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04 .Differential expression using DESeq2
========================================
The analysis process includes three main steps, namely normalization, dispersion estimation and test for differential expression.
```
library(phyloseq)
library(ggplot2)
library(scales)
library(gridExtra)
suppressPackageStartupMessages(l... | github_jupyter |
```
import json
with open("../out/202006222159_spanishfn.json") as fp:
data = json.load(fp)
from scipy.stats import rankdata
def rank_transform(orig):
data = np.copy(orig)
indices = [i for i, s in enumerate(data) if s > 0]
norm = rankdata([data[i] for i in indices], "max") / len(indices)
for i, s... | github_jupyter |
# Quantum Teleportation
This notebook demonstrates quantum teleportation. We first use Qiskit's built-in simulators to test our quantum circuit, and then try it out on a real quantum computer.
## Contents
1. [Overview](#overview)
2. [The Quantum Teleportation Protocol](#how)
3. [Simulating the Teleportati... | github_jupyter |
# Model viewer
Quickly view results of previously run models in Jupyter Notebook. Results and parameters can also be viewed in the directory itself, but this notebook provides a quick way to either (1) view all data from a single run in one place and (2) compare the same file across multiple runs. It does require some... | github_jupyter |
# Bayesian Survival Analysis
Copyright 2017 Allen Downey
MIT License: https://opensource.org/licenses/MIT
```
from __future__ import print_function, division
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import thinkbayes2
import thinkplot
```
## Sur... | github_jupyter |
# Imports
```
import numpy as np
import sklearn.metrics
from sklearn import linear_model
from sklearn.datasets import load_breast_cancer
```
# Load Data
"Breast Cancer" is a tiny dataset for binary classification
```
features, targets = load_breast_cancer(return_X_y=True)
print('Features')
print('shape:', features.... | github_jupyter |
# Sample Survey Bihar Election 2021 EDA
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
```
### Load the dataset into a pandas dataframe. Name the variable as “survey”.
```
survey=pd.read_excel('Sample Survey.xlsx',sheet_name='Data')
survey.head()
```
### How many sa... | github_jupyter |
```
# !wget http://s3-ap-southeast-1.amazonaws.com/huseinhouse-storage/bert-bahasa/bert-bahasa-base.tar.gz
# !tar -zxf bert-bahasa-base.tar.gz
from tqdm import tqdm
import json
import bert
from bert import run_classifier
from bert import optimization
from bert import tokenization
from bert import modeling
import numpy ... | github_jupyter |
```
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
heart_df = pd.read_csv("data/heart-disease.csv")
heart_df.head() # classification dataset - supervised learning
```
## 1. Tuning hyperparameters by hand
so far we've worked with training and test datase... | github_jupyter |
# 6장. 알고리즘 체인과 파이프라인
*아래 링크를 통해 이 노트북을 주피터 노트북 뷰어(nbviewer.org)로 보거나 구글 코랩(colab.research.google.com)에서 실행할 수 있습니다.*
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://nbviewer.org/github/rickiepark/intro_ml_with_python_2nd_revised/blob/main/06-algorithm-chains-and-pipelines... | github_jupyter |
# Mixture Density Network for Regression
```
import nbloader,os,warnings
warnings.filterwarnings("ignore")
import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
import tensorflow as tf
import tensorflow.contrib.slim as slim
from sklearn.utils import shuffle
from util import gpusession,create_gradi... | github_jupyter |
# Ensembles notebook
<a href="https://mybinder.org/v2/gh/tinkoff-ai/etna/master?filepath=examples/ensembles.ipynb">
<img src="https://mybinder.org/badge_logo.svg" align='left'>
</a>
This notebook contains the simple examples of using the ensemble models with ETNA library.
**Table of Contents**
* [Load Dataset]... | github_jupyter |
# Calculation of the entropy for sources with and without memory
## Introduction
This tutorial will get you familiar with the calculation of the entropy associated with a given source. We start by recalling some definitions and fundamental results from the [Shannon's information theory](http://people.math.harvard.edu/~... | github_jupyter |
<a href="https://colab.research.google.com/github/clemencia/ML4PPGF_UERJ/blob/master/correlations.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
**Valores esperados, médias e variância**
**Valor esperado** ou média de x:
$\mu = E[x] = \int_{-\in... | github_jupyter |
# Tutorial NlOpt
## Зачем это нужно?
В современных компетенциях инженерных или научных специальностей всё чаще приходится сталкиваться с теми или иными задачами требующими оптимизации функции.
В общем смысле под оптимизацией понимают поиск экстремума исследуемой функции.
$$f(x,y) \rightarrow max(min)$$
Заметим, что ... | github_jupyter |
# Vessels making voyages
The `voyages` table contains top level information about a voyage from one port to another, including when and where the voyage started and ended, and which vessel was involved in the voyage. You can use this information to identify which vessels made a voyage from one port to another in some t... | github_jupyter |
# The Lasso
Modified from the github repo: https://github.com/JWarmenhoven/ISLR-python which is based on the book by James et al. Intro to Statistical Learning.
```
# %load ../standard_import.txt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import scale
from sklea... | github_jupyter |
```
import pandas as pd
from googleapiclient.discovery import build
from googleapiclient.errors import HttpError
from oauth2client.tools import argparser
target = "아디다스 슈퍼스타"
DEVELOPER_KEY = "AIzaSyAnEEAKE50qxf5lHbsucDiMNayh9aFUj5g"
YOUTUBE_API_SERVICE_NAME="youtube"
YOUTUBE_API_VERSION="v3"
youtube = build(YOUTUBE_API... | github_jupyter |
# Stacking LSTM Layers
-----------------
Here we implement an LSTM model on all a data set of Shakespeare works. We will stack multiple LSTM models for a more accurate representation of Shakespearean language. We will also use characters instead of words.
```
import os
import re
import string
import requests
import n... | github_jupyter |
#### Verification Alignment
A forecast is verified by comparing a set of initializations at a given lead to
observations over some window of time. However, there are a few ways to decide *which*
initializations or verification window to use in this alignment.
One must pass the keyword ``alignment=...`` to the hindcas... | github_jupyter |
In this notebook, we explore the learning curve for the toxic spans detector
```
from transformers import RobertaTokenizer, RobertaForTokenClassification
from transformers import BertTokenizer, BertForTokenClassification
from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch
import numpy ... | github_jupyter |
# DecisionTreeRegressor with Normalize
This Code template is for regression analysis using simple DecisionTreeRegressor based on the Classification and Regression Trees algorithm along with Normalize Feature Scaling technique.
### Required Packages
```
import warnings
import numpy as np
import pandas as pd
import se... | github_jupyter |
```
from IPython.core.display import HTML, display
display(HTML("<style>.container { width:80% !important; }</style>"))
display(HTML("<style>div.output_scroll { height: 44em; }</style>"))
%%capture
# install popmon (if not installed yet)
import sys
!"{sys.executable}" -m pip install popmon
import pandas as pd
import... | github_jupyter |
## packages
```
import tensorflow as tf
from tensorflow import keras
import tensorflow_probability as tfp
from tensorflow.keras import layers
from tensorflow.keras.models import load_model
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import RobustScaler
from tensorflow.keras.preprocessing.... | github_jupyter |
## Flexible models
This toolbox can handle models with fitted model parts. In this demo we will see how this is done.
First we need some imports:
```
import numpy as np
import matplotlib.pyplot as plt
import rsatoolbox
```
As a first step lets generate a few random RDMs, which will serve as our data. We generate 10 ... | github_jupyter |
# 1-异常检测
## note:
* [covariance matrix](http://docs.scipy.org/doc/numpy/reference/generated/numpy.cov.html)
* [multivariate_normal](http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.multivariate_normal.html)
* [seaborn bivariate kernel density estimate](https://stanford.edu/~mwaskom/software/seaborn/ge... | github_jupyter |
## Filtering and Annotation Tutorial
### Filter
You can filter the rows of a table with [Table.filter](https://hail.is/docs/0.2/hail.Table.html#hail.Table.filter). This returns a table of those rows for which the expression evaluates to `True`.
```
import hail as hl
hl.utils.get_movie_lens('data/')
users = hl.rea... | github_jupyter |
# Logistic Regression
---
Lets first import required libraries:
```
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix, jac... | github_jupyter |
# Linear regression as a statistical estimation problem
### Dr. Tirthajyoti Sarkar, Fremont, CA 94536
---
This notebook demonstrates linear regression as a statistical estimation problem. We will see how to do the following as part of a linear regression modeling,
- Compute statistical properties like standard error... | github_jupyter |
## Tercera parte pandas
- Operaciones con fechas
- Combinar dataframes
- Reacomodar datos
```
%pylab inline
import pandas as pd
# Cargar nuestra base de datos de elencos
elenco = pd.read_csv('data/cast.csv', encoding='utf-8')
elenco.head()
# Ahora tambien cargaremos datos de otra base de datos
#
fecha_lanz = pd.read... | github_jupyter |
# Initialization
Welcome to the first assignment of "Improving Deep Neural Networks".
Training your neural network requires specifying an initial value of the weights. A well chosen initialization method will help learning.
If you completed the previous course of this specialization, you probably followed our ins... | github_jupyter |
```
import os, sys
import paddle
sys.path.append('/workspace/fnet_paddle/PaddleNLP')
from paddlenlp.datasets import load_dataset
test_ds = load_dataset("glue", name="cola", splits=("test"))
len(test_ds)
def convert_example(example,
tokenizer,
max_seq_length=512,
... | github_jupyter |
```
# get current timestamp for proper documentation of testing and validation results
from datetime import datetime
currentTime = str(datetime.now())
model_save_name = 'causal_classifier_' + currentTime + '.bin'
#path = F"/content/gdrive/My Drive/Causality Classification/"
```
## Setup
Load the transformers library... | github_jupyter |
# Interactions from the literature
```
%pylab inline
%config InlineBackend.figure_format = 'retina'
import json
import numpy as np
studies = [ { 'name' : 'Gopher, Lice',
'type' : 'parasitism',
'host' : 'data/gopher-louse/gopher.tree',
'guest': 'data/gopher-louse/lice.tree',
... | github_jupyter |
# Rigid-body transformations in three-dimensions
> Marcos Duarte
> Laboratory of Biomechanics and Motor Control ([http://demotu.org/](http://demotu.org/))
> Federal University of ABC, Brazil
The kinematics of a rigid body is completely described by its pose, i.e., its position and orientation in space (and the co... | github_jupyter |
There are two main functions
* decision_function
* predict_proba
Most of classifiers have at least one of them, and many have both
```
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_circles
import numpy as np
import matpl... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
import torch
import torch.nn as nn
class DepthwiseConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding=1):
super(DepthwiseConv2d, self).__init__()
self.depthwiseconv = nn.Sequential(
... | github_jupyter |
```
import os
import csv
import sys
import scipy.optimize as opt
import scipy.stats as stat
from operator import itemgetter
import random
import numpy as np
import numpy.ma as ma
import numpy.linalg as la
pi = np.pi
sin = np.sin
cos = np.cos
def fillin2(data):
"""
Fills in blanks of arrays without shifting fra... | github_jupyter |
```
import pandas as pd
import numpy as np
data=pd.read_csv("/home/jay/Desktop/Cricket/Final/FinalTrainingDataset.csv")
data
X=data.iloc[:,1:14].values
y=data.iloc[:,14].values
y
X.shape
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.20,random_state... | github_jupyter |
# 作業 : (Kaggle)鐵達尼生存預測
***
- 分數以網站評分結果為準, 請同學實際將提交檔(*.csv)上傳試試看
https://www.kaggle.com/c/titanic/submit
# [作業目標]
- 試著模仿範例寫法, 在鐵達尼生存預測中, 觀查堆疊泛化 (Stacking) 的寫法與效果
# [作業重點]
- 完成堆疊泛化的寫作, 看看提交結果, 想想看 : 分類與回歸的堆疊泛化, 是不是也與混合泛化一樣有所不同呢?(In[14])
如果可能不同, 應該怎麼改寫會有較好的結果?
- Hint : 請參考 mlxtrend 官方網站 StackingClassifier 的頁面說明 : ... | github_jupyter |
# SLU09 - Linear Algebra & NumPy, Part 1
### Learning Notebook 1/2
In this notebook we will be covering the following:
- **Vectors**: definition, transpose, norm, multiplication by a scalar and addition, linear combinations, linear independence and dot product;
- **Introduction to NumPy arrays:** vectors and nump... | github_jupyter |
```
# from google.colab import drive
# drive.mount('/content/drive')
# path = "/content/drive/MyDrive/Research/cods_comad_plots/sdc_task/mnist/"
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import torch
import torchvision
import torchvisi... | github_jupyter |
## Notebook 1:
```
### Notebook 1
### Data set 1 (Viburnum)
### Language: Bash
### Data Location: NCBI SRA PRJNA299402 & PRJNA299407
%%bash
## make a new directory for this analysis
mkdir -p empirical_1/
mkdir -p empirical_1/halfrun
mkdir -p empirical_1/fullrun
## import Python libraries
import pandas as pd
import num... | github_jupyter |
# Class Session 10 - Date Hubs and Party Hubs
## Comparing the histograms of local clustering coefficients of date hubs and party hubs
In this class, we will analyze the protein-protein interaction network for two classes of yeast proteins, "date hubs" and "party hubs" as defined by Han et al. in their 2004 study of ... | github_jupyter |
```
import autoreg
import GPy
import numpy as np
from matplotlib import pyplot as plt
from __future__ import print_function
%matplotlib inline
from autoreg.benchmark import tasks
# Function to compute root mean square error:
def comp_RMSE(a,b):
return np.sqrt(np.square(a.flatten()-b.flatten()).mean())
# Define cl... | github_jupyter |
[](http://rpi.analyticsdojo.com)
<center><h1>Train Test Splits with Python</h1></center>
<center><h3><a href = 'http://rpi.analyticsdojo.com'>rpi.analyticsdojo.com</a></h3></center>
```
#Let's get rid of ... | github_jupyter |
# MNIST SVD Classification
Follows Chapter 11 of Matrix Methods in Data Mining and Pattern Recognition by Lars Elden,
with added dimensionality reduction visualization
#### Author: Daniel Yan
#### Email: daniel.yan@vanderbilt.edu
```
from keras.datasets import mnist
from matplotlib import pyplot as plt
import numpy as... | github_jupyter |
The goal of this notebook is to verify that you can load the checkpointed model from it's github repo and run it on a few test image samples and verify that the whole inference pipeline works.
```
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
```
... | github_jupyter |
# Neural Network for Regression
In the previous homework you implemented a linear regression network. In this exercise, we will solve the same problem with a neural network instead, to leverage the power of Deep Learning.
We will implement our neural networks using a modular approach. For each layer we will implement ... | github_jupyter |
```
# import packages
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import featuretools as ft
import lightgbm as lgb
%matplotlib inline
import seaborn as sns
import math
import pickle
import os, sys, gc, warnings, random, datetime
RSEED = 50
```
## Load Data
```
# Load training data
df_train... | github_jupyter |
# Transfer Learning
A Convolutional Neural Network (CNN) for image classification is made up of multiple layers that extract features, such as edges, corners, etc; and then use a final fully-connected layer to classify objects based on these features. You can visualize this like this:
<table>
<tr><td rowspan=2 st... | github_jupyter |
```
var = 3
print(var)
var = 7
var
arr1 = []
type(arr1)
arr2 = [1,2,3,4,5]
type(arr2)
len(arr2)
dir(arr1)
print(arr1)
arr1.append(3)
arr1
arr1.append(4)
arr1
arr1.append(5)
arr1.insert(3,2)
arr1
dir(arr1.insert)
arr3 = [1,3,4,'Winner','Emeto',4,6,4]
arr3
arr3.count(4)
arr3.index(3)
def hi():
print('Hello Fellows!')... | github_jupyter |
# [Sensor name]
:::{eval-rst}
:opticon:`tag`
:badge:`[Environment],badge-primary`
:badge:`Sensors,badge-secondary`
:::
## Context
### Purpose
*Describe the purpose of the use case.*
### Sensor description
*Describe the main features of the sensor e.g. variables.*
### Highlights
*Provide 3-5 bullet points that conve... | github_jupyter |
```
%pylab inline
from simqso.sqgrids import *
from simqso import sqbase
from simqso.sqmodels import QLF_McGreer_2013
# set up a luminosity-redshift grid
M = AbsMagVar(UniformSampler(-30,-25),restWave=1450)
z = RedshiftVar(UniformSampler(1,5))
MzGrid = QsoSimGrid([M,z],(4,3),2,seed=12345)
scatter(MzGrid.z,MzGrid.absMag... | github_jupyter |
# <div align="center">Credit Fraud Detector</div>
---------------------------------------------------------------------
you can find the kernel link below:
> ###### [ Kaggle](https://www.kaggle.com/janiobachmann/credit-fraud-dealing-with-imbalanced-datasets)
## Introduction
In this kernel we will use various predicti... | github_jupyter |
This short example show how to get data from FMI Open Data multipointcoverage format. The format is used in INSPIRE specifications and is somewhat complex. Anyway, it's the most efficient way to get large amounts of data.
Here we fetch all observations from Finland during two days.
This example is for "old" format WF... | github_jupyter |
# HPDM097: Foundations of combinatorial optimisation for routing and scheduling problems in health
Many healthcare systems manage assets or workforce that they need to deploy geographically. One example, is a community nursing team. These are teams of highly skilled nurses that must visit patients in their own home. A... | github_jupyter |
# Cox model
```
import warnings
import arviz as az
import numpy as np
import pymc3 as pm
import scipy as sp
import theano.tensor as tt
from pymc3 import (
NUTS,
Gamma,
Metropolis,
Model,
Normal,
Poisson,
find_MAP,
sample,
starting,
)
from theano import function as fn
from theano i... | github_jupyter |
## ------- >--------- >----------PLAYSTORE ANALYSIS USING PYTHON-------- >----------- >--------- ##
# BY :
# ARAVINTH.S
# BE - COMP SCIENCE ENGINEERING
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
ps = pd.read_csv("plst.csv")
ps.head()
ps.shape
ps.size
ps.... | github_jupyter |
# Example Seldon Core Deployments using Helm with Istio
Prequisites
* [Install istio](https://istio.io/latest/docs/setup/getting-started/#download)
## Setup Cluster and Ingress
Use the setup notebook to [Setup Cluster](https://docs.seldon.io/projects/seldon-core/en/latest/examples/seldon_core_setup.html#Setup-Clus... | github_jupyter |
<a href="https://colab.research.google.com/github/agemagician/CodeTrans/blob/main/prediction/transfer%20learning%20fine-tuning/source%20code%20summarization/csharp/small_model.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
**<h3>Summarize the cshar... | github_jupyter |
# Spam Filter using Naive Bayes Classifier
```
import os
print(os.listdir("../input"))
```
**Import libraries**
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
```
**Read csv file**
```
df = pd.read_csv('../input/spam.csv', encoding='latin-1')[['v... | github_jupyter |
# Setup
```
library(ggplot2)
library(cowplot)
library(ranger)
library(Metrics)
library(latex2exp)
library(reshape2)
library(akima)
library(pander)
```
# Generate Model
Following is an example of how to generate the prediction model using the Random Forest Model with AIWC metrics and experimental runtimes of the Exte... | github_jupyter |
Note! For a most up to date version of this notebook, make sure you copy from:
[](https://colab.research.google.com/drive/1wTMIrJhYsQdq_u7ROOkf0Lu_fsX5Mu8a)
## Configs and Hyperparameters
Support a variety of models, you can find more pretrain... | github_jupyter |
# Attentional Networks in Computer Vision
Prepared by Comp411 Teaching Unit (TA Can Küçüksözen) in the context of Computer Vision with Deep Learning Course. Do not hesitate to ask in case you have any questions, contact me at: ckucuksozen19@ku.edu.tr
Up until this point, we have worked with deep fully-connected networ... | github_jupyter |
# German Company Registry IDs
## Introduction
The function `clean_de_handelsregisternummer()` cleans a column containing German company registry id (handelsregisternummer) strings, and standardizes them in a given format. The function `validate_de_handelsregisternummer()` validates either a single handelsregisternumm... | github_jupyter |
```
import sys
sys.path.append("/home/sean/pench")
sys.path.append("/network/lustre/iss01/home/adrien.martel")
import os
# os.environ["CUDA_VISIBLE_DEVICES"]="1"
# !git clone https://github.com/vlawhern/arl-eegmodels.git
from eegmodels.EEGModels import EEGNet, ShallowConvNet, DeepConvNet
from myModels import dualLSTM,... | github_jupyter |
# Home 4: Build a seq2seq model for machine translation.
### Name: [Your-Name?]
### Task: Translate English to [what-language?]
## 0. You will do the following:
1. Read and run my code.
2. Complete the code in Section 1.1 and Section 4.2.
* Translation English to **German** is not acceptable!!! Try another lan... | github_jupyter |
###### Content under Creative Commons Attribution license CC-BY 4.0, code under MIT license (c)2014 L.A. Barba, G.F. Forsyth.
# Relax and hold steady
Ready for more relaxing? This is the third lesson of **Module 5** of the course, exploring solutions to elliptic PDEs.
In [Lesson 1](http://nbviewer.ipython.org/github/... | github_jupyter |
# Day 0 Practical: Churn for Bank Customers
Welcome to the first practical session of the SPAI Advanced Machine Learning Workshop. In this practical, you will experience the full workflow of building a simple classifier to predict whether does a customer decides to leave the bank*(also known as churning)* given the fe... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/NAIP/loop_FeatureCollection.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" ... | github_jupyter |
# Aplicação: cores PANTONE
## Leitura de arquivos _json_
```
import os, json
# diretório base
base = '../database/pantone-colors/'
for fi in os.listdir(base):
n,e = os.path.splitext(fi)
if e == '.json':
with open(os.path.join(base,fi), 'r') as f:
# define variáveis dinamicamente
... | github_jupyter |
<div class="contentcontainer med left" style="margin-left: -50px;">
<dl class="dl-horizontal">
<dt>Title</dt> <dd> RGB Element</dd>
<dt>Dependencies</dt> <dd>Matplotlib</dd>
<dt>Backends</dt>
<dd><a href='./RGB.ipynb'>Matplotlib</a></dd>
<dd><a href='../bokeh/RGB.ipynb'>Bokeh</a></dd>
<dd><a href='../... | github_jupyter |
## pyHail MESH Animation
This code utilizes the pyHAIL package to plot MESH, or the "maximum expected size of hail", grid the plots, and then create an animation with the plots.
```
from __future__ import print_function
import warnings
import warnings
warnings.filterwarnings('ignore')
"""
MESH sub-module of pyhail
C... | github_jupyter |
# MinPy (MXNet NumPy)
*"Everybody loves NumPy."*
In this tutorial, we present MinPy -- a NumPy-like package based on MXNet. NumPy is a well-known python package widely used in scientific computing, statistics and machine learning. It supports a wide range of tensor operators and is very friendly to machine learning b... | github_jupyter |
```
__depends__=[]
__dest__="../results/f8.eps"
```
# Plot Terms in the Two-fluid EBTEL Equations
As part of our derivation of the two-fluid EBTEL equations, we'll plot the different terms of the two-fluid electron energy equation,
$$
\frac{L}{\gamma - 1}\frac{dp_e}{dt} = \psi_{TR} - (\mathcal{R}_C + \mathcal{R}_{TR})... | github_jupyter |
```
from bayestuner.tuner import BayesTuner
import numpy as np
import seaborn as sns
from bayestuner.optimizer import DifferentialEvolution
from bayestuner.acquisitionfunc import UCB
import matplotlib.pyplot as plt
import math
import matplotlib.animation as animation
from sklearn.gaussian_process.kernels import Constan... | github_jupyter |
```
## This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/... | github_jupyter |
```
# Allow us to load `open_cp` without installing
import sys, os.path
sys.path.insert(0, os.path.abspath(".."))
```
# Crime prediction from Hawkes processes
Here we continue to explore the EM algorithm for Hawkes processes, but now concentrating upon:
1. Mohler et al. "Randomized Controlled Field Trials of Predict... | github_jupyter |
# Emotion Classification
**Module 1: Introduction**
* Author: [Andrés Mitre](https://github.com/andresmitre), [Center for Research in Mathematics (CIMAT)](http://www.cimat.mx/en) in Zacatecas, México.
For installation, I highly recommend to follow the instructions from [Jeff Heaton](https://sites.wustl.edu/jeffheaton... | github_jupyter |
# Qiskit Aer: Applying noise to custom unitary gates
The latest version of this notebook is available on https://github.com/Qiskit/qiskit-tutorial.
## Introduction
This notebook shows how to add custom unitary gates to a quantum circuit, and use them for noise simulations in Qiskit Aer.
```
from qiskit import execu... | github_jupyter |
# 🔪 JAX - The Sharp Bits 🔪
*levskaya@ mattjj@*
When walking about the countryside of [Italy](https://iaml.it/blog/jax-intro), the people will not hesitate to tell you that __JAX__ has _"una anima di pura programmazione funzionale"_.
__JAX__ is a language for __expressing__ and __composing__ __transformations__ of ... | github_jupyter |
```
import os
import pyvtk
import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
# The data structure in element-wise output is too complicated for xarray.open_mfdataset.
# Here we open the files as individual datasets and concatenate them on the variable level.
# This code is compatible with parallel ... | github_jupyter |
# Image Deduplication with FiftyOne
This recipe demonstrates a simple use case of using FiftyOne to detect and
remove duplicate images from your dataset.
## Requirements
This notebook requires the `tensorflow` package:
```
!pip install tensorflow
```
## Download the data
First we download the dataset to disk. The... | github_jupyter |
# Comprehensive Guide to Grouping and Aggregating with Pandas
Chris Mofitt. "Comprehensive Guide to Grouping and Aggregating with Pandas". _Practical Business Python_, 9 Nov. 2020, https://pbpython.com/groupby-agg.html.
```
import pandas as pd
import seaborn as sns
df = sns.load_dataset('titanic')
```
## Pandas aggre... | github_jupyter |
# Tabular Datasets
As we have already discovered, Elements are simple wrappers around your data that provide a semantically meaningful representation. HoloViews can work with a wide variety of data types, but many of them can be categorized as either:
* **Tabular:** Tables of flat columns, or
* **Gridded:** Arr... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import os, sys
sys.path.insert(0, os.path.expandvars('/data/users/$USER/fbsource/fbcode/beanmachine'))
sys.path.insert(1, os.path.expandvars('/data/users/$USER/fbsource/third-party/pypi/flowtorch/0.0.dev2'))
import beanmachine.ppl as bm
import beanmachine.ppl as bm
import matplot... | github_jupyter |
```
import pandas as pd
import numpy as np
from tqdm import tqdm_notebook
from sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer, TfidfVectorizer
from sklearn.neighbors import NearestNeighbors
%matplotlib inline
links = pd.read_csv('links.csv')
movies = pd.read_csv('movies.csv')
ratings = pd.r... | github_jupyter |
## Unsupervised Learning
## Project: Creating Customer Segments
## Getting Started
In this project analyzed a dataset containing data on various customers' annual spending amounts (reported in *monetary units*) of diverse product categories for internal structure. One goal of this project is to best describe the vari... | github_jupyter |
```
# PyTorch
import torch
from torch import nn, optim
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, datasets
import torch.nn.functional as F
from torch.optim.lr_scheduler import ReduceLROnPlateau
# PyTorch Lightning
import pytorch_lightning as pl
from pytorch_lightning import Tr... | github_jupyter |
```
import os
import numpy as np
import pandas as pd
import cv2
import math
import matplotlib.pyplot as plt
import torch
from PIL import Image
import torchvision.transforms as T
train_data= pd.read_csv("../ELEC576project/sartorius-cell-instance-segmentation/train.csv")
def rotate_image(image, angle):
# Get th... | github_jupyter |
# Rigid-body transformations in three-dimensions
> Marcos Duarte
> Laboratory of Biomechanics and Motor Control ([http://demotu.org/](http://demotu.org/))
> Federal University of ABC, Brazil
The kinematics of a rigid body is completely described by its pose, i.e., its position and orientation in space (and the co... | github_jupyter |
# Design of Digital Filters
*This jupyter notebook is part of a [collection of notebooks](../index.ipynb) on various topics of Digital Signal Processing.
## Example: Non-Recursive versus Recursive Filter
In the following example, the characteristics and computational complexity of a non-recursive and a recursive fil... | github_jupyter |
# Visualizing tweets and the Logistic Regression model
**Objectives:** Visualize and interpret the logistic regression model
**Steps:**
* Plot tweets in a scatter plot using their positive and negative sums.
* Plot the output of the logistic regression model in the same plot as a solid line
## Import the required li... | github_jupyter |
# Student-t Process
PyMC3 also includes T-process priors. They are a generalization of a Gaussian process prior to the multivariate Student's T distribution. The usage is identical to that of `gp.Latent`, except they require a degrees of freedom parameter when they are specified in the model. For more information, ... | github_jupyter |
# GMNS Format Validation for networks stored as CSV files
This notebook demonstrates validation for whether a GMNS network conforms to the schema.
It uses a modified version of [GMNSpy](https://github.com/e-lo/GMNSpy), originally developed by Elizabeth Sall.
The first time you run this notebook after cloning this rep... | github_jupyter |
# Census- Employment Status Data
```
import pandas as pd
import requests
#Census Subject Table API for Employment Status data within Unified School Districts in California for 2018
url="https://api.census.gov/data/2016/acs/acs1/subject?get=group(S2301)&for=school%20district%20(unified)&in=state:06"
#Request for HTTP D... | github_jupyter |
#### New to Plotly?
Plotly's Python library is free and open source! [Get started](https://plotly.com/python/getting-started/) by downloading the client and [reading the primer](https://plotly.com/python/getting-started/).
<br>You can set up Plotly to work in [online](https://plotly.com/python/getting-started/#initiali... | github_jupyter |
<h1><font color='blue'> 8E and 8F: Finding the Probability P(Y==1|X)</font></h1>
<h2><font color='Geen'> 8E: Implementing Decision Function of SVM RBF Kernel</font></h2>
<font face=' Comic Sans MS' size=3>After we train a kernel SVM model, we will be getting support vectors and their corresponsing coefficients $\alph... | github_jupyter |
# Mnist classification pipeline using Sagemaker
The `mnist-classification-pipeline.py` sample runs a pipeline to train a classficiation model using Kmeans with MNIST dataset on Sagemaker.
We will have all required steps here and for other details like how to get source data, please check [documentation](https://githu... | github_jupyter |
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