text stringlengths 2.5k 6.39M | kind stringclasses 3
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```
import torch
import time
import numpy as np
import sigkernel
import matplotlib.pyplot as plt
dyadic_order = 5
_naive_solver = False
# specify static kernel
static_kernel = sigkernel.LinearKernel()
# static_kernel = sigkernel.RBFKernel(sigma=.5)
# initialize signature kernel
signature_kernel = sigkernel.SigKernel... | github_jupyter |
## Visualizing of EcoFOCI Glider Locations from Science Data Set - single profiles
```
%matplotlib inline
import os
import xarray as xa
import numpy as np
import cmocean
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
```
Using profile 262 which was corrected for the 0.5 threshold but not any othe... | github_jupyter |
## _*H2 dissociation curve using VQE with UCCSD*_
This notebook demonstrates using QISKit ACQUA Chemistry to plot graphs of the ground state energy of the Hydrogen (H2) molecule over a range of inter-atomic distances using VQE and UCCSD. It is compared to the same energies as computed by the ExactEigensolver
This not... | github_jupyter |
```
%matplotlib inline
import gym
import itertools
import matplotlib
import numpy as np
import pandas as pd
import sys
if "../" not in sys.path:
sys.path.append("../")
from collections import defaultdict
from lib.envs.windy_gridworld import WindyGridworldEnv
from lib import plotting
matplotlib.style.use('ggplot'... | github_jupyter |
# K-Neirest-Neighbors of star
Define path where stars files are located, and save their filenames in array.
```
from os import listdir
from os.path import isfile, join
import time
from astropy.coordinates import SkyCoord
from astropy import units as u
from astropy.coordinates import Angle
import numpy as np
import pa... | github_jupyter |
# JSON and Library Catalog Data
How can we extract data from APIs (machine-readable online data sources)?
In this lesson, we look at how we can use data from the web.
We will use real-world data from
[The National Library of Norway](https://www.nb.no/).
The National Library has a [search API](https://api.nb.no/)
whic... | github_jupyter |
```
ReloadProject('deep_learning')
```
## Environment Setup
Let's assume a world with 11 states: 0-10. Each time the agent and move +1 or -1, with 0-1 -> 10 and 10+1 -> 0. All actions that gets the agent closer to state "5" gets reward +1, otherwise gets reward -1.
```
STATE_ZERO_ARRAY = np.zeros(1, dtype=int)
TARGET... | github_jupyter |
# Imports
```
import torch
from torch.autograd import Variable
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.insert(0, "lib/")
from data.coco_dataset import CocoDataset
from utils.preprocess_sample import preprocess_sample
from utils.collate_custom imp... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Image/spectral_unmixing.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" href... | github_jupyter |
# Story
A program trying to optimaize a portfolio using mean-variance with objectives of Max Sharpe Ratio, Global Min Volatility, min risk given return, max return given vol.
```
!pip install pandas_datareader
!pip install yfinance
import pandas as pd
import numpy as np
from datetime import datetime
#from pandas_datar... | github_jupyter |
# **DISTIL Usage Example: MedMNIST**
Here, we show how to use DISTIL to perform active learning on image classification tasks (MedMNIST's OrganAMNIST). This notebook can be easily executed on Google Colab.
## Installation and Imports
```
# Get DISTIL
!git clone https://github.com/decile-team/distil.git
!pip install ... | github_jupyter |
# Exploring the bulldozer data set
Let's explore another Kaggle data set [Blue Book for Bulldozers](https://www.kaggle.com/c/bluebook-for-bulldozers/data). This one is more challenging because it has lots of missing values and there are more opportunities to extract information and cleanup various columns. There ar... | github_jupyter |
# Deploy machine learning models to Azure
description: (preview) deploy your machine learning or deep learning model as a web service in the Azure cloud.
## Connect to your workspace
```
from azureml.core import Workspace
ws = Workspace.from_config()
ws
```
## Register your model
A registered model is a logical c... | github_jupyter |
**This notebook is an exercise in the [Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning) course. You can reference the tutorial at [this link](https://www.kaggle.com/alexisbcook/categorical-variables).**
---
By encoding **categorical variables**, you'll obtain your best resul... | github_jupyter |
<a href="https://colab.research.google.com/github/100rab-S/TensorFlow-Advanced-Techniques/blob/main/C3W1_L1_transfer_learning_cats_dogs.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Basic transfer learning with cats and dogs data
### Import ten... | github_jupyter |
# Exercise 17 - Structured Data using RNNs
## Setup GPU & TensorFlow
```
# Choose to the GPU number you want to use,
# otherwise you will get a Python error
# e.g. USE_GPU = 4
USE_GPU = 4
# Import TensorFlow
import tensorflow as tf
# Print the installed TensorFlow version
print(f'TensorFlow version: {tf.__version__... | github_jupyter |
This experiments on MNIST by creating multiple views of the dataset.
As the paper deals with binary classification, we use digits 0 to 4 in class 0 and 5 to 9 in class 1
```
import pandas as pd
import numpy as np
from scipy.signal import convolve2d
from scipy.fft import ifftn
from sklearn.model_selection import cro... | github_jupyter |
<!--NAVIGATION-->
< [Setting Up](01.01 Setting Up.ipynb) | [Contents](Index.ipynb) | [Algorithmic Trading Architecture](01.03 Algorithmic Trading Architecture.ipynb) >
# Understanding the Documentations
In order to make use of the API effective, we need to understand the input parameters and the corresponding output.... | github_jupyter |
<img src="../static/aeropython_name_mini.png" alt="AeroPython" style="width: 300px;"/>
# Clase 2b: Visualización con matplotlib
_Después de estudiar la sintaxis de Python y empezar a manejar datos numéricos de manera un poco más profesional, ha llegado el momento de visualizarlos. Con la biblioteca **matplotlib** pod... | github_jupyter |
# Use BlackJAX with TFP
BlackJAX can take any log-probability function as long as it is compatible with JAX's JIT. In this notebook we show how we can use tensorflow-probability as a modeling language and BlackJAX as an inference library.
We reproduce the Eight Schools example from the [TFP documentation](https://www... | github_jupyter |
```
trainingSet = [
(1, "Chinese Beijing Chinese", "yes"),
(2, "Chinese Chinese Shanghai", "yes"),
(3, "Chinese Macao", "yes"),
(4, "Tokyo Japan Chinese", "no")
]
testSet = [
(5, "Chinese Chinese Chinese Tokyo Japan")
]
traningSetYes = [d for d in trainingSet if d[2] == "yes"]
traningSetNo = [d for... | github_jupyter |
# Average Directional Index (ADX)
https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:average_directional_index_adx
Average Directional Index (ADX) is technical indicator; as a result, the values range from 0 to 100. The ADX gives a signal of trend strength.
If ADX is below 20, the trend is ... | github_jupyter |
# How to generate rates of elevation change and time series from ICESat-2
ICESat-2 Hackweek 2020\
Johan Nilsson\
Jet Propulsion Laboratory\
2020-06-16
In this tutorial we will present an easy processing flow to generate gridded altimetry time series of elevation change based on the captoolkit software. We will then u... | github_jupyter |
```
import math
import numpy as np
import pandas as pd
from datetime import datetime
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('seaborn-whitegrid')
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import classification_report
from sklearn.metrics impor... | github_jupyter |
```
import numpy as np
import pandas as pd
import scipy
from datetime import datetime, timedelta
import sys
## For SDK
import getpass
from odp_sdk import ODPClient
from getpass import getpass
sys.path.append('/Users/tarabaris/GitHub/odp-sdk-python/Examples')
from UtilityFunctions import *
import warnings
warnings.filte... | github_jupyter |
<a href="https://colab.research.google.com/github/Sergeydigl3/pepe-nude-colab/blob/master/DeepNude_(PepeNude%2C_DreamTime)_Google_Colab.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# INIT
```
#@title Clonong repo and install requirements
!git cl... | github_jupyter |
This is essentially a copy of https://github.com/rmeinl/apricot-julia/blob/5f130f846f8b7f93bb4429e2b182f0765a61035c/notebooks/python_reimpl.ipynb
```
import matplotlib.pyplot as plt
import seaborn; seaborn.set_style('whitegrid')
import time
import scipy
import numpy as np
from numba import njit
from numba import pra... | github_jupyter |
<a href="https://hub.callysto.ca/jupyter/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fcallysto%2Fcallysto-sample-notebooks&branch=master&subPath=notebooks/Science/Investigating_el... | github_jupyter |
# Making Models Smaller via Knowledge Distillation
### A text classification example using Hugging Face Transformers and Amazon SageMaker
Welcome to our end-to-end example of _knowledge distillation_ using Hugging Face Transformers & Amazon SageMaker! This example is adapted from Chapter 8 of the O'Reilly book [_Natu... | github_jupyter |
## Emoji prediction for weibo tweets
This is the final project for Nanjing University Data Mining class in spring 2019
```
import pandas as pd
import numpy as np
```
Load the raw weibo tweets (train.text) and preprocessed label (train.label). Save them into two lists respectively.
```
DATA_FOLDER='data/'
TRAIN_TEXT=... | github_jupyter |
# CBOE VXXLE Index
In this notebook, we'll take a look at the CBOE VXXLE Index dataset, available on the [Quantopian Store](https://www.quantopian.com/store). This dataset spans 16 Mar 2011 through the current day. This data has a daily frequency. VXXLE is the CBOE Energy Sector ETF Volatility Index, reflecting the i... | github_jupyter |
```
import os
os.chdir("../")
import pandas as pd
import seaborn
import sklearn
import geopandas as gpd
import missingno as msno
import seaborn as sns
from preprocessing.preprocessing import standardize_education_level, standardize_date
from datetime import datetime
import numpy as np
from pathlib import Path
aggregate... | github_jupyter |
```
import pandas as pd
import numpy as np
series = pd.Series({'Col1':[0,1,2,3,4,5]})
```
# Pandas
Pandas is an extremely useful library for handeling and manipulating tabular data and arrays.
It is largely considered to be one of the most essential libraries in a Pythonic Data Scientist's tool kit but it has a myri... | github_jupyter |
```
###########################################################################################################################
# Hassan Shahzad
# 18i-0441
# CS-D
# FAST-NUCES ISB
# chhxnshah@gmail.com
# "Predicting heart disease using ... | github_jupyter |
# Posterior Predictive Checks in PyMC3
PPCs are a great way to validate a model. The idea is to generate data sets from the model using parameter settings from draws from the posterior.
`PyMC3` has random number support thanks to [Mark Wibrow](https://github.com/mwibrow) as implemented in [PR784](https://github.com/p... | github_jupyter |
Sebastian Raschka, 2015
# Python Machine Learning
# Chapter 12 - Training Artificial Neural Networks for Image Recognition
Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s).
```
%load_ext watermark
%w... | github_jupyter |
__Before using the codes below, notation:__
1. for the mixture component, using a sliced-like list.
<p>eg: V_water:V_toluene:V_ethanol = [::] </p>
# Retrieve the wavelength for the tie-line
## Import packages and set initial parameters for the graphs
```
from dataGadgets import *
plt.rcParams['figure.figsize'] = [... | github_jupyter |
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID";
os.environ["CUDA_VISIBLE_DEVICES"]="0";
```
*ktrain* uses TensorFlow 2. To support sequence-tagging, *ktrain* also currently uses the CRF module from `keras_contrib`, which is not yet fully compatible w... | github_jupyter |
# Performance Overview
Here, we will example the performance of FNGS as a function of time on several datasets. These investigations were performed on a 4 core machine (4 threads) with a 4.0 GhZ processor.
# BNU1
```
%matplotlib inline
import numpy as np
import re
import matplotlib.pyplot as plt
def memory_function... | github_jupyter |
[notebook_Readme.md on github](https://github.com/KnowEnG/Spreadsheets_Transformation/blob/master/docs/notebook_Readme.md)
```
%%html
<style>div.input {display:none;} div.output_stderr{display:none}</style>
""" To Start This Notebook Click On: Cell > Run All (in the jupyter menu above) """
import warnings
... | github_jupyter |
```
import pandas as pd
import numpy as np
pd.set_option("display.max_rows",30)
%matplotlib inline
class dataset:
kdd_train_2labels = pd.read_pickle("dataset/kdd_train_2labels_20percent.pkl")
kdd_train_2labels_y = pd.read_pickle("dataset/kdd_train_2labels_y_20percent.pkl")
kdd_test_2labels = pd.read_pi... | github_jupyter |
# 数据采样
`Ascend` `GPU` `CPU` `数据准备`
[](https://authoring-modelarts-cnnorth4.huaweicloud.com/console/lab?share-url-b64=aHR0cHM6Ly9taW5kc3BvcmUtd2Vic2l0ZS5vYnMuY24tbm9ydGgtNC5teWh1YXdlaWNsb3VkLmNvbS9ub3RlYm9vay9tb2RlbGFydHMvcHJvZ3Jhb... | github_jupyter |
# Analyzing CIA World Factbook with SQL
SQL is the short for Structured Query Language. It is a domain-specific language used in programming and designed for managing data held in a relational database management systems. According to [Wikipedia](https://en.wikipedia.org/wiki/SQL), it is particularly useful in handlin... | github_jupyter |
#Summerizing Text with T5
Copyright 2021, Denis Rothman. MIT License. Hugging Face usage example was modified for educational purposes.
[Hugging Face Models](https://huggingface.co/transformers/model_doc/t5.html)
[Hugging Face Framework Usage](https://huggingface.co/transformers/usage.html)
```
!pip install transfor... | github_jupyter |
# 3. 第三章 - 尝试解决一个实际问题
目录：
* 3.1 加载 MNIST 数据集
* 3.2 定义模型
* 3.3 选择损失函数和优化器
* 3.4 模型训练并验证
* 3.5 可视化验证
现在，让我们依靠计图的强大力量，解决你的第一个实际问题吧！
```
# 加载计图
import jittor as jt
# 开启 GPU 加速
jt.flags.use_cuda = 1
```
## 任务：使用 Jittor 对 MNIST 手写数字进行识别
**任务描述如下：**
* MNIST 手写数字数据库，主要收集了不同人群真实的手写数字记录（包括 0 到 9 十个数字）。该数据库包含训练集上 60,000 个... | github_jupyter |
# Recurrent Neural Networks in Theano
Credits: Forked from [summerschool2015](https://github.com/mila-udem/summerschool2015) by mila-udem
First, we import some dependencies:
```
%matplotlib inline
from synthetic import mackey_glass
import matplotlib.pyplot as plt
import theano
import theano.tensor as T
import numpy
... | github_jupyter |
# Building a non-linear gravity inversion from scratch (almost)
In this notebook, we'll build a non-linear gravity inversion to estimate the relief of a sedimentary basin. We'll implement smoothness regularization and see its effects on the solution. We'll also see how we can break the inversion by adding random noise... | github_jupyter |
# BinaryClassExoplanets
***Matt Paterson, hello@hireMattPaterson.com***<br>
This notebook takes a dataset from the Kepler Satelite, KOI cumulative dataset, from https://exoplanetarchive.ipac.caltech.edu/cgi-bin/TblView/nph-tblView?app=ExoTbls&config=cumulative <br>
Here I do a rudimentary check on some exoplanet data,... | github_jupyter |
```
# Real life data
import logging
import threading
import itertools
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import axes3d
import seaborn as seabornInstance
from sqlalchemy import Column, Integer, String, Float, DateTime, Boolean, ... | github_jupyter |
# Supercompressible (7d)
L. F. Pereira (lfpereira@fe.up.pt)\
September 22, 2020
This notebook creates the **design of experiments** and the metadata required to run the **numerical simulations**.
**Note**: the parametric `Abaqus` scripts were created using `f3dasm` implementations. You can also create your own func... | github_jupyter |
```
%run -i ../python/common.py
UC_SKIPTERMS=True
%run -i ../python/ln_preamble.py
```
# UC-SLS Lecture 20 : Using LibC to access the OS and escape the confines our process
- Preliminaries
- libraries
- Standard library : `libc.a[.so]`
- Address Space management:
- dynamic memory for data items: `malloc` and `fr... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
from IPython.display import Image
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
import os
import json
import numpy as onp
import jax
import pickle
import matplotlib.pyplot as plt
import pandas as pd
from time... | github_jupyter |
```
#IMPORT SEMUA LIBRARY DISINI
#IMPORT LIBRARY PANDAS
import pandas as pd
#IMPORT LIBRARY POSTGRESQL
import psycopg2
from psycopg2.extensions import ISOLATION_LEVEL_AUTOCOMMIT
#IMPORT LIBRARY CHART
from matplotlib import pyplot as plt
from matplotlib import style
#IMPORT LIBRARY PDF
from fpdf import FPDF
#IMPORT LIBR... | github_jupyter |
# Diagrammatic Differentiation
**for Quantum Machine Learning**
[arXiv:2103.07960](https://arxiv.org/abs/2103.07960)
_Alexis Toumi_$^{\dagger\star}$,
Richie Yeung$^\star$,
Giovanni de Felice$^{\dagger\star}$
$^\dagger$ University of Oxford
$\quad^\star$ Cambridge Quantum Computing Ltd.
**Contents:**
1. Dual number... | github_jupyter |
lets check the target class densities for the DR9SV imaging
```
import os
import fitsio
import numpy as np
# -- desitarget --
from desitarget.sv1.sv1_targetmask import bgs_mask
# -- plotting --
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rcParams['text.usetex'] = True
mpl.rcParams['font.family'] = ... | github_jupyter |
# Word vectors from SEC filings using Gensim: Preprocessing
In this section, we will learn word and phrase vectors from annual SEC filings using gensim to illustrate the potential value of word embeddings for algorithmic trading. In the following sections, we will combine these vectors as features with price returns t... | github_jupyter |
```
%%HTML
<!-- Mejorar visualización en proyector -->
<style>
.rendered_html {font-size: 1.2em; line-height: 150%;}
div.prompt {min-width: 0ex; padding: 0px;}
.container {width:95% !important;}
</style>
%autosave 0
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import disp... | github_jupyter |
# Predicting Conversations Gone Awry With Convokit
This interactive tutorial demonstrates how to predict whether a conversation will eventually lead to a personal attack, as seen in the paper [Conversations Gone Awry: Detecting Early Signs of Conversational Failure](http://www.cs.cornell.edu/~cristian/Conversations_go... | github_jupyter |
## Node Classification on Citation Network
In this tutorial, we demostrate how GraphScope process node classification task on citation network by combining analytics, interactive and graph neural networks computation.
In this example, we use [ogbn-mag](https://ogb.stanford.edu/docs/nodeprop/#ogbn-mag) dataset. ogbn-ma... | github_jupyter |
# Exercícios de fixação sobre: Pandas, Matplotlib e Seaborn
Elaborado por <a
href="https://www.linkedin.com/in/bruno-coelho-277519129/">Bruno Gomes
Coelho</a>, para as aulas do grupo [DATA](https://github.com/icmc-data).
### Instruções:
Siga o passo do notebook, adiconando seu códiga toda vez que ver um `# your code... | github_jupyter |
## 機械力学テキスト 12章 ロボットシミュレーション
# プログラム例と実習
#### 宇都宮大学 吉田 勝俊
### 重要な注意
- このNotebookを開いただけの状態では，編集結果は保存されないので，各自，「ファイル」メニューから「ドライブにコピーを保存」してください．
- 操作方法の詳細は [Python / Colab 超入門](https://github.com/ktysd/python-startup/wiki) で勉強してください．
### １．使用するライブラリの読込
```
#この枠をクリックしてアクティブにしてから，Shiftを押しながらEnterを押すと，枠内のコードが実行されます．以下同じです... | github_jupyter |
```
import os
import numpy as np
import matplotlib.pyplot as plt
%matplotlib notebook
%config InlineBackend.figure_format = 'retina'
#%matplotlib qt
#%gui qt
dataDir = "/Users/rhl/TeX/Talks/DSFP/2018-01/Exercises/Detectors"
```
Let's start by looking at some extracted spectra
Read the data (and don't worry about ... | github_jupyter |
```
# noexport
import os
os.system('export_notebook identify_domain_training_data_v4.ipynb')
from tmilib import *
import csv
import sys
num_prev_enabled = int(sys.argv[1])
num_labels_enabled = 1 + num_prev_enabled # since we disabled the n label
data_version = 4+8+8 + num_prev_enabled
print 'num_prev_enabled', num_pre... | github_jupyter |
# **Import required Libraries!**
in the below block we're importing the libraries that we will use in the further codes in this Question!
```
import os, gzip, torch
import torch.nn as nn
import numpy as np
import scipy.misc
import imageio
import matplotlib.pyplot as plt
from torchvision import datasets, transforms
im... | github_jupyter |
# 蛋白质预训练和性质预测
在这份教程中，我们将介绍如何构建一个序列模型来进行蛋白质性质预测。具体来说，我们将展示如何对模型进行预训练并针对下游任务进行微调。关于这个主题的更多详细介绍请查阅[这里](https://github.com/PaddlePaddle/PaddleHelix/tree/dev/apps/pretrained_protein/tape/README_cn.md)。
近年来，随着测序技术的发展，蛋白质序列数据库的规模显著扩大。然而，必须通过湿实验才能够获得的有标注蛋白序列的成本仍然很高。此外，由于标记样本数量不足，模型有很高的概率过拟合数据。借鉴自然语言处理（NLP）的思想，通过自监督学习可以在大量无标注... | github_jupyter |
<a href="https://colab.research.google.com/github/Dmitri9149/Transformer_From_Scratch/blob/main/Final_Working_Transformer_MXNet_v6_102400_128_10_25_10_20.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!pip install -U mxnet-cu101==1.7.0
!pip ins... | github_jupyter |
<a href="https://colab.research.google.com/github/chandlerbing65nm/Cassava-Leaf-Disease-Classification/blob/main/ViT_Inference.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Check Resources
```
gpu_info = !nvidia-smi
gpu_info = '\n'.join(gpu_inf... | github_jupyter |
# Data Exploration
## Prepare notebook
Import libraries for plotting, data cleaning, weighting observations, and regular expressions
```
from matplotlib import rcParams # plotting
import numpy as np # computing
import pandas as pd # data analysis
import pickle # serialisation
import re # regular expressions
imp... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import os
import cv2
from tqdm import tqdm
# www.microsoft.com/en-us/download/confirmation.aspx?id=54765
DATADIR = "E:/Datasets/PetImages"
CATEGORIES = ["Dog", "Cat"]
for category in CATEGORIES: # do dogs and cats
path = os.path.join(DATADIR,category) # cr... | github_jupyter |
```
import os
import mypackages.myrasters as mr
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
swc_dir = os.path.join('..', 'output/soil_water_content_prepared')
soil_dir = os.path.join('..', 'output/soilgrids_prepared')
out_dir = os.path.join('..', 'output/corrections_calculated')
months = ['jan... | github_jupyter |
# Build barcode variant table
This Python Jupyter notebook builds consensus sequences for barcoded variants from the mutations called in the processed PacBio CCSs.
It then uses these consensus sequences to build a codon variant table.
## Set up analysis
### Import Python modules.
Use [plotnine](https://github.com/has2... | github_jupyter |
```
#initialization
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import math
# importing Qiskit
from qiskit import IBMQ, BasicAer
from qiskit.providers.ibmq import least_busy
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, execute
# import basic plot tools
from qiskit.t... | github_jupyter |
# Automatic Differentiation
> The **backpropagation** algorithm was originally introduced in the 1970s, but its importance wasn't fully appreciated until a famous 1986 paper by David Rumelhart, Geoffrey Hinton, and Ronald Williams. (Michael Nielsen in "Neural Networks and Deep Learning", http://neuralnetworksanddeeple... | github_jupyter |
```
from datascience import *
import sympy
solve = lambda x,y: sympy.solve(x-y)[0] if len(sympy.solve(x-y))==1 else "Not Single Solution"
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import datetime as dt
import warnings
warnings.simplefilter("ignore")
%matplotlib inline
```
# Cournot Competi... | github_jupyter |
<a href="https://colab.research.google.com/github/yukinaga/automl/blob/main/section_4/01_automl_titanic.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# タイタニック号生存者の予測
AutoMLにより、タイタニック号の生存者を予測します。
訓練済みのモデルによる予測結果は、csvファイルに保存して提出します。
## PyCaretのイン... | github_jupyter |
## Finding sources in the VAST Pilot Survey
This notebook gives an example of how to use vast-tools in a notebook environment to perform a search of known sources or coordinates.
Below are the imports required for this example. The main import required from vast-tools is the Query object, with which queries can be in... | github_jupyter |
```
import numpy as np
class EGreedy:
"""
Implementation of EGreedy algorithm as described in Section 2 of book:
Reinforcement Learning: An Introduction (Version 2)
Richard S. Sutton and Andrew G. Barto
"""
def __init__(self, k, epsilon=0.1):
"""
Constructor of EGreedy
... | github_jupyter |
# <img src="https://github.com/JuliaLang/julia-logo-graphics/raw/master/images/julia-logo-color.png" height="100" /> _for Pythonistas_
> TL;DR: _Julia looks and feels a lot like Python, only much faster. It's dynamic, expressive, extensible, with batteries included, in particular for Data Science_.
This notebook is a... | github_jupyter |
```
import matplotlib as mpl
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
from sklearn.metrics import accuracy_score
from sklearn.feature_extraction.text import CountVectorizer, TfidfTran... | github_jupyter |
# 第2回講義 演習
```
from sklearn.utils import shuffle
from sklearn.datasets import fetch_mldata, fetch_openml
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import numpy as np
np.random.seed(34)
```
## 目次
課題1. ロジスティック回帰の実装と学習 (OR)
1. シグモイド関数
2. データセットの設定と重みの定義
3. train関数... | github_jupyter |
```
import os
import torch
from neo import *
import yaml
from tqdm import tqdm
%load_ext autoreload
%autoreload 2
import pandas as pd
from database_env import *
def get_times(path, env_config, time=False):
env = DataBaseEnv(env_config)
plans = {}
times = {}
paths = list(Path(path).glob("*sql.jso... | github_jupyter |
## A tremendously brief R tutorial
Hadley Wickham is better at teaching R than I am and it brings me absolutely no shame to say so. If you want to understand how R works and to quickly learn how to put R into practice, your best bet is to turn to his R for data science course and dig in. But like it's unnessessary to ... | github_jupyter |
## PRINCIPLE COMPONENT ANALYSIS
### Authors
Ndèye Gagnessiry Ndiaye and Christin Seifert
### License
This work is licensed under the Creative Commons Attribution 3.0 Unported License https://creativecommons.org/licenses/by/3.0/
This notebook:
- creates PCA projections of Iris dataset
```
import pandas as pd
i... | github_jupyter |
<figure>
<IMG SRC="https://mamba-python.nl/images/logo_basis.png" WIDTH=125 ALIGN="right">
</figure>
# Warnings
_developed by Onno Ebbens_
<hr>
Warnings in Python zijn bedoelt om een gebruiker van code te waarschuwen maar wel door te gaan met het uitvoeren van de code. In dit notebook wordt uitgelegd welke in... | github_jupyter |
```
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import pandas as pd
import numpy as np
import time
from sklearn.metrics import log_loss
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.preprocessing import RobustScaler
from keras.prep... | github_jupyter |
# Data-X Project: Electricity Price Prediction
## Feature Modeling Group: Machine Learning Optimization Pipeline
Description of Notebook
Retail electricity prices across different regions will have varying dependencies on all kinds of other signals in the energy marketplace. This notebook is an "pipeline" that integr... | github_jupyter |
## Domain knowledge discretisation
Frequently, when engineering variables in a business setting, the business experts determine the intervals in which they think the variable should be divided so that it makes sense for the business. Typical examples are the discretisation of variables like Age and Income.
Income fo... | github_jupyter |
# Examples for lolviz
## Install
If on mac, I had to do this:
```bash
$ brew install graphviz # had to upgrade graphviz on el capitan
```
Then
```bash
$ pip install lolviz
```
## Sample visualizations
```
from lolviz import objviz, listviz, lolviz, callviz, callsviz, treeviz, strviz
objviz([u'2016-08-12',107.779... | github_jupyter |
<h1>Loops in Python</h1>
<p><strong>Welcome!</strong> This notebook will teach you about the loops in the Python Programming Language. By the end of this lab, you'll know how to use the loop statements in Python, including for loop, and while loop.</p>
<div class="alert alert-block alert-info" style="margin-top: 20px... | github_jupyter |
```
import xml.etree.ElementTree as ET
# !pip install xml-python
# parsing the xml data
# intro to Element Tree
tree = ET.parse('movie.xml')
tree
root = tree.getroot()
print(root)
root.tag
root.attrib
# for loop
for child in root:
print(child.tag ,":attrib> ",child.attrib)
for elem in root.iter():
print(elem.t... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W1D5_DimensionalityReduction/W1D5_Tutorial4.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Neuromatch Academy: Week 1, Day 5, Tutorial 4
# Di... | github_jupyter |
# Challenge: Analyzing Text about Data Science
In this example, let's do a simple exercise that covers all steps of a traditional data science process. You do not have to write any code, you can just click on the cells below to execute them and observe the result. As a challenge, you are encouraged to try this code ... | github_jupyter |
```
import os
from glob import glob
from joblib import Parallel, delayed
from tqdm import tqdm_notebook as tqdm
import pickle
import pandas as pd
import pumpp
import jams
import numpy as np
def root(x):
return os.path.splitext(os.path.basename(x))[0]
AUDIO = jams.util.find_with_extension('/home/bmcfee/data/eric_... | github_jupyter |
# COVID-19 Classification using Logistic Regression
In this notebook, we have implemented baseline models Logistic Regression Model. Later, we have also taken the layer embeddings to analyse the decision boundaries using clustering approach
```
! pip install opencv-python
import numpy as np
import os
import cv2
from ... | github_jupyter |
## Polynomial Chaos Expansion Example 6
Author: Katiana Kontolati \
Date: December 8, 2020
In this example, PCE is used to generate a surrogate model for a given set of 8D data.
### Robot arm function
<img src="Example_6_function.png" alt="Drawing" style="width: 200px;"/>
**Dimensions:** 8
**Description:** Model... | github_jupyter |
### osu!nn #5: New Map Reader
Reads the data from the music. This data will be used to create a whole map!
Final edit: 2018/8/16
Before you read data from the music, it needs timing.
Luckily there are some BPM analyzers on the web, and those are pretty accurate, so no need of Deep Learning for that!
The analyzer I... | github_jupyter |
# SN Like candidates in the last 4 days
### Ken Smith
Get supernova candidates ingested into Lasair within the last 4 days. This notebook will use the Lasair client code, except for acquisiton of the user token.
Demonstrates usage of:
* /query/
* /objects/
### Python (3 only) requirements - pip install
lasair, reque... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
```
# QUESTION 3(a)
```
dataset = pd.read_csv("iris.csv", header=None)
dataset.head()
```
## Rename Column Names
```
dataset.columns = ['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width', 'Class']
dataset.head()
```
## Check for m... | github_jupyter |
## Pre-Procesing
```
from preprocessing import train_valid_test_split, combine_labels, get_attribute_dims
# Train-Test Split Folders
SOURCE_DATA_DIR = "data/ClothingAttributeDataset/images/"
TARGET_DATA_DIR = "data/ClothingAttributeDataset/"
# Labels File
LABEL_DIR = "data/ClothingAttributeDataset/labels/"
labels_fil... | github_jupyter |
# Iterative methods
We explore in this notebook some iterative techniques for linear algebra problems. The implementations are written to expose how the methods work and are not optimised. Production-level implementations typically involve some specialised optimisations.
This notebook illustrates:
- Power iteration ... | github_jupyter |
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