notebooks/QH.ipynb | dougsweetser/AIG | ce23119bbde41671438fb805dfba4b04b42d84d6 | [
"Apache-2.0"
] | null | null | null | Notebooks/QH.ipynb | dougsweetser/AIG | ce23119bbde41671438fb805dfba4b04b42d84d6 | [
"Apache-2.0"
] | null | null | null | 43.781431 | 218 | 0.430657 | true | 26,281 | Qwen/Qwen-72B | 1. YES
2. YES | 0.899121 | 0.779993 | 0.701308 | __label__eng_Latn | 0.415636 | 0.467706 |
```python
from sympy.physics.mechanics import ReferenceFrame,Point,dynamicsymbols
from sympy.physics.mechanics import Point
from sympy import latex,pprint,symbols,init_printing
from sympy.algebras.quaternion import Quaternion
import numpy as np
init_printing() # Para visualizar símbolos
```
## 3. Cinemática de puntos
... | db90e522c630118e4322361fc0b1baf734ecd2e4 | 29,562 | ipynb | Jupyter Notebook | Capitulo_3/Capitulo3.ipynb | JonathanCamargo/Dinamica_Mecanica_Material_Interactivo | 58fd21f1efccd1eda88f03e39cf69d122d280fdd | [
"MIT"
] | null | null | null | Capitulo_3/Capitulo3.ipynb | JonathanCamargo/Dinamica_Mecanica_Material_Interactivo | 58fd21f1efccd1eda88f03e39cf69d122d280fdd | [
"MIT"
] | null | null | null | Capitulo_3/Capitulo3.ipynb | JonathanCamargo/Dinamica_Mecanica_Material_Interactivo | 58fd21f1efccd1eda88f03e39cf69d122d280fdd | [
"MIT"
] | null | null | null | 103.003484 | 6,356 | 0.815134 | true | 1,073 | Qwen/Qwen-72B | 1. YES
2. YES | 0.934395 | 0.808067 | 0.755054 | __label__spa_Latn | 0.955436 | 0.592576 |
# Check CFHT Zero Point Value
## Theoretical Background
The Zero Point value (in ADU/sec) for the `r` filter in the [CFHT specifications page](https://www.cfht.hawaii.edu/Instruments/Imaging/Megacam/generalinformation.html) is 26.22 and it seems to be over estimated. In the [LSST GitHub code](https://github.com/LSSTD... | d34c3cf17c63917bf41fd3996cbb2095132a08cf | 4,052 | ipynb | Jupyter Notebook | data/CFHT/ZP_check.ipynb | CosmoStat/ShapeDeconv | 3869cb6b9870ff1060498eedcb99e8f95908f01a | [
"MIT"
] | 4 | 2020-12-17T14:58:28.000Z | 2022-01-22T06:03:55.000Z | data/CFHT/ZP_check.ipynb | CosmoStat/ShapeDeconv | 3869cb6b9870ff1060498eedcb99e8f95908f01a | [
"MIT"
] | 9 | 2021-01-13T10:38:28.000Z | 2021-07-06T23:37:08.000Z | data/CFHT/ZP_check.ipynb | CosmoStat/ShapeDeconv | 3869cb6b9870ff1060498eedcb99e8f95908f01a | [
"MIT"
] | null | null | null | 33.213115 | 410 | 0.578727 | true | 895 | Qwen/Qwen-72B | 1. YES
2. YES | 0.956634 | 0.828939 | 0.792991 | __label__eng_Latn | 0.869241 | 0.680717 |
# [1] Scientific computation
There are several packages that provide multidimensional data manipulation, optimization, regression, interpolation and visualization, among other possibilities.
## 0. Some arithmetic insights
### [Integers](https://docs.python.org/3/c-api/long.html)
In python, integers have arbitrary p... | 74ec891bc6bd2b9e6b3528b78f37d9604bed0346 | 80,275 | ipynb | Jupyter Notebook | 20-scientific_computation.ipynb | leowindwave/YAPT | ee5ec568ed746f90a18dc514836624d435a7ccdb | [
"CC0-1.0"
] | null | null | null | 20-scientific_computation.ipynb | leowindwave/YAPT | ee5ec568ed746f90a18dc514836624d435a7ccdb | [
"CC0-1.0"
] | null | null | null | 20-scientific_computation.ipynb | leowindwave/YAPT | ee5ec568ed746f90a18dc514836624d435a7ccdb | [
"CC0-1.0"
] | null | null | null | 21.509914 | 1,013 | 0.465712 | true | 15,604 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91611 | 0.835484 | 0.765395 | __label__eng_Latn | 0.243223 | 0.6166 |
\title{myHDL Combinational Logic Elements: Demultiplexers (DEMUXs))}
\author{Steven K Armour}
\maketitle
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc" style="margin-top: 1em;"><ul class="toc-item"><li><span><a href="#Refrances" data-toc-modified-id="Refrances-1"><span class="toc-item-num">1&... | 0eba95224c0d4fcd85e1b599895e001437e51dc9 | 210,860 | ipynb | Jupyter Notebook | myHDL_DigLogicFundamentals/myHDL_Combinational/Demultiplexers(DEMUX).ipynb | PyLCARS/PythonUberHDL | f7ae2293d6efaca7986d62540798cdf061383d06 | [
"BSD-3-Clause"
] | 31 | 2017-10-09T12:15:14.000Z | 2022-02-28T09:05:21.000Z | myHDL_DigLogicFundamentals/myHDL_Combinational/Demultiplexers(DEMUX).ipynb | cfelton/PythonUberHDL | f7ae2293d6efaca7986d62540798cdf061383d06 | [
"BSD-3-Clause"
] | null | null | null | myHDL_DigLogicFundamentals/myHDL_Combinational/Demultiplexers(DEMUX).ipynb | cfelton/PythonUberHDL | f7ae2293d6efaca7986d62540798cdf061383d06 | [
"BSD-3-Clause"
] | 12 | 2018-02-09T15:36:20.000Z | 2021-04-20T21:39:12.000Z | 30.440306 | 11,221 | 0.380115 | true | 44,691 | Qwen/Qwen-72B | 1. YES
2. YES | 0.685949 | 0.815232 | 0.559208 | __label__yue_Hant | 0.237068 | 0.137558 |
<a href="https://colab.research.google.com/github/kojiyam/information-theory/blob/main/it3.ipynb" target="_parent"></a>
# 巡回符号・ガロア体上の多項式環における剰余
参考 https://stackoverflow.com/questions/14173007/polynomial-with-modular-coefficients-library-in-python
https://numpy.org/doc/stable/reference/generated/numpy.poly1d.html#num... | a68784cc2b4f183bca35ec54a9b6e230ac2bb482 | 30,889 | ipynb | Jupyter Notebook | it3.ipynb | kojiyam/information-theory | 601cf119b8c7b5d24bd61594e2a4d7b9a8c6f223 | [
"MIT"
] | null | null | null | it3.ipynb | kojiyam/information-theory | 601cf119b8c7b5d24bd61594e2a4d7b9a8c6f223 | [
"MIT"
] | null | null | null | it3.ipynb | kojiyam/information-theory | 601cf119b8c7b5d24bd61594e2a4d7b9a8c6f223 | [
"MIT"
] | null | null | null | 41.075798 | 2,774 | 0.618473 | true | 1,549 | Qwen/Qwen-72B | 1. YES
2. YES | 0.787931 | 0.737158 | 0.58083 | __label__yue_Hant | 0.323146 | 0.187792 |
# Description:
* calculations for modeling fragments in a CsCl gradient under non-equilibrium conditions
# Notes
* Good chapter on determining G+C content from CsCl gradient analysis
http://www.academia.edu/428160/Using_Analytical_Ultracentrifugation_of_DNA_in_CsCl_Gradients_to_Explore_Large-Scale_Properties_of_Geno... | e5c5f731a819b1a46e17efdfcc7b86f6751f9d40 | 60,930 | ipynb | Jupyter Notebook | ipynb/.ipynb_checkpoints/non-equilibrium_calcs-checkpoint.ipynb | arischwartz/test | 87a8306a294f59b0eef992529ce900cea876c605 | [
"MIT"
] | 2 | 2019-03-15T09:46:48.000Z | 2019-06-05T18:16:39.000Z | ipynb/.ipynb_checkpoints/non-equilibrium_calcs-checkpoint.ipynb | arischwartz/test | 87a8306a294f59b0eef992529ce900cea876c605 | [
"MIT"
] | 1 | 2020-11-01T23:18:10.000Z | 2020-11-01T23:18:10.000Z | ipynb/.ipynb_checkpoints/non-equilibrium_calcs-checkpoint.ipynb | arischwartz/test | 87a8306a294f59b0eef992529ce900cea876c605 | [
"MIT"
] | null | null | null | 81.348465 | 17,851 | 0.80389 | true | 2,911 | Qwen/Qwen-72B | 1. YES
2. YES | 0.782662 | 0.665411 | 0.520792 | __label__eng_Latn | 0.545708 | 0.048303 |
```python
import numpy as np
import sympy
sympy.init_printing(use_unicode=True)
from sympy import symbols,simplify,diff,latex,Piecewise
from sympy.solvers import solve
from IPython.display import display
from typing import Callable
from sympy.utilities.lambdify import lambdify, implemented_function
%matplotlib inline
... | fb3e8c8f123f88051678b507a976073068600f10 | 317,354 | ipynb | Jupyter Notebook | old/market-equilibrium-old-demand-curves.ipynb | erelsgl/bitcoin-simulations | 79bfa0930ab9ad17be59b9cad1ec6e7c3530aa3b | [
"MIT"
] | 1 | 2018-11-26T02:44:38.000Z | 2018-11-26T02:44:38.000Z | old/market-equilibrium-old-demand-curves.ipynb | erelsgl/bitcoin-simulations | 79bfa0930ab9ad17be59b9cad1ec6e7c3530aa3b | [
"MIT"
] | null | null | null | old/market-equilibrium-old-demand-curves.ipynb | erelsgl/bitcoin-simulations | 79bfa0930ab9ad17be59b9cad1ec6e7c3530aa3b | [
"MIT"
] | 3 | 2018-09-06T00:11:26.000Z | 2021-08-29T17:14:59.000Z | 181.448828 | 22,926 | 0.852175 | true | 3,368 | Qwen/Qwen-72B | 1. YES
2. YES | 0.857768 | 0.853913 | 0.732459 | __label__eng_Latn | 0.257587 | 0.54008 |
##Ejercicio 4 Practica 1
Para cada uno de los siguientes sistemas encontrar todos los puntos de equilibrio y determinar el tipo de cada punto de equilibio aislado.
* c)
$$\left\{ \begin{array}{lcc}
\dot{x}_{1}=(1-x_{1})x_{1}-\frac{2x_{1}x_{2}}{1+x_{1}}\\
\\ \dot{x}_{2}=(2-\frac{x_{2}}{1+x_... | bbdcced9c2e3ea6b03eb25048b684892bb166986 | 21,009 | ipynb | Jupyter Notebook | practica1_eje4_c.ipynb | elsuizo/Nonlinear_systems | 9636d4a450339b8c735934923810c9539ac76042 | [
"MIT"
] | null | null | null | practica1_eje4_c.ipynb | elsuizo/Nonlinear_systems | 9636d4a450339b8c735934923810c9539ac76042 | [
"MIT"
] | null | null | null | practica1_eje4_c.ipynb | elsuizo/Nonlinear_systems | 9636d4a450339b8c735934923810c9539ac76042 | [
"MIT"
] | null | null | null | 64.051829 | 2,852 | 0.751154 | true | 389 | Qwen/Qwen-72B | 1. YES
2. YES | 0.924142 | 0.865224 | 0.79959 | __label__spa_Latn | 0.281417 | 0.696048 |
```python
import numpy as np
import sympy as sp
import matplotlib.pyplot as plt
from pyodesys.tests._robertson import get_ode_exprs
from pyodesys.symbolic import ScaledSys, PartiallySolvedSystem
sp.init_printing()
%matplotlib inline
```
```python
linf, linj = get_ode_exprs()
logf, logj = get_ode_exprs(True, True)
li... | 6ed7264819a68b85920479a2c7f5bea9387e3731 | 4,077 | ipynb | Jupyter Notebook | examples/_extend_by_integration.ipynb | slayoo/pyodesys | 8e1afb195dadf6c6f8e765873bc9dd0fae067c39 | [
"BSD-2-Clause"
] | 82 | 2015-09-29T16:51:03.000Z | 2022-02-02T13:26:50.000Z | examples/_extend_by_integration.ipynb | slayoo/pyodesys | 8e1afb195dadf6c6f8e765873bc9dd0fae067c39 | [
"BSD-2-Clause"
] | 28 | 2015-09-29T14:40:45.000Z | 2021-09-18T19:29:50.000Z | examples/_extend_by_integration.ipynb | slayoo/pyodesys | 8e1afb195dadf6c6f8e765873bc9dd0fae067c39 | [
"BSD-2-Clause"
] | 13 | 2016-03-18T14:00:39.000Z | 2021-09-17T13:54:29.000Z | 27.362416 | 129 | 0.574197 | true | 729 | Qwen/Qwen-72B | 1. YES
2. YES | 0.774583 | 0.746139 | 0.577947 | __label__eng_Latn | 0.201839 | 0.181094 |
# Mixed Integer Linear Programming (MILP)
## Introduction
* Some variables are restricted to be integers
* NP-complete
* Applications
* Production planning
* Scheduling
* Many more...
## The standard form
\begin{align}
\text{maximize}\ & \mathbf{c}^T\mathbf{x} + \mathbf{k}^T\mathbf{y} \\
\text{subject ... | a2573da47dda73fcef7f83bbf17f1e829b159d78 | 4,538 | ipynb | Jupyter Notebook | mathematicalProgramming/Video08/Video08.ipynb | codingperspective/videoMaterials | 8c9665466d8912c6f0c701c25ad9eb4802fb73a3 | [
"CC0-1.0"
] | null | null | null | mathematicalProgramming/Video08/Video08.ipynb | codingperspective/videoMaterials | 8c9665466d8912c6f0c701c25ad9eb4802fb73a3 | [
"CC0-1.0"
] | null | null | null | mathematicalProgramming/Video08/Video08.ipynb | codingperspective/videoMaterials | 8c9665466d8912c6f0c701c25ad9eb4802fb73a3 | [
"CC0-1.0"
] | 6 | 2021-11-21T05:02:50.000Z | 2022-02-17T04:44:57.000Z | 22.577114 | 286 | 0.485236 | true | 736 | Qwen/Qwen-72B | 1. YES
2. YES | 0.969785 | 0.877477 | 0.850964 | __label__eng_Latn | 0.43201 | 0.815408 |
<a href="https://colab.research.google.com/github/deanhadzi/DS-Unit-2-Regression-Classification/blob/master/module2/DSPT2_lesson_regression_classification_2.ipynb" target="_parent"></a>
Lambda School Data Science
*Unit 2, Sprint 1, Module 2*
---
# Regression & Classification, Module 2
- Go from simple regression ... | 80273432b08c293b0d2d1bf2ec43da163154c513 | 643,409 | ipynb | Jupyter Notebook | module2/DSPT2_lesson_regression_classification_2.ipynb | deanhadzi/DS-Unit-2-Regression-Classification | a8392f380d76c4fbf7deb75ffbf266c08ae6c537 | [
"MIT"
] | null | null | null | module2/DSPT2_lesson_regression_classification_2.ipynb | deanhadzi/DS-Unit-2-Regression-Classification | a8392f380d76c4fbf7deb75ffbf266c08ae6c537 | [
"MIT"
] | null | null | null | module2/DSPT2_lesson_regression_classification_2.ipynb | deanhadzi/DS-Unit-2-Regression-Classification | a8392f380d76c4fbf7deb75ffbf266c08ae6c537 | [
"MIT"
] | null | null | null | 210.677472 | 197,090 | 0.831724 | true | 10,149 | Qwen/Qwen-72B | 1. YES
2. YES
| 0.754915 | 0.672332 | 0.507553 | __label__eng_Latn | 0.783499 | 0.017545 |
# Lecture 3
In this lecture we move onto non-homogeneous second-order ordinary differentials. We want to solve equations of the form
$$
a \frac{d^{2}y}{dx^{2}} + b \frac{dy}{dx} + c y = f(x)
$$
where $a$, $b$ and $c$ are real constants. We've seem previously that for the case $b^{2} \ne 4ac$ and
$f(x) = 0$, the gene... | 2e7f38c68684f432a5e82f3d7f795d8c30dad26d | 102,810 | ipynb | Jupyter Notebook | notebooks/Lecture3.ipynb | quang-ha/IA-maths-Ipython | 8ff8533d64a3d8db8e4813a7b6dfee39339fd846 | [
"BSD-3-Clause"
] | null | null | null | notebooks/Lecture3.ipynb | quang-ha/IA-maths-Ipython | 8ff8533d64a3d8db8e4813a7b6dfee39339fd846 | [
"BSD-3-Clause"
] | null | null | null | notebooks/Lecture3.ipynb | quang-ha/IA-maths-Ipython | 8ff8533d64a3d8db8e4813a7b6dfee39339fd846 | [
"BSD-3-Clause"
] | null | null | null | 154.137931 | 18,565 | 0.837039 | true | 2,409 | Qwen/Qwen-72B | 1. YES
2. YES | 0.894789 | 0.890294 | 0.796626 | __label__eng_Latn | 0.974506 | 0.689161 |
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