text stringlengths 2.5k 6.39M | kind stringclasses 3
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|---|---|
```
%matplotlib inline
import numpy as np
from scipy.sparse.linalg import spsolve
from scipy.sparse import csr_matrix
import matplotlib.pyplot as plt
import seaborn as sns
from condlib import conductance_matrix_READ
from timeit import default_timer as timer
# Memory array parameters
rL = 12
rHRS = 1e6
rPU = 1e3
n = 16
... | github_jupyter |
# Telescopes: Tutorial 5
This notebook will build on the previous tutorials, showing more features of the `PsrSigSim`. Details will be given for new features, while other features have been discussed in the previous tutorial notebook. This notebook shows the details of different telescopes currently included in the `P... | github_jupyter |
```
import glob
import os
import sys
import struct
import pandas as pd
from nltk.tokenize import sent_tokenize
from tensorflow.core.example import example_pb2
sys.path.append('../src')
import data_io, params, SIF_embedding
def return_bytes(reader_obj):
len_bytes = reader_obj.read(8)
str_len = struct.unpack('q... | github_jupyter |
## FCLA/FNLA Fast.ai Numerical/Computational Linear Algebra
### Lecture 3: New Perspectives on NMF, Randomized SVD
Notes / In-Class Questions
WNixalo - 2018/2/8
Question on section: [Truncated SVD](http://nbviewer.jupyter.org/github/fastai/numerical-linear-algebra/blob/master/nbs/2.%20Topic%20Modeling%20with%20NMF%2... | github_jupyter |
# USDA Unemployment
<hr>
```
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
```
# Data
## US Unemployment data by county
Economic Research Service
U.S. Department of Agriculture
link:
### Notes
- Year 2020, Median Household Income (2019), & '% of State Median HH Income ha... | github_jupyter |
```
import pandas as pd
import bs4 as bs
dfs=pd.read_html('https://en.wikipedia.org/wiki/Research_stations_in_Antarctica#List_of_research_stations')
dfr=pd.read_html('https://en.wikipedia.org/wiki/Antarctic_field_camps')
df=dfs[1][1:]
df.columns=dfs[1].loc[0].values
df.to_excel('bases.xlsx')
import requests
url='https:... | github_jupyter |
# Python Collections
* Lists
* Tuples
* Dictionaries
* Sets
## lists
```
x = 10
x = 20
x
x = [10, 20]
x
x = [10, 14.3, 'abc', True]
x
print(dir(x))
l1 = [1, 2, 3]
l2 = [4, 5, 6]
l1 + l2 # concat
l3 = [1, 2, 3, 4, 5, 6]
l3.append(7)
l3
l3.count(2)
l3.count(8)
len(l3)
sum(l3), max(l3), min(l3)
l1
l2
l_sum = [] # ... | github_jupyter |
# Generate and Perform Tiny Performances from the MDRNN
- Generates unconditioned and conditioned output from RoboJam's MDRNN
- Need to open `touchscreen_performance_receiver.pd` in [Pure Data](http://msp.ucsd.edu/software.html) to hear the sound of performances.
- To test generated performances, there need to be exam... | github_jupyter |
*Accompanying code examples of the book "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python" by [Sebastian Raschka](https://sebastianraschka.com). All code examples are released under the [MIT license](https://github.com/rasbt/deep-learning-book/blob/master/LICEN... | github_jupyter |
<a href="https://colab.research.google.com/github/modichirag/flowpm/blob/master/notebooks/flowpm_tutorial.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
%pylab inline
from flowpm import linear_field, lpt_init, nbody, cic_paint
import tensorflo... | github_jupyter |
# 1. 다변수 가우시안 정규분포MVN
$$\mathcal{N}(x ; \mu, \Sigma) = \dfrac{1}{(2\pi)^{D/2} |\Sigma|^{1/2}} \exp \left( -\dfrac{1}{2} (x-\mu)^T \Sigma^{-1} (x-\mu) \right)$$
- $\Sigma$ : 공분산 행렬, positive semidefinite
- x : 확률변수 벡터 $$x = \begin{bmatrix} x_1 \\ x_2 \\ \vdots \\ x_M \end{bmatrix}
$$
eg.
$\mu = \begin{bmatrix}2 \\ 3 \... | github_jupyter |
# First Graph Convolutional Neural Network
This notebook shows a simple GCN learning using the KrasHras dataset from [Zamora-Resendiz and Crivelli, 2019](https://www.biorxiv.org/content/10.1101/610444v1.full).
```
import gcn_prot
import torch
import torch.nn.functional as F
from os.path import join, pardir
from rando... | github_jupyter |
<a href="https://colab.research.google.com/github/DingLi23/s2search/blob/pipelining/pipelining/exp-cscv/exp-cscv_cscv_1w_ale_plotting.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
### Experiment Description
> This notebook is for experiment \<ex... | github_jupyter |
# SAT Analysis
**We wish to answer the question whether SAT is a fairt test?**
## Read in the data
```
import pandas as pd
import numpy as np
import re
data_files = [
"ap_2010.csv",
"class_size.csv",
"demographics.csv",
"graduation.csv",
"hs_directory.csv",
"sat_results.csv"
]
data = {}
fo... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Peeker-Groups" data-toc-modified-id="Peeker-Groups-1"><span class="toc-item-num">1 </span>Peeker Groups</a></span></li></ul></div>
# Peeker Groups
`Peeker` objects are normally stored in a glob... | github_jupyter |
# Multivariate Analysis for Planetary Atmospheres
This notebooks relies on the pickle dataframe in the `notebooks/` folder. You can also compute your own using `3_ColorColorFigs.ipynb`
```
#COLOR COLOR PACKAGE
from colorcolor import compute_colors as c
from colorcolor import stats
import matplotlib.pyplot as plt
imp... | github_jupyter |
```
from sklearn.cluster import MeanShift, estimate_bandwidth
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datetime
import math
import os
import sys
from numpy.fft import fft, ifft
import glob
def remove_periodic(X, df_index, detrending=True, model='additive', frequency_threshold=0.1e... | github_jupyter |
## loading an image
```
from PIL import Image
im = Image.open("lena.png")
```
## examine the file contents
```
from __future__ import print_function
print(im.format, im.size, im.mode)
```
- The *format* attribute identifies the source of an image. If the image was not read from a file, it is set to None.
- The *si... | github_jupyter |
```
import pandas as pd
import numpy as np
import scanpy as sc
import os
from sklearn.cluster import KMeans
from sklearn.cluster import AgglomerativeClustering
from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.metrics.cluster import adjusted_mutual_info_score
from sklearn.metrics.cluster import homog... | github_jupyter |
<h3>Sin Cython</h3>
<p>Este programa genera $N$ enteros aleatorios entre $1$ y $M$, y una vez obtenidos los eleva al cuadrado y devuelve la suma de los cuadrados. Por tanto, calcula el cuadrado de la longitud de un vector aleatorio con coordenadas enteros en el intervalo $[1,M]$.</p>
```
def cuadrados(N,M)... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
F = [] # 충청(현재 없음)
G = [] # 기타(현재 없음)
re... | github_jupyter |
## Exploratory data analysis of Dranse discharge data
Summary: The data is stationary even without differencing, but ACF and PACF plots show that an hourly first order difference and a periodic 24h first order difference is needed for SARIMA fitting.
Note: Final fitting done in Google Colab due to memory constraints ... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
from pymedphys_monomanage.tree import PackageTree
import networkx as nx
from copy import copy
package_tree = PackageTree('../../packages')
package_tree.package_dependencies_digraph
package_tree.roots
modules = list(package_tree.digraph.neighbors('pymedphys_analysis'))
modules
int... | github_jupyter |
##### M5_Idol_lyrics/SongTidy 폴더의 전처리 ipnb을 총정리하고, 잘못된 코드를 수정한 노트북
### 가사 데이터(song_tidy01) 전처리
**df = pd.read_csv('rawdata/song_data_raw_ver01.csv')**<br>
**!!!!!!!!!!!!!순서로 df(번호)로 지정!!!!!!!!!!!!!**
1. Data20180915/song_data_raw_ver01.csv 데이터로 시작함 (키스있는지체크)
- 제목에 리믹스,라이브,inst,영일중,ver 인 행
- 앨범에 나가수, 불명, 복면인 행
... | github_jupyter |
```
from linebot import LineBotApi
from linebot.exceptions import LineBotApiError
```
# 官方DEMO- Message Type :https://developers.line.me/en/docs/messaging-api/message-types/
# Doc : https://github.com/line/line-bot-sdk-python/blob/master/linebot/models/send_messages.py
```
CHANNEL_ACCESS_TOKEN = "YOUR CHANNEL TOKEN"... | github_jupyter |
```
import pandas as pd
import numpy as np
from pathlib import Path
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
from sklearn.feature_extraction import DictVectorizer
from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.inspection imp... | github_jupyter |
# Heikin-Ashi PSAR Strategy
_Roshan Mahes_
In this tutorial, we implement the so-called _Parabolic Stop and Reverse (PSAR)_ strategy. Given any stock, currency or commodity, this indicator tells us whether to buy or sell the stock at any given time. The momentum strategy is based on the open, high, low and close price... | github_jupyter |
## 1. Introduction to pyLHD
pyLHD is a python implementation of the R package [LHD](https://cran.r-project.org/web/packages/LHD/index.html) by Hongzhi Wang, Qian Xiao, Abhyuday Mandal. As of now, only the algebraic construction of Latin hypercube designs (LHD) are implemented in this package. For search algorithms t... | github_jupyter |
# NEXUS tool: case study for the Souss-Massa basin - energy demand calculations
In this notebook a case study for the Souss-Massa basin is covered using the `nexustool` package. The water requirements for agricultural irrigation and domestic use were previously calculated using the Water Evaluation and Planning System... | github_jupyter |
Interactive analysis with python
--------------------------------
Before starting this tutorial, ensure that you have set up _tangos_ [as described here](https://pynbody.github.io/tangos/) and the data sources [as described here](https://pynbody.github.io/tangos/data_exploration.html).
We get started by importing the... | github_jupyter |
n=b
```
# Binary representation ---> Microsoft
# Difficulty: School Marks: 0
'''
Write a program to print Binary representation of a given number N.
Input:
The first line of input contains an integer T, denoting the number of test cases. Each test case contains an integer N.
Output:
For each test case, print the b... | github_jupyter |
# Generative models - variational auto-encoders
### Author: Philippe Esling (esling@ircam.fr)
In this course we will cover
1. A [quick recap](#recap) on simple probability concepts (and in TensorFlow)
2. A formal introduction to [Variational Auto-Encoders](#vae) (VAEs)
3. An explanation of the [implementation](#imple... | github_jupyter |
```
!wget https://datahack-prod.s3.amazonaws.com/train_file/train_LZdllcl.csv -O train.csv
!wget https://datahack-prod.s3.amazonaws.com/test_file/test_2umaH9m.csv -O test.csv
!wget https://datahack-prod.s3.amazonaws.com/sample_submission/sample_submission_M0L0uXE.csv -O sample_submission.csv
# Import the required packa... | github_jupyter |
[View in Colaboratory](https://colab.research.google.com/github/3catz/DeepLearning-NLP/blob/master/Time_Series_Forecasting_with_EMD_and_Fully_Convolutional_Neural_Networks_on_the_IRX_data_set.ipynb)
# TIME SERIES FORECASTING -- using Empirical Mode Decomposition with Fully Convolutional Networks for One-step ahead for... | github_jupyter |
<a href="https://colab.research.google.com/github/unicamp-dl/IA025_2022S1/blob/main/ex07/Guilherme_Pereira/Aula_7_Guilherme_Pereira.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
nome = 'Guilherme Pereira'
print(f'Meu nome é {nome}')
```
# Ex... | github_jupyter |
# Network Training
## Includes
```
# mass includes
import os, sys, warnings
import ipdb
import torch as t
import torchnet as tnt
from tqdm.notebook import tqdm
# add paths for all sub-folders
paths = [root for root, dirs, files in os.walk('.')]
for item in paths:
sys.path.append(item)
from ipynb.fs.full.config ... | github_jupyter |
```
from __future__ import print_function
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
%matplotlib inline
#Importamos nuestros módulos y clases necesarias
import Image_Classifier as img_clf
import Labeled_Image as li
import classifiers as clfs
from skimage import i... | github_jupyter |
# COMP90051 Workshop 3
## Logistic regression
***
In this workshop we'll be implementing L2-regularised logistic regression using `scipy` and `numpy`.
Our key objectives are:
* to become familiar with the optimisation problem that sits behind L2-regularised logistic regression;
* to apply polynomial basis expansion a... | github_jupyter |
```
import numpy as np
import pandas as pd
from sklearn import *
import warnings; warnings.filterwarnings("ignore")
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
train.shape, test.shape, sub.shape
```
Wordplay in Column Names
====... | github_jupyter |
# Accessing the Trigger
In ATLAS all access to event trigger decision is via the Trigger Decision Tool (TDT). There is quite a bit of information attached to the trigger, and its layout is quite complex - for that reason one should use the TDT to access the data. It is not really possible for a human to navigate the d... | github_jupyter |
# Additive Secret Sharing
Author:
- Carlos Salgado - [email](mailto:csalgado@uwo.ca) - [linkedin](https://www.linkedin.com/in/eng-socd/) - [github](https://github.com/socd06)
## Additive Secret Sharing
Additive Secret Sharing is a mechanism to share data among parties and to perform computation on it.
.
## Setup
```
import numpy as np
from scipy.integrate import odeint
import pysindy as ps
```
Let's generate some training data from the [Lorenz system](https://e... | github_jupyter |
# An Introduction to Natural Language in Python using spaCy
## Introduction
This tutorial provides a brief introduction to working with natural language (sometimes called "text analytics") in Pytho, using [spaCy](https://spacy.io/) and related libraries.
Data science teams in industry must work with lots of text, one... | github_jupyter |
```
%load_ext watermark
%watermark -d -u -a 'Andreas Mueller, Kyle Kastner, Sebastian Raschka' -v -p numpy,scipy,matplotlib,scikit-learn
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
```
# SciPy 2016 Scikit-learn Tutorial
# In Depth - Support Vector Machines
SVM stands for "support vector m... | github_jupyter |
# Think Bayes
This notebook presents example code and exercise solutions for Think Bayes.
Copyright 2018 Allen B. Downey
MIT License: https://opensource.org/licenses/MIT
```
# Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignmen... | github_jupyter |
<img width="10%" alt="Naas" src="https://landen.imgix.net/jtci2pxwjczr/assets/5ice39g4.png?w=160"/>
# YahooFinance - Get Stock Update
<a href="https://app.naas.ai/user-redirect/naas/downloader?url=https://raw.githubusercontent.com/jupyter-naas/awesome-notebooks/master/YahooFinance/YahooFinance_Get_Stock_Update.ipynb" ... | github_jupyter |
Before we begin, let's execute the cell below to display information about the CUDA driver and GPUs running on the server by running the `nvidia-smi` command. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar ... | github_jupyter |
# Recruitment Across Datasets
In this notebook, we further examine the capability of ODIF to transfer across datasets, building upon the prior FTE/BTE experiments on MNIST and Fashion-MNIST. Using the datasets found in [this repo](https://github.com/neurodata/LLF_tidy_images), we perform a series of experiments to eva... | github_jupyter |
$\newcommand{\xv}{\mathbf{x}}
\newcommand{\wv}{\mathbf{w}}
\newcommand{\Chi}{\mathcal{X}}
\newcommand{\R}{\rm I\!R}
\newcommand{\sign}{\text{sign}}
\newcommand{\Tm}{\mathbf{T}}
\newcommand{\Xm}{\mathbf{X}}
\newcommand{\Im}{\mathbf{I}}
\newcommand{\Ym}{\mathbf{Y}}
$
### ITCS8010
# G_np Simulation Experiment
I... | github_jupyter |
## Classify Radio Signals from Space using Keras
In this experiment, we attempt to classify radio signals from space.
Dataset has been provided by SETI. Details can be found here:
https://github.com/setiQuest/ML4SETI/blob/master/tutorials/Step_1_Get_Data.ipynb
## Import necessary libraries
```
import pandas as pd
i... | github_jupyter |
### building a dask array without knowing sizes
#### from dask.dataframe
```
from dask import array as da, dataframe as ddf, delayed, compute
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
da.from_delayed
def get_chunk_df(array_size,n_cols):
col_names = [f"col_{i}" for i in range(n_cols)]... | github_jupyter |
## Linear Regression with PyTorch
#### Part 2 of "PyTorch: Zero to GANs"
*This post is the second in a series of tutorials on building deep learning models with PyTorch, an open source neural networks library developed and maintained by Facebook. Check out the full series:*
1. [PyTorch Basics: Tensors & Gradients](... | github_jupyter |
# Spin-polarized calculations with BigDFT
The goal of this notebook is to explain how to do a spin-polarized calculation with BigDFT (`nspin=2`).
We start with the molecule O$_2$ and a non-spin polarized calculation, which is the code default.
To do that we only have to specify the atomic positions of the molecule.
`... | github_jupyter |
<a href="https://colab.research.google.com/github/GoogleCloudPlatform/training-data-analyst/blob/master/courses/fast-and-lean-data-science/01_MNIST_TPU_Keras.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## MNIST on TPU (Tensor Processing Unit)<br... | github_jupyter |
```
import numpy as np
import pandas as pd
df_can = pd.read_excel('https://ibm.box.com/shared/static/lw190pt9zpy5bd1ptyg2aw15awomz9pu.xlsx',
sheet_name='Canada by Citizenship',
skiprows=range(20),
skip_footer=2
)
print('Data dow... | github_jupyter |
```
import numpy as np
import pandas as pd
from pathlib import Path
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from tqdm import tqdm
from scipy.spatial.distance import cdist
from sklearn.metrics import roc_curve, roc_auc_score
timings = Path('timings/')
raw_data = Path('surface_data/raw/protein_s... | github_jupyter |
## Student Activity on Advanced Data Structure
In this activity we will have to do the following tasks
- Look up the definition of permutations, and dropwhile from [itertools documentation](https://docs.python.org/3/library/itertools.html) in Python
- Using permutations generate all possible three digit numbers that ... | github_jupyter |
---
**Universidad de Costa Rica** | Escuela de Ingeniería Eléctrica
*IE0405 - Modelos Probabilísticos de Señales y Sistemas*
### `PyX` - Serie de tutoriales de Python para el análisis de datos
# `Py5` - *Curvas de ajuste de datos*
> Los modelos para describir un fenómeno y sus parámetros pueden obtenerse a partir... | github_jupyter |
# This task is not quite ready as we don't have an open source route for simulating geometry that requires imprinting and merging. However this simulation can be carried out using Trelis.
# Heating Mesh Tally on CAD geometry made from Components
This constructs a reactor geometry from 3 Component objects each made fr... | github_jupyter |
# MDT Validation Notebook
Validated on Synthea +MDT population vs MEPS for Pediatric Asthma
```
import pandas as pd
import datetime as dt
import numpy as np
from scipy.stats import chi2_contingency
```
# Grab medication RXCUI of interest
Grabs the MEPS product RXCUI lists for filtering of Synthea to medicati... | github_jupyter |
```
import numpy as np
import pandas as pd
from tqdm import tqdm_notebook, tqdm
from scipy.spatial.distance import jaccard
from surprise import Dataset, Reader, KNNBasic, KNNWithMeans, SVD, SVDpp, accuracy
from surprise.model_selection import KFold, train_test_split, cross_validate, GridSearchCV
import warnings
warni... | github_jupyter |
<a href="https://colab.research.google.com/github/ArpitaChatterjee/Comedian-transcript-Analysis/blob/main/Exploratory_Data_Analysis.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#To find the pattern of each comedian and find the reason of the lika... | github_jupyter |
# Going deeper with Tensorflow
В этом семинаре мы начнем изучать [Tensorflow](https://www.tensorflow.org/) для построения deep learning моделей.
Для установки tf на свою машину
* `pip install tensorflow` версия с поддержкой **cpu-only** для Linux & Mac OS
* для автомагической поддержки GPU смотрите документацию [TF ... | github_jupyter |
<link rel="stylesheet" href="../../styles/theme_style.css">
<!--link rel="stylesheet" href="../../styles/header_style.css"-->
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">
<table width="100%">
<tr>
<td id="image_td" width="15%" class="head... | github_jupyter |
```
import pandas as pd
import os
import time
import re
import numpy as np
import json
from urllib.parse import urlparse, urljoin
run_root = "/home/icejm/Code/OpenWPM/stockdp/page_ana/"
# gather all potent/black links
count = 0
for root, dirs, files in os.walk(os.path.abspath('.')):
if len(dirs)==0:
for i i... | github_jupyter |
# CSAILVision semantic segmention models
This is a semantic segmentation notebook using an [ADE20K](http://groups.csail.mit.edu/vision/datasets/ADE20K/) pretrained model from the open source project [CSAILVision/semantic-segmentation-pytorch](https://github.com/CSAILVision/semantic-segmentation-pytorch).
For other de... | github_jupyter |
```
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from tqdm import tqdm
%matplotlib inline
from torch.utils.data import Dataset, DataLoader
import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
from torch.nn import functional as F
device = torch.device("cuda" i... | github_jupyter |
# Bayesian Camera Calibration
> Let's apply Bayesian analysis to calibrate a camera
- toc: true
- badges: true
- comments: true
- categories: [Bayesian, Computer Vision]
- image: images/2020-03-28-Bayesian-Camera-Calibration/header.jpg
```
import numpy as np
import matplotlib.pyplot as plt
import pymc3 as pm
plt.rc... | github_jupyter |
# How to read data from varius file formats
some of the most basic things noone ever treaches you is how to actually access your data in various formats. This notebook shows a couple of examples on how to read data from a number of sources. Feel free to edit this notebook with more methods that you have worked with.
... | github_jupyter |
```
from itertools import combinations
import qiskit
import numpy as np
import tqix
import sys
def generate_u_pauli(num_qubits):
lis = [0, 1, 2]
coms = []
if num_qubits == 2:
for i in lis:
for j in lis:
coms.append([i, j])
if num_qubits == 3:
for i in lis:
... | github_jupyter |
# Simulate Artificial Physiological Signals
Neurokit's core signal processing functions surround electrocardiogram (ECG), respiratory (RSP), electrodermal activity (EDA), and electromyography (EMG) data. Hence, this example shows how to use Neurokit to simulate these physiological signals with customized parametric co... | github_jupyter |
<a href="https://colab.research.google.com/github/cstorm125/abtestoo/blob/master/notebooks/frequentist_colab.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# A/B Testing from Scratch: Frequentist Approach
Frequentist A/B testing is one of the most... | github_jupyter |
## CS536: Perceptrons
#### Done by - Vedant Choudhary, vc389
In the usual way, we need data that we can fit and analyze using perceptrons. Consider generating data points (X, Y) in the following way:
- For $i = 1,....,k-1$, let $X_i ~ N(0, 1)$ (i.e. each $X_i$ is an i.i.d. standard normal)
- For $i = k$, generate $X_k$... | github_jupyter |
# 5章 線形回帰
```
# 必要ライブラリの導入
!pip install japanize_matplotlib | tail -n 1
!pip install torchviz | tail -n 1
!pip install torchinfo | tail -n 1
# 必要ライブラリのインポート
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib
from IPython.display import display
import torch
import torch.n... | github_jupyter |
# XGBoost vs LightGBM
In this notebook we collect the results from all the experiments and reports the comparative difference between XGBoost and LightGBM
```
import matplotlib.pyplot as plt
import nbformat
import json
from toolz import pipe, juxt
import pandas as pd
import seaborn
from toolz import curry
from bokeh... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W3D3_NetworkCausality/W3D3_Tutorial3.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Neuromatch Academy 2020 -- Week 3 Day 3 Tutorial 3
# Caus... | github_jupyter |
```
# Setup Sets
cities = ["C1", "C2", "C3", "C4", "C5", "C6", "C7", "C8", "C9"]
power_plants = ["P1", "P2", "P3", "P4", "P5", "P6"]
connections = [("C1", "P1"), ("C1", "P3"), ("C1","P5"), \
("C2", "P1"), ("C2", "P2"), ("C2","P4"), \
("C3", "P2"), ("C3", "P3"), ("C3","P4"), \
... | github_jupyter |
```
import pandas as pd
disp_url = 'https://raw.githubusercontent.com/PacktWorkshops/The-Data-Science-Workshop/master/Chapter12/Dataset/disp.csv'
trans_url = 'https://raw.githubusercontent.com/PacktWorkshops/The-Data-Science-Workshop/master/Chapter12/Dataset/trans.csv'
account_url = 'https://raw.githubusercontent.com/P... | github_jupyter |
```
import os
import sys
import time
import matplotlib.pyplot as plt
import numpy as np
import GCode
import GRBL
# Flip a 2D array. Effectively reversing the path.
flip2 = np.array([
[0, 1],
[1, 0],
])
flip2
# Flip a 2x3 array. Effectively reversing the path.
flip3 = np.array([
[0, 0, 1],
[0, 1, 0],
... | github_jupyter |
```
import pandas as pd
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
raw_data = pd.read_excel("hydrogen_test_classification.xlsx")
raw_data.head()
# 分开特征值和标签值
X = raw_data.drop("TRUE VALUE", axis=1).copy()
y = raw_data["TRUE VALUE"]
y.unique()
from sklearn.model_selection import train_test... | github_jupyter |
# Supervised Learning
Supervised learning consists in learning the link between two datasets: the observed data X and an external variable y that we are trying to predict, usually called “target” or “labels”. Most often, y is a 1D array of length n_samples.
If the prediction task is to classify the observations in a ... | github_jupyter |
# Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and... | github_jupyter |
```
# Author: Robert Guthrie
from copy import copy
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.optim as optim
torch.manual_seed(1)
def argmax(vec):
# return the argmax as a python int
_, idx = torch.max(vec, 1)
return idx.item()
def prepare_sequence(seq, to_ix):
... | github_jupyter |
Lambda School Data Science
*Unit 2, Sprint 1, Module 3*
---
```
%%capture
import sys
# If you're on Colab:
if 'google.colab' in sys.modules:
DATA_PATH = 'https://raw.githubusercontent.com/LambdaSchool/DS-Unit-2-Applied-Modeling/master/data/'
!pip install category_encoders==2.*
# If you're working locally:
... | github_jupyter |
# Simple ARIMAX
This code template is for Time Series Analysis and Forecasting to make scientific predictions based on historical time stamped data with the help of ARIMAX algorithm
### Required Packages
```
import warnings
import numpy as np
import pandas as pd
import seaborn as se
import matplotlib.pyplot a... | github_jupyter |
# Plotting with matplotlib
### Setup
```
%matplotlib inline
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 10)
pd.set_option('display.max_rows', 10)
```
### Getting the pop2019 DataFrame
```
csv ='../csvs/nc-est2019-agesex-res.csv'
pops = pd.read_csv(csv, usecols=['SEX', 'AGE', 'POPEST... | github_jupyter |
```
import string
import random
from deap import base, creator, tools
## Create a Finess base class which is to be minimized
# weights is a tuple -sign tells to minimize, +1 to maximize
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
```
This will define a class ```FitnessMax``` which inherits the Fitness... | github_jupyter |
# 06_Business_Insights
In this section, we will expend upon the features used by the model and attempt to explain its significance as well as contributions to the pricing model.
Accordingly, in Section Four, we identified the following key features that that are strong predictors of housing price based upon a combina... | github_jupyter |
# Bulk RNA-seq eQTL analysis
This notebook provide a command generator on the XQTL workflow so it can automate the work for data preprocessing and association testing on multiple data collection as proposed.
```
%preview ../images/eqtl_command.png
```
This master control notebook is mainly to serve the 8 tissues snu... | github_jupyter |
```
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import plotly.plotly as py
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import plotly.graph_objs as go
init_notebook_mode(connected=True)
%matplotlib inline
data_folder = r'C:\Users\ocni\PycharmProjects... | github_jupyter |
# Artificial Intelligence Nanodegree
## Convolutional Neural Networks
---
In this notebook, we visualize four activation maps in a CNN layer.
### 1. Import the Image
```
import cv2
import scipy.misc
import matplotlib.pyplot as plt
%matplotlib inline
# TODO: Feel free to try out your own images here by changing i... | github_jupyter |
# Part - 2: COVID-19 Time Series Analysis and Prediction using ML.Net framework
## COVID-19
- As per [Wiki](https://en.wikipedia.org/wiki/Coronavirus_disease_2019) **Coronavirus disease 2019** (**COVID-19**) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease wa... | github_jupyter |
[](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/jupyter/transformers/HuggingFace%20in%20Spark%20NLP%20-%20RoBertaForTokenClassification.ipynb)
## Import RoBertaForTokenClassification models from HuggingFac... | github_jupyter |
# Section 2.1 `xarray`, `az.InferenceData`, and NetCDF for Markov Chain Monte Carlo
_How do we generate, store, and save Markov chain Monte Carlo results_
```
import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
import arviz as az
import pystan
import xarray as xr
from IP... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W1D1_ModelTypes/student/W1D1_Tutorial1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Tutorial 1: "What" models
**Week 1, Day 1: Model Types*... | github_jupyter |
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