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# Analyize Google Chrome history
Idea and initial code taken from the [Analyzing Browser History Using Python and Pandas](https://applecrazy.github.io/blog/posts/analyzing-browser-hist-using-python/) blogpost by __AppleCrazy__.
```
%matplotlib inline
import os
import pandas as pd
import numpy as np
import sqlite3
imp... | github_jupyter |
<table align="left" width="100%"> <tr>
<td style="background-color:#ffffff;">
<a href="http://qworld.lu.lv" target="_blank"><img src="../images/qworld.jpg" width="35%" align="left"> </a></td>
<td style="background-color:#ffffff;vertical-align:bottom;text-align:right;">
prepared... | github_jupyter |
# Welcome Spokane .NET User Group!
```
//Not explicitly needed
#r "System.CommandLine"
//Other NuGet packages: #r "nuget:<package name>"
using System.CommandLine;
using System.CommandLine.Builder;
using System.CommandLine.Parsing;
using System.CommandLine.Invocation;
using System.CommandLine.IO;
using Syste... | github_jupyter |
# 概率
:label:`sec_prob`
简单地说，机器学习就是做出预测。
根据病人的临床病史，我们可能想预测他们在下一年心脏病发作的*概率*。
在飞机喷气发动机的异常检测中，我们想要评估一组发动机读数为正常运行情况的概率有多大。
在强化学习中，我们希望智能体（agent）能在一个环境中智能地行动。
这意味着我们需要考虑在每种可行的行为下获得高奖励的概率。
当我们建立推荐系统时，我们也需要考虑概率。
例如，假设我们为一家大型在线书店工作，我们可能希望估计某些用户购买特定图书的概率。
为此，我们需要使用概率学。
有完整的课程、专业、论文、职业、甚至院系，都致力于概率学的工作。
所以很自然地，我们在这部分的目标不是教授你整个科目... | github_jupyter |
<i>Copyright (c) Microsoft Corporation. All rights reserved.</i>
<i>Licensed under the MIT License.</i>
# Apply Diversity Metrics
## -- Compare ALS and Random Recommenders on MovieLens (PySpark)
In this notebook, we demonstrate how to evaluate a recommender using metrics other than commonly used rating/ranking met... | github_jupyter |
# Traveling Salesman Problem (TSP) solved with Genetic Algorithms.
The Traveling Salesman Problem is a classic optimization problem that has as objective to calculate the most efficient
way to visit N cities with minimum travelled distance.
We will use a basic genetic algorithm to solve this problem by finding an opt... | github_jupyter |
<br>
# Painters Identification using ConvNets
### Marco Tavora
<br>
## Index
- [Building Convolutional Neural Networks](#convnets)
- [Small ConvNets](#smallconvnets)
- [Imports for Convnets](#importconvnets)
- [Preprocessing](#keraspreprocessing)
- [Training the model](#traincnn)
... | github_jupyter |
# Demo Collect Rook Usage
```
import pandas as pd
import hvplot.pandas # noqa
from rooki.client import Rooki
# Available hosts
hosts = {
'demo': 'rook.dkrz.de',
'dkrz': 'rook3.cloud.dkrz.de',
'ceda': 'rook-wps1.ceda.ac.uk',
}
# Use cache
cache_id = {
'ceda': '1f8181bc-d351-11eb-9402-005056aba41c',
... | github_jupyter |
```
import os
import json
import psycopg2
import pandas as pd
import geopandas as gpd
from geopandas import GeoSeries, GeoDataFrame
import folium
import fiona
from pyproj import Proj, transform
import osmnx as ox
import networkx as nx
import matplotlib.colors as colors
import matplotlib.cm as cm
from shapely.ops import... | github_jupyter |
```
from sys import modules
IN_COLAB = 'google.colab' in modules
if IN_COLAB:
!pip install -q ir_axioms[examples] python-terrier
# Start/initialize PyTerrier.
from pyterrier import started, init
if not started():
init(tqdm="auto")
from pyterrier.datasets import get_dataset, Dataset
# Load dataset.
dataset_na... | github_jupyter |
STAT 453: Deep Learning (Spring 2020)
Instructor: Sebastian Raschka (sraschka@wisc.edu)
- Course website: http://pages.stat.wisc.edu/~sraschka/teaching/stat453-ss2020/
- GitHub repository: https://github.com/rasbt/stat453-deep-learning-ss20
- Runs on CPU (not recommended here) or GPU (if available)
# ResNet-34 Convo... | github_jupyter |
# 4.2.2 Dependence on the Node Degree
```
%load_ext autoreload
%autoreload 2
%matplotlib notebook
from sensible_raw.loaders import loader
from world_viewer.cns_world import CNSWorld
from world_viewer.glasses import Glasses
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import networkx as nx
fro... | github_jupyter |
# Create all possible tSNE
This is a quick and dirty script to create all possible tSNEs.
```
# %load ../start.py
# Imports
import os
import sys
from pathlib import Path
from tempfile import TemporaryDirectory
import string
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as ... | github_jupyter |
# High-level CNTK Example
```
# Parameters
EPOCHS = 10
N_CLASSES=10
BATCHSIZE = 64
LR = 0.01
MOMENTUM = 0.9
GPU = True
LOGGER_URL='msdlvm.southcentralus.cloudapp.azure.com'
LOGGER_USRENAME='admin'
LOGGER_PASSWORD='password'
LOGGER_DB='gpudata'
LOGGER_SERIES='gpu'
import numpy as np
import os
import sys
import cntk
fr... | github_jupyter |
```
#A notebook for Tweet Sentiment Analysis by Jonathan Ivy
import tweepy
import re
import pickle
import matplotlib.pyplot as plt
import numpy as np
from tweepy import OAuthHandler #does the job of authenticating our client machine with Twitter server
#Now initialize all keys we need, and they should be entered in the... | github_jupyter |
# SQLAlchemy-Mutable examples
## SQAlchemy setup
```
from sqlalchemy_mutable import Mutable, MutableType, MutableModelBase, Query, partial
from sqlalchemy import Column, Integer, String, create_engine
from sqlalchemy.orm import sessionmaker, scoped_session
from sqlalchemy.ext.declarative import declarative_base
fr... | github_jupyter |
## Lagrange interpolation
Given $(n+1)$ distinct points $\{q_i\}_{i=0}^n$ in the interval $[0,1]$,
we define the *Lagrange interpolation* operator $\mathcal{L}^n$ the operator
$$
\mathcal{L}^n : C^0([0,1]) \mapsto \mathcal{P}^n
$$
which satisfies
$$
(\mathcal{L}^n f)(q_i) = f(q_i), \qquad i=0,\dots,n.
$$
This operato... | github_jupyter |
# Concept extraction from text
## 1. Loading text file into string
### Option 1. Downloading a wikipedia article's text
```
from bs4 import BeautifulSoup
import requests
url = 'https://en.wikipedia.org/wiki/Star'
source = requests.get(url).text
soup = BeautifulSoup(source,'lxml')
text_set = soup.find_all(['p']) ... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy
import pandas
import tqdm
import hetmech.hetmat
import hetmech.degree_group
import hetmech.degree_weight
import hetmech.pipeline
%matplotlib inline
hetmat = hetmech.hetmat.HetMat('../../data/hetionet-v1.0.hetmat/')
metapaths = ['DaGbC', 'SpDpS', 'SEcCrCtD', 'CiPCiCtD']... | github_jupyter |
```
%matplotlib inline
import numpy as np
import pandas as pd
import math
from scipy import stats
import pickle
from causality.analysis.dataframe import CausalDataFrame
from sklearn.linear_model import LinearRegression
import datetime
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams['font.sans-seri... | github_jupyter |
# Chapter 6: Sections 2-3
```
%pylab inline
```
## 6.2 Nearest-Neighbor Density Estimation
This method was first proposed by Dressler 1980 in an astrophysical context. The implied point density at a position $x$ is
$\hat{f}_{K}(x) = \frac{K}{V_{D}(d_{K})}$
or more simply
$\hat{f}_{K}(x) = \frac{C}{d^{D}_{K}}$
Th... | github_jupyter |
# Processing a HCP Dataset
Here we have run an HCP dataset through DSI Studio using the recommended parameters from the documentation. This includes
* Gradient unwarping
* Motion/Eddy correction
* TopUp
The images (dwi mask, dwi data and graddev files) were downloaded directly from connectomedb.org. The resu... | github_jupyter |
# Continuous Signals
*This Jupyter notebook is part of a [collection of notebooks](../index.ipynb) in the bachelors module Signals and Systems, Communications Engineering, Universität Rostock. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*
## Standard Si... | github_jupyter |
# Setup
```
### Libraries
import pandas as pd
from IPython.display import display
### Python OBDC bridge
import pyodbc
### IRIS Python Native API
import irisnative
### others...
import time
### SQL Connection parameters
dsn = 'IRIS IntegeratedML monitor'
server = 'irisimlsvr'
port = '51773'
database = 'USER'
userna... | github_jupyter |
# Predicting Whether a Breast Cancer Sample is Benign or Malignant
## Learning Objectives:
1. Understand what SageMaker Script Mode is, and how it can be leveraged.
2. Read in data from S3 to SageMaker
3. User prebuilt SageMaker containers to build, train, and deploy customer sklearn model
4. Use batch transform to ... | github_jupyter |
# WS2332 - Project 7 - Lecture 2
Miguel Bessa
<div>
<img src=docs/tudelft_logo.jpg width=300px></div>
**What:** Lab Session 1 of course WS2332 (Project 7): Introduction to Machine Learning
* Today's lecture focuses on **regression via supervised learning in 1D**
**How:** Jointly workout this notebook
* GitHub: https... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '2'
import tensorflow as tf
import numpy as np
from tensor2tensor.data_generators import problem_hparams
from tensor2tensor.models import evolved_transformer
from tensor2tensor.models import transformer
from tensor2tensor.utils import optimize
import json
with open('t... | github_jupyter |
<a href="https://colab.research.google.com/github/hemanthsunny/machine_learning/blob/master/Neural_network_layers.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
*Reference*
https://medium.com/fintechexplained/what-are-hidden-layers-4f54f7328263
``... | github_jupyter |
### Cybenko Equations
Printing equations using the coeficients obtained in cybenko_approx.ipynb
```
import numpy as np
import math
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def calc_iter(episode,n):
weights = nn_parameters[n][0]
biases = nn_parameters[n][1]
ip_h_weights = weights[0][0]
h_... | github_jupyter |
# Unsupervised methods
In this lesson, we'll cover unsupervised computational text anlalysis approaches. The central methods covered are TF-IDF and Topic Modeling. Both of these are common approachs in the social sciences and humanities.
[DTM/TF-IDF](#dtm)<br>
[Topic modeling](#topics)<br>
### Today you will
* Unde... | github_jupyter |
# Parameter identification example
Here is a simple toy model that we use to demonstrate the working of the inference package
$\emptyset \xrightarrow[]{k_1} X \; \; \; \; X \xrightarrow[]{d_1} \emptyset$
### Run the MCMC algorithm to identify parameters from the experimental data
In this demonstration, we will try... | github_jupyter |
# For External users
You can open this notebook in [Google Colab](https://colab.research.google.com/github/google/meterstick/blob/master/confidence_interval_display_demo.ipynb).
## Installation
You can install from pip for the stable version
```
#@test {"skip": true}
!pip install meterstick
```
or from GitHub for ... | github_jupyter |
```
#Given a map consisting of known poses and a start and end pose, find the optimal path between using A*
#Generate the relative motion in se2 between poses.
#This is straight line motion.
#Also implements cubic interpolation for a smooth trajectory across all points in path.
import matplotlib.pyplot as plt
import nu... | github_jupyter |
## 载入 `dmind` 插件
```
%load_ext dmind
```
## 载入 `dmind` 需要的附件
```
%dmindheader
```
## text 格式
```
%%dmind text
DMind
是一个 jupyter notebook 插件
是一个思维导图插件
```
## markdown 格式, 逻辑结构图
```
%%dmind markdown right
# DMind使用文档
## 安装
### pip install dmind
## 使用
### 载入插件
#### %load_ext dmind
### 载入需要的附件
#### %dmindh... | github_jupyter |
## 1 - '시퀀스 투 시퀀스' 신경망 학습
이 시리즈에서는 PyTorch 및 TorchText를 사용하여 한 시퀀스에서 다른 시퀀스로 이동하는 기계 학습 모델을 구축 할 것입니다. 이것은 독일어에서 영어로의 번역에서 수행되지만, 모델은 요약과 같이 한 시퀀스에서 다른 시퀀스로 이동하는 것과 관련된 모든 문제에 적용될 수 있습니다.
이 첫 번째 노트북에서는 [Sequence to Sequence Learning with Neural Networks](https://arxiv.org/abs/1409.3215)의 모델을 구현하여 일반적인 개념들을 간단하게 이해하... | github_jupyter |
# Lab Environment for BIA Pipeline
This notebook instance will act as the lab environment for setting up and triggering changes to our pipeline. This is being used to provide a consistent environment, gain some familiarity with Amazon SageMaker Notebook Instances, and to avoid any issues with debugging individual lap... | github_jupyter |
# Ising fitter for capped homopolymer repeat proteins.
Authors: Doug Barrick, Jacob D. Marold, Kathryn Geiger-Schuller, Tural Aksel, Ekaterina Poliakova-Georgantas, Sean Klein, Kevin Sforza, Mark Peterson
This notebook performs an Ising model fit to consensus Ankyrin repeat proteins (cANK). It reads data from Aviv d... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import base
LAST_N = 15
def get_success_and_fail_numbers_at_each_task():
grouped_users = base.get_dataset_and_group_by_user()
number_of_target_fails_top = {}
number_of_target_success_top = {}
number_of_target_fails_som = {}
... | github_jupyter |
#data make
```
import numpy as np
import matplotlib.pyplot as plt
def make_data(dimention=2):
#正常データの作成（2次元）
x1 = np.random.normal(1, 0.3, (1, 100))
y1 = np.random.normal(1, 0.3, (1, 100))
x2 = np.random.normal(1.5, 0.3, (1, 100))
y2 = np.random.normal(1.5, 0.3, (1, 100))
#テストデータの作成（2次元）
... | github_jupyter |
# PharmSci 175/275 (UCI)
## What is this??
The material below is an instructional session/lecture on docking, scoring and pose prediction from Drug Discovery Computing Techniques, PharmSci 175/275 at UC Irvine.
Extensive materials for this course, as well as extensive background and related materials, are available o... | github_jupyter |
[Source](https://www.dataquest.io/blog/pandas-python-tutorial/)
Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages, and makes importing and analyzing data much easier. Pandas builds on packages like NumPy and... | github_jupyter |
## 加载模型
```
import os
GPUID='0'##调用GPU序号
os.environ["CUDA_VISIBLE_DEVICES"] = GPUID
import torch
from apphelper.image import xy_rotate_box,box_rotate,solve
import model
import cv2
import numpy as np
import cv2
def plot_box(img,boxes):
blue = (0, 0, 0) #18
tmp = np.copy(img)
for box in boxes:
cv2.r... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
# Live Updating and Interactive Plots
## 1 Plotting Live data
In our work, We are often required to plot Live data.
* **psutil**: Cross-platform lib for process and system monitoring in Python
https://github.com/giampaolo/psutil
```text
python3 -m pip install psutil
```
### 1.1 Python Script
* matplotlib.py... | github_jupyter |
# Funzioni 1 - introduzione
## [Scarica zip esercizi](../_static/generated/functions.zip)
[Naviga file online](https://github.com/DavidLeoni/softpython-it/tree/master/functions)
Una funzione prende dei parametri e li usa per produrre o riportare qualche risultato.
<div class="alert alert-warning">
**ATTENZIONE**
... | github_jupyter |
# 5.3.1 The Validation Set Approach
```
# imports and setup
import numpy as np
import pandas as pd
pd.set_option('precision', 2) # number precision for pandas
pd.set_option('display.max_rows', 12)
pd.set_option('display.float_format', '{:20,.2f}'.format) # get rid of scientific notation
# load data
auto = pd.read_cs... | github_jupyter |
# CS155 Project 3 - Shakespearean Sonnets: Pre-processing data
**Author:** Liting Xiao
**Description:** this notebook pre-processes the Shakespeare's sonnet datasets for training.
```
import re
import pickle
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams.update({'font.size':... | github_jupyter |
```
import os
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
import scipy.io
import scipy.misc
import numpy as np
import pandas as pd
import PIL
import tensorflow as tf
from skimage.transform import resize
from keras import backend as K
from keras.layers import Input, Lambda, Conv2D
from keras.mo... | github_jupyter |
```
!pip install ipynb
import numpy as np
import os
from time import time
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import NullFormatter
import seaborn as sns
import ipynb
sns.set(style="darkgrid")
import warnings
#warnings.simplefilter(action='ignore', category=IntegrationWarning)
from... | github_jupyter |
```
# default_exp models.XCMPlus
```
# XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification
> This is an unofficial PyTorch implementation of XCM created by Ignacio Oguiza.
**References:**
* Fauvel, K., Lin, T., Masson, V., Fromont, É., & Termier, A. (2020). XCM: An Explainab... | github_jupyter |
# Geocomputing course
Welcome to geocomputing!
## Day 1. Introduction to Python
- Installation flailing
- Quick course overview
- [**Intro to Python**](../notebooks/Intro_to_Python.ipynb)
- Lightning talks
- A couple of quick demos
- [**Intro to Python**](../notebooks/Intro_to_Python.ipynb) — continued
- Che... | github_jupyter |
```
import numpy as np
from complete import *
import pickle
from simtk import unit
```
i will start by extracting benzene in solvent and running the vanilla `annealed_importance_sampling` on it
```
with open('benzene_methylbenzene.solvent.factory.pkl', 'rb') as f:
factory = pickle.load(f)
with open('benzene_methy... | github_jupyter |
# Sentiment Analysis - CP322
## Riley Huston (190954880) | Samson Goodenough (190723380) | Shailendra Singh ()
```
# import libraries
import nltk
import pandas as pd
import sklearn
import re
from nltk.corpus import stopwords
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.stem.porter import PorterStemmer
fro... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sympy
sympy.init_printing(use_latex='mathjax')
%matplotlib inline
```
## 적분
- 적분(integral)은 미분과 반대되는 개념이다.
- 부정적분(indefinite integral)
- 정적분(definite integral)
#### 부정적분(indefinite integral)
- 부정적분은 정확하게 미분과 반대되는 개념, 즉 반-미분(anti-deriv... | github_jupyter |
# 22 - Model Deployment
by [Alejandro Correa Bahnsen](albahnsen.com/)
version 0.1, May 2016
## Part of the class [Practical Machine Learning](https://github.com/albahnsen/PracticalMachineLearningClass)
This notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License]
## Agenda:
1.... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal
import scipy
import scipy.io as sio
import copy
import pylab as pl
import time
from IPython import display
```
## Chirp parameters
```
start_freq = 770000
band_freq = 80000
duration = 0.0004
samples_one_second = 10000000
rate = samples_on... | github_jupyter |
These class functions are the adversarial attack systems for NER; if entities == True an entity attack is performed, if entities == False an entity context attack is performed. It has options for performing a Random Attack (default is set to False).
```
import argparse
import glob
import logging
import os
import rando... | github_jupyter |
```
!unzip "/content/drive/MyDrive/Colab Notebooks/curso word2vec/cbow_s300.zip"
!unzip "/content/drive/MyDrive/Colab Notebooks/curso word2vec/skip_s300.zip"
```
## Libs Usadas
```
import nltk
import string
import numpy as np
import pandas as pd
from gensim.models import KeyedVectors
from sklearn.dummy import DummyCl... | github_jupyter |
# Taxon name information
## Input Name
Enter the taxon name.
```
#@title String fields
taxonNameFull = 'Solanum baretiae' #@param {type:"string"}
taxonName = taxonNameFull.split(" ")
```
## Initialisation
### Importing Libraries
```
!pip install -q SPARQLWrapper
!pip install -q pykew
import requests
import jso... | github_jupyter |
# Earthquake plots
```
def ProduceSpatialQuakePlot(Observations, FitPredictions):
current_time = timenow()
print_red(
current_time + " Produce Spatial Earthquake Plots " + config.experiment + " " + config.comment
)
dayindexmax = Num_Seq - Plottingdelay
Numdates = 4
denom = 1.0 / np.floa... | github_jupyter |
# Profiling PyTorch Multi GPU Single Node Training Job with Amazon SageMaker Debugger
This notebook will walk you through creating a PyTorch training job with the SageMaker Debugger profiling feature enabled. It will create a multi GPU single node training using Horovod.
## 1. Create a Training Job with Profiling Ena... | github_jupyter |
# 0 - Setup Notebook Pod
## 0.1 - Run in Jupyter Bash Terminal
```bash
# create application-default credentials
gcloud auth application-default login
```
# 1 - Initialize SparkSession
```
import pyspark
from pyspark.sql import SparkSession
# construct spark_jars list
spark_jars = ["https://storage.googleapis.com/ha... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
## VENRON-Electricity Dataset preprocessing for Fonduer
This script is used to pre-process the spreadsheets in order to apply the cell annotations from the prediction json files or the corresponding manually labeled annotation range sheet.
```
import os
import pandas as pd
import json
# First create xlsx output folde... | github_jupyter |
# Workshop DL01: Deep Neural Networks
## Agenda:
- Introduction to deep learning
- Apply DNN to MNIST dataset and IEEE fraud dataset
For this workshop we are gonna talk about deep learning algorithms and train DNN models with 2 datasets. We will first start with an easier dataset as demonstration, namely the MNIST ... | github_jupyter |
# Structured natural language processing with Pandas and spaCy (code)
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import spacy
nlp = spacy.load("en_core_web_sm")
sns.set_style("whitegrid")
from IPython.core.display import display, HTML
display(HTML("<style>.conta... | github_jupyter |
# Deep Dreams (with Caffe)
Credits: Forked from [DeepDream](https://github.com/google/deepdream) by Google
This notebook demonstrates how to use the [Caffe](http://caffe.berkeleyvision.org/) neural network framework to produce "dream" visuals shown in the [Google Research blog post](http://googleresearch.blogspot.ch/... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import ipyrad
import ipyrad.analysis as ipa
import ipcoal
import matplotlib.pyplot as plt
import msprime
import numpy as np
import toytree
import toyplot
print(ipyrad.__version__)
# Many loci
def simloci(sample_size_pop1=10,
sample_size_pop2=10,
get_pis=Fal... | github_jupyter |
# Instructions
To start, go to Kernal -> 'Restart and Run All' -> 'Restart and Run All Cells'
replace the wallet address in the cell below with the address you want to analyse
```
# Bearwhale wallet v2 : 9eyXNatnA6YM4tS1TjadEA6TFrd9bdufbFuykV89iX9vE9RBZZe
# v1 : 9hyDXH72HoNTiG2pvxFQwxAhWBU8CrbvwtJDtnYoa4jfpaSk1d3
ta... | github_jupyter |
# A Simple Autoencoder
We'll start off by building a simple autoencoder to compress the MNIST dataset. With autoencoders, we pass input data through an encoder that makes a compressed representation of the input. Then, this representation is passed through a decoder to reconstruct the input data. Generally the encoder... | github_jupyter |
# FLASK
-----------------------
Usaremos Python y una biblioteca llamada **Flask** para escribir nuestro propio servidor web, implementando funciones adicionales. Flask también es un **framework**, donde se establece un conjunto de convenciones para la utilización de sus librerias.
Por ejemplo, al igual que otras li... | github_jupyter |
# Union of MSigDB overlaps and DGIdb results
#### Overview
Aggregate the lists from step 5 into gene sets.
the genes\_, dgidb\_, and gsea\_ files generated by step5 no longer exist.
Instead, load the json from step 5 and generate those files in a tmpdir.
Then, run the R script, reading from a tmpdir,
and storing t... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Automated Ma... | github_jupyter |
# Goal
The aim of this notebook is to compare the mappability of profiles obtained with the different databases on CAMI challenge data
# Init
```
library(tidyverse)
library(stringr)
library(forcats)
library(cowplot)
library(data.table)
library(glue)
```
# Var
```
work_dir = "/ebio/abt3_projects/Struo/struo_benchma... | github_jupyter |
# Face Filter
By Joshua Franklin and Tiffany Phan.<br>
Created for the CSCI 4622 - Machine Learning final project.
```
import os
import io
import json
import requests
import numpy as np
import matplotlib.pyplot as plt
from typing import Tuple
from PIL import Image
from keras import Sequential
from keras.layers impor... | github_jupyter |
```
# Code to get landmarks from mediapipe and then create a bounding box around dip joints to segment them
# for further analysis
# Use distance transform to further correct the landmarks from mediapipe
# See if you can get tip indices from drawing a convex hull around hand??
# NOTE: Current code works on already ... | github_jupyter |
```
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
from six.moves import range
pickle_file = 'SVHN.pickle'
with open(pickle_file, 'rb') as f:
save = pickle.load(f)
#train_dataset = save['train_dataset']
train_labels = save['train_labels']
... | github_jupyter |
## Murine bone-marrow derived macrophages
https://data.broadinstitute.org/bbbc/BBBC020/
## Make a torch dataset
```
from segmentation.datasets import BroadDataset
```
### Show some images
```
%matplotlib inline
import matplotlib.pyplot as plt
#base = '/Users/nicholassofroniew/Documents/BBBC/BBBC020_v1/BBBC020_v1-c... | github_jupyter |
""" This notebook describes how one can use pickle library to save files so that the TensorFlow Kernel can be used with Desi condition_spectra function.
Saving files like this has two major advantages:
(1) It saves a lot of time as one does not have to run the condition_spectra function everytime before training th... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
```
import numpy as np
# most recent version is here: https://github.com/NSLS-II-LIX/pyXS
from pyxs import Data2D,Mask
from pyxs.ext import RQconv
def RotationMatrix(axis, angle):
if axis=='x' or axis=='X':
rot = np.asarray(
[[1., 0., 0.],
[0., np.cos(angle), -np.sin(angle)],
... | github_jupyter |
```
import numpy as np
from qiskit import Aer, IBMQ
from qiskit.utils import QuantumInstance
from qiskit.circuit import QuantumCircuit, ParameterVector
from qiskit.opflow import StateFn, Z, I, CircuitSampler, Gradient, Hessian
from qiskit.algorithms.optimizers import GradientDescent
import matplotlib.pyplot as plt
# Co... | github_jupyter |
```
import pandas as pd
import numpy as np
import csv
import networkx as nx
import matplotlib.pyplot as plt
import os
import sys
from scipy.stats import hypergeom
#Builiding-up INTERSECTION Interactome graph
intersect = pd.read_csv("intersection_interactome.tsv", sep = '\t')
G_int = nx.from_pandas_edgelist(intersect,... | github_jupyter |
# Read datasets
```
import pandas as pd
countries_of_the_world = pd.read_csv('../datasets/countries-of-the-world.csv')
countries_of_the_world.head()
mpg = pd.read_csv('../datasets/mpg.csv')
mpg.head()
student_data = pd.read_csv('../datasets/student-alcohol-consumption.csv')
student_data.head()
survey_data = pd.read_c... | github_jupyter |
```
import warnings
warnings.filterwarnings('ignore')
import random
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import glob
from PIL import Image
import cv2
%matplotlib inline
# tweaks for libraries
np.set_printoptions(precision=4, linewidth=1024, suppress=True)
plt.style.use('seaborn')
s... | github_jupyter |
### Generating `publications.json` partitions
This is a template notebook for generating metadata on publications - most importantly, the linkage between the publication and dataset (datasets are enumerated in `datasets.json`)
Process goes as follows:
1. Import CSV with publication-dataset linkages. Your csv should h... | github_jupyter |
```
# plotting libraries
import matplotlib
import matplotlib.pyplot as plt
# numpy (math) libary
import numpy as np
# Constants
n0 = 3.48 # standard refractive index
n2 = 5e-14 # [cm²/W] intensity-dependent refractive index
e0 = 8.85418782e-12 # vacuum permittivity epsilon_0
c0 = 299792458 # speed of light in vacuum c... | github_jupyter |
```
# Initialize Otter
import otter
grader = otter.Notebook()
```
# A 2016 Election Analysis
```
import numpy as np
from datascience import *
# These lines set up graphing capabilities.
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import warnings
warnings.simp... | github_jupyter |
# 8 Puzzle solver
* Parsa KamaliPour - 97149081
* In this repository we're going to solve this puzzle using $ A^* $ and $ IDA $
#### imports:
```
import copy
import pandas as pd
import numpy as np
import collections
import heapq
```
#### Test case 1:
```
input_puzzle_1 = [
[1, 2, 3],
[4, 0, 5],
[7, 8, ... | github_jupyter |
# Statistical Downscaling and Bias-Adjustment
`xclim` provides tools and utilities to ease the bias-adjustement process through its `xclim.sdba` module. Almost all adjustment algorithms conform to the `train` - `adjust` scheme, formalized within `TrainAdjust` classes. Given a reference time series (ref), historical si... | github_jupyter |
```
%matplotlib inline
```
Introduction to PyTorch
***********************
Introduction to Torch's tensor library
======================================
All of deep learning is computations on tensors, which are
generalizations of a matrix that can be indexed in more than 2
dimensions. We will see exactly what this... | github_jupyter |
# Lab 10.3.1 Visdom Example
**Jonathan Choi 2021**
**[Deep Learning By Torch] End to End study scripts of Deep Learning by implementing code practice with Pytorch.**
If you have an any issue, please PR below.
[[Deep Learning By Torch] - Github @JonyChoi](https://github.com/jonychoi/Deep-Learning-By-Torch)
Here, we ... | github_jupyter |
# Deep Learning & Art: Neural Style Transfer
Welcome to the second assignment of this week. In this assignment, you will learn about Neural Style Transfer. This algorithm was created by Gatys et al. (2015) (https://arxiv.org/abs/1508.06576).
**In this assignment, you will:**
- Implement the neural style transfer alg... | github_jupyter |
# Planet Data Collection
Using the Open Exoplanet Catalogue database: https://github.com/OpenExoplanetCatalogue/open_exoplanet_catalogue/
## Data License
Copyright (C) 2012 Hanno Rein
Permission is hereby granted, free of charge, to any person obtaining a copy of this database and associated scripts (the "Database"),... | github_jupyter |
# Fast Style Transfer with FastEstimator
In this notebook we will demonstrate how to do a neural image style transfer with perceptual loss as described in [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://cs.stanford.edu/people/jcjohns/papers/eccv16/JohnsonECCV16.pdf).
Typical neural style ... | github_jupyter |
# Introduction to Python
Ported to python from http://htmlpreview.github.io/?https://github.com/andrewpbray/oiLabs-base-R/blob/master/intro_to_r/intro_to_r.html
First, we need to import the libraries that we need. By convention, we apply aliases that we can use to reference the libraries later. `pandas` contains class... | github_jupyter |
# 1. Frame the Problem
- Descriptive
- Exploratory
- Inferential
# 2. Acquire the Data
> "Data is the new oil"
- Download from an internal system
- Obtained from client, or other 3rd party
- Extracted from a web-based API
- Scraped from a website
- Extracted from a PDF file
- Gathered manually and recorded
We wil... | github_jupyter |
# Getting started with TensorFlow (Eager Mode)
**Learning Objectives**
- Understand difference between Tensorflow's two modes: Eager Execution and Graph Execution
- Practice defining and performing basic operations on constant Tensors
- Use Tensorflow's automatic differentiation capability
## Introduction
**Eag... | github_jupyter |
## Deep Compressive Object Decoder (DCOD)
Implementation and proof of work.
```
import os
import random
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from network import deep_decoder
import tensorflow as tf
from tensorflow.keras import layers as ls, activations as acts
import... | github_jupyter |
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.nlp import *
from sklearn.linear_model import LogisticRegression
```
## IMDB dataset and the sentiment classification task
The [large movie review dataset](http://ai.stanford.edu/~amaas/data/sentiment/) contains a collection of 50,000 reviews fr... | github_jupyter |
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