text stringlengths 2.5k 6.39M | kind stringclasses 3
values |
|---|---|
# Demographic Data Analyzer
In this challenge you must analyze demographic data using Pandas. You are given a dataset of demographic data that was extracted from the 1994 Census database. Here is a sample of what the data looks like:
| | age | workclass | fnlwgt | education | education-num | marital... | github_jupyter |
# Deep Learning & Art: Neural Style Transfer
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 algorithm
- Generate novel artistic images using ... | github_jupyter |
```
from openpyxl import load_workbook
import requests.api
import warnings
from openpyxl import Workbook
import random
import re
from time import sleep
import urllib
import urllib3
import requests
from bs4 import BeautifulSoup
from collections import OrderedDict
forsvaingall=[]
urllib3.disable_warnings()
headers = {'Us... | github_jupyter |
# How to use Geogebra with Jupyter notebooks
[GeoGebra](geogebra.org) is a powerful tool for creating interactive classroom materials and visualizations that many teachers are familiar with. One limitation of GeoGebra is the distribution of these materials. While GeoGebra allows a teacher to export their material to t... | github_jupyter |
# Analysis of the London Rental Property Market
Analysis of the London rental property market based on all rental listings added to <a href="http://www.rightmove.co.uk" _target="blank">rightmove</a> in the last 24 hours.
```
# Setup:
import os, sys
sys.path.append(os.path.dirname(os.getcwd()))
from rightmove_webscrape... | github_jupyter |
# Analysis and visualization of 3D data in Python
Daniela Ushizima, Alexandre de Siqueira, Stéfan van der Walt
_BIDS @ University of California, Berkeley_
_Lawrence Berkeley National Laboratory - LBNL_
* Support material for the tutorial _Analysis and visualization of 3D data in Python_.
This tutorial will introdu... | github_jupyter |
# Reinforcement Learning Control Center Example
This notebook provides an example code for how to integrate the RL Control Center into an existing training pipeline. To learn more about the RL Control Center, read here: https://medium.com/p/4f27b134bb2a
For more reinforcment learning tutorials, see:
https://github.com... | github_jupyter |
## Chosen algorithm : FV MC Prediction
(In this case, first visit and every visit do not differ, as we have only one state action pair to visit at every episode start)
```
%matplotlib inline
import gym
import matplotlib
import numpy as np
import sys
from collections import ... | github_jupyter |
<center><img src="src/bqplot.svg" width="50%"></center>
# Repository: https://github.com/bloomberg/bqplot
# Installation:
`conda install -c conda-forge bqplot`
## Base plot
```
import numpy as np
import bqplot.pyplot as plt
x = np.linspace(0, 10, 20)
y = x**3
fig = plt.figure(animation_duration=1000)
scatter = plt.... | github_jupyter |
```
%matplotlib notebook
import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy as sp
import IPython
from IPython.display import display
import sklearn
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocess... | github_jupyter |
```
import sys
sys.path.append('..')
import torch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sympy import simplify_logic
from lens.utils.base import validate_network
from lens.utils.relu_nn import get_reduced_model, prune_features
from lens import logic
import lens
torch.manual_seed(0... | github_jupyter |
```
import numpy as np
from numpy import mean
from numpy import std
from matplotlib import pyplot
from sklearn.model_selection import KFold
from keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxP... | github_jupyter |
# Introduction to Feature Columns
**Learning Objectives**
1. Load a CSV file using [Pandas](https://pandas.pydata.org/)
2. Create an input pipeline using tf.data
3. Create multiple types of feature columns
## Introduction
In this notebook, you classify structured data (e.g. tabular data in a CSV file) using [f... | github_jupyter |
```
%matplotlib inline
#%matplotlib ipympl
%load_ext autoreload
%autoreload 2
from pylab import *
import pandas as pd
#Define a function to load the data
def load_data(start,end,ch,name="OKSeq",root="../data_hela/"):
#Start and end are in kb
#return x in kb and signalvalue
data = {"OKSeq":"OKSeq_5kb.cs... | github_jupyter |
# Practical session 1 - Some Python basics
Course: [SDIA-Python](https://github.com/guilgautier/sdia-python)
Dates: 09/21/2021-09/22/2021
Instructor: [Guillaume Gautier](https://guilgautier.github.io/)
Students (pair):
- [Student 1]([link](https://github.com/username1))
- [Student 2]([link](https://github.com/usern... | github_jupyter |
# Poisson HMM Demo
## Applying an HMM to electrophysiology data from a motor-control task
In this notebook, we'll show how SSM can be used for modeling neuroscience data. This notebook is based off the 2008 paper ["Detecting Neural-State Transitions Using Hidden Markov Models for Motor Cortical Prostheses"](https://we... | github_jupyter |
```
import numpy as np
import scipy.sparse as sp
import matplotlib.pyplot as plt
from SimPEG import Mesh, Utils, Solver
from scipy.constants import mu_0, epsilon_0
%matplotlib inline
```
# Sensitivity computuation for 1D magnetotelluric (MT) problem
## Purpose
With [SimPEG's](http://simpeg.xyz) mesh class, we disc... | github_jupyter |
# NHDPlusV1 Flowlines into Data Distillery Gc2
This code is in progress and is testing the use of Python to extract data from ScienceBase, add registration
information, and export data into Data Distillery Gc2.
General workflow involves:
1: Identify needed data in ScienceBase
2: Request data from ScienceBase by NHD... | github_jupyter |
# Visualisierung von Netzwerkgraphen
Bokeh unterstützt nativ die Erstellung von Netzwerkgraphen mit konfigurierbaren Interaktionen zwischen Kanten und Knoten.
## Edge- und Node-Renderer
Das Hauptmerkmal von `GraphRenderer` ist, dass es separate GlyphRenderer für Diagrammknoten und Diagrammkanten gibt. Dies ermöglic... | github_jupyter |
```
import pandas as pd
import tldextract
import numpy as np
pd.options.mode.chained_assignment = None
```
Read in the original dataset of 19K websites, as well as the 11K websites we retrieved product pages from.
```
web19k = pd.read_csv('../../data/final-list/shopping-english.csv')
web11k = pd.read_csv('../../data/... | github_jupyter |
# Settings
```
EXP_NO = 38
SEED = 1
N_SPLITS = 5
TARGET = 'target'
GROUP = 'art_series_id'
REGRESSION = False
assert((TARGET, REGRESSION) in (('target', True), ('target', False), ('sorting_date', True)))
CV_THRESHOLD = 0.80
PAST_EXPERIMENTS = tuple(exp_no for exp_no in range(4, 28 + 1)
# 7 は予測... | github_jupyter |
```
import pandas as pd
df = pd.read_csv('../results/mai_doc2vec_sim.csv')
df
import spacy
nlp = spacy.load('ja_ginza')
for p in nlp.pipeline:
print(p)
import textdistance
# text_pair = df.sort_values('SIM', ascending=False).iloc[203][['T2_Mai', 'T2_Maisho', 'SIM']].values.flatten()
text_pair = df.sample(1)[['T2_Ma... | github_jupyter |
# Exercise 20 - CostSensitive Churn
[paper](http://download.springer.com/static/pdf/125/art%253A10.1186%252Fs40165-015-0014-6.pdf?originUrl=http%3A%2F%2Fdecisionanalyticsjournal.springeropen.com%2Farticle%2F10.1186%2Fs40165-015-0014-6&token2=exp=1462974790~acl=%2Fstatic%2Fpdf%2F125%2Fart%25253A10.1186%25252Fs40165-015... | github_jupyter |
# Support Vector Regression with MinMaxScaler and QuantileTransformer
This Code template is for regression analysis using simple Support Vector Regressor(SVR) based on the Support Vector Machine algorithm with feature rescaling technique MinMaxScaler and feature transformation technique QuantileTransformer in a pipeli... | github_jupyter |
## https://pymoo.org/getting_started.html
```
!pip install pymoo==0.4.2.2
```
## Questions:
- What are reference directions / how do they work? https://pymoo.org/misc/reference_directions.html
```
import numpy as np
from pymoo.util.misc import stack
from pymoo.model.problem import Problem
class MyProblem(Problem):... | github_jupyter |
High level:
This notebook shows all the inconsistencies of field that were produced with dictionaries (and have hebrew in the name) with their respective numeric values for the markers_hebrew table.
The specific analysis below is based on data from 2020-01-13_views_and_main_tables folder from Jan 12, 2020 that can be ... | github_jupyter |
# Equivalent Layer technique for estimating magnetization direction of a magnetized source
#### Importing libraries
```
% matplotlib inline
import sys
import numpy as np
import matplotlib.pyplot as plt
import cPickle as pickle
import datetime
import timeit
from scipy.optimize import nnls
from fatiando.gridder import ... | github_jupyter |
To most investors, short selling is a shadowy, mysterious corner of the markets. Many do not make use of shorting - and I suspect a majority don't understand how to glean insights from trends in short selling activity.
Over the past several years, I've traded short about as often as long and have consequently learn... | github_jupyter |
# testing on wellcome images
We can now test our models' performance when transferred onto the Wellcome images dataset. In doing so, we'll get a better understanding of how well they generalise and which gaps in their knowledge we'll need to fill as we continue to modify them.
```
%matplotlib inline
import matplotlib.... | github_jupyter |
# Classifying Images with pre-built TF Container on Vertex AI
This notebook demonstrates how to implement different image models on MNIST using the [tf.keras API](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras).
## Learning Objectives
1. Understand how to build a Dense Neural Network (DNN) for imag... | github_jupyter |
```
import numpy as np
import urdf2casadi.urdfparser as u2c
from urdf2casadi.geometry import plucker
from urdf_parser_py.urdf import URDF, Pose
from timeit import Timer, timeit, repeat
import casadi as cs
def median(lst):
n = len(lst)
if n < 1:
return None
if n % 2 == 1:
return sort... | github_jupyter |
<a href="https://colab.research.google.com/github/anoushkrit/MOOCs/blob/master/TensorFlow-in-Practice/Introduction-to-Tensorflow/Horse_or_Human_NoValidation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!wget --no-check-certificate \
https... | github_jupyter |
# Course set-up
```
__author__ = "Christopher Potts"
__version__ = "CS224u, Stanford, Spring 2021"
```
This notebook covers the steps you'll need to take to get set up for [CS224u](http://web.stanford.edu/class/cs224u/).
## Contents
1. [Anaconda](#Anaconda)
1. [The course Github repository](#The-course-Github-repos... | github_jupyter |
# REINFORCE in lasagne
Just like we did before for q-learning, this time we'll design a lasagne network to learn `CartPole-v0` via policy gradient (REINFORCE).
Most of the code in this notebook is taken from approximate qlearning, so you'll find it more or less familiar and even simpler.
__Frameworks__ - we'll accep... | github_jupyter |
```
%matplotlib inline
from matplotlib import style
style.use('fivethirtyeight')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import datetime as dt
```
# Reflect Tables into SQLAlchemy ORM
```
# Python SQL toolkit and Object Relational Mapper
import sqlalchemy
from sqlalchemy.ext.automap imp... | github_jupyter |
```
!pip install dynet
!git clone https://github.com/neubig/nn4nlp-code.git
from __future__ import print_function
import time
from collections import defaultdict
import random
import math
import sys
import argparse
import dynet as dy
import numpy as np
#the parameters from mixer
NXENT = 40
NXER = 20
delta = 2
# form... | github_jupyter |
# Assignment 2 Key
```
import pandas as pd
import numpy as np
```
#### First let's load the dataset into dataframe df
```
df = pd.read_csv("train_set.csv")
```
#### Take a look at the dataset first and see how many variables it has.
```
df.head(5)
```
### 1. (1 pt.) Convert the numeric claim_amount target to a... | github_jupyter |
#Transformer
```
from google.colab import drive
drive.mount('/content/drive')
# informer, ARIMA, Prophet, LSTMa와는 다른 형식의 CSV를 사용한다.(Version2)
!pip install pandas
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Data/... | github_jupyter |
```
# default_exp uniformis
#hide
from nbdev.showdoc import *
#hide
# stellt sicher, dass beim verändern der core library diese wieder neu geladen wird
%load_ext autoreload
%autoreload 2
```
# Uniform IS
## Basic Settings
```
# imports
from bfh_mt_hs2020_sec_data.core import *
from pathlib import Path
from typing i... | github_jupyter |
# How to define a compartment population model in Compartor
$$
\def\n{\mathbf{n}}
\def\x{\mathbf{x}}
\def\N{\mathbb{\mathbb{N}}}
\def\X{\mathbb{X}}
\def\NX{\mathbb{\N_0^\X}}
\def\C{\mathcal{C}}
\def\Jc{\mathcal{J}_c}
\def\DM{\Delta M_{c,j}}
\newcommand\diff{\mathop{}\!\mathrm{d}}
\def\Xc{\mathbf{X}_c}
\def\Yc{\mathbf... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
from sklearn.cross_validation import train_test_split
import seaborn as sns
from itertools import combinations_with_replacement
sns.set()
df = pd.read_csv('TempLinkoping2016.csv')
df.head()
X = df.i... | github_jupyter |
<!-- dom:TITLE: Data Analysis and Machine Learning: Logistic Regression -->
# Data Analysis and Machine Learning: Logistic Regression
<!-- dom:AUTHOR: Morten Hjorth-Jensen at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State... | 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 |
# Part 2: Loading a saved model
__Before starting, we recommend you enable GPU acceleration if you're running on Colab. You'll also need to upload the weights you downloaded previously using the following block and using the upload button to upload your bettercnn.weights file:__
```
# Execute this code block to insta... | github_jupyter |
# 04. Preprocessing Racing Bib Numbers (RBNR) Dataset
### Purpose:
Create augmented images with annotations for the RBNR dataset, and then convert the annotations to the Darknet TXT format.
### Before Running Notebook:
1. Install the imgaug library for data augmentation. Augmentation code adapted from the imgaug doc... | github_jupyter |
# T1218.009 - Signed Binary Proxy Execution: Regsvcs/Regasm
Adversaries may abuse Regsvcs and Regasm to proxy execution of code through a trusted Windows utility. Regsvcs and Regasm are Windows command-line utilities that are used to register .NET [Component Object Model](https://attack.mitre.org/techniques/T1559/001) ... | github_jupyter |
```
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
from torch.optim.lr_scheduler import StepLR
! pip install torchsummary
from torchsummary import summar... | github_jupyter |
<a href="https://colab.research.google.com/github/Kabongosalomon/Secure-and-Private-AI-Scholarship-Challenge-from-Facebook/blob/master/Part_1_Tensors_in_PyTorch_(Exercises).ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Introduction to Deep Learn... | github_jupyter |
# Model 2: random forest
```
# Import libraries
import numpy as np
import pandas as pd
import itertools
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.metrics import con... | github_jupyter |
# S2 Fig. Classification accuracy for training and testing on individual feature groups.
```
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mpl # perhaps change this later
import matplotlib.pyplot as plt
import scipy.io as io
import os
import functions.model_reliance as mr
from sklea... | github_jupyter |
```
import os
import numpy as np
import cv2
import glob
import itertools
import datetime
import seaborn as sns
from matplotlib import pyplot as plt
import matplotlib.patches as patches
from sklearn.model_selection import train_test_split
from shutil import copy, copyfile
from keras.preprocessing.image import ImageDataG... | github_jupyter |
# Simple RidgeClassifier with QuantileTransformer
This Code template is for the Classification tasks using the simple RidgeClassifier and feature transformation technique QuantileTransformer in a pipeline
### Required Packages
```
!pip install imblearn
import warnings
import numpy as np
import pandas as pd
imp... | github_jupyter |
# Linear Mixed Effects Models
```
%matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf
```
**Note**: The R code and the results in this notebook has been converted to markdown so that R is not required to build the documents. The R results in th... | github_jupyter |
# This is a notebook implementing the multilingual BERT for NER classification on the DaNE dataset
```
# Loading packages
## Standard packages
import os
import math
import pandas as pd
import numpy as np
## pyTorch
import torch
import torch.nn.functional as F
from torch import nn
from torch.optim import Adam
from ... | github_jupyter |
# ab[x] toolkit: tutorial
The ab[x] toolkit contains three primary tools:
* **abstar**, which performs germline assignment and primary sequence annotation
* **abutils**, which provides programming primitives and commonly used functions like clustering and alignment
* **abcloud**, for launching, configuring and ma... | github_jupyter |
# Feature transformation with Amazon SageMaker Processing and SparkML
Typically a machine learning (ML) process consists of few steps. First, gathering data with various ETL jobs, then pre-processing the data, featurizing the dataset by incorporating standard techniques or prior knowledge, and finally training an ML m... | github_jupyter |
# Data and Empirics
In today's session we will start to explore how to actually work with data in an interesting way. At least, interesting to us economists.
We will start by finding some data and getting it ready to work with. Then, we will look at how to do a simple linear regression, before expanding that to mult... | github_jupyter |
Youtube Video Explanation : https://youtu.be/nwM9ScrFVEU
**Filter Method Types**
1. Basic Filter Methods
- VarianceThreshod (Remove the Constant Feature and Quasi-Constant Features)
- Remove Duplicate Features
2. Correlation & Ranking Filter Methods
- Pearson’s correlation coefficient
- Spearman’s rank... | github_jupyter |
# Dimensionality Reduction
Ideally, one would not need to extract or select feature in the input data. However, reducing the dimensionality as a separate pre-processing steps may be advantageous:
1. The complexity of the algorithm depends on the number of input dimensions and size of the data.
2. If some features are... | github_jupyter |
```
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from functools import reduce
plt.style.use('ggplot')
results = pd.read_csv("/Users/rob/proj/lt/gwgm/geowave-geomesa-comparative-analysis/analyze/gwgm-ca-run-results-Oct1.csv")
results[results.queryName.str.contains("TRACKS-US... | github_jupyter |
# Exporting data from BigQuery to Google Cloud Storage
In this notebook, we export BigQuery data to GCS so that we can reuse our Keras model that was developed on CSV data.
```
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
%pip install google-cloud-bigquery==1.25.0
```
Please ignore any incompat... | github_jupyter |
# Monitor System Bottlenecks and Profile Framework Operators using Amazon Debugger
This notebook provides an introduction to interactive analysis of the data captured by SageMaker Debugger.
## Table of Contents
* [1. Install and import the latest SageMaker Python SDK](#1)<br>
* [1.1. Import Debugger classes for ... | github_jupyter |
# Circuit Breakers with Seldon and Ambassador
This notebook shows how you can deploy Seldon Deployments which can have circuit breakers via Ambassador's circuit breakers configuration.
## Setup Seldon Core
Use the setup notebook to [Setup Cluster](https://docs.seldon.io/projects/seldon-core/en/latest/examples/seldon... | github_jupyter |
<a href="https://colab.research.google.com/github/AfrahAlharbi/ML_Week2/blob/main/D5_ML_Week2_Project.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#### **Group Members:**
* Nada Alzahrani
* Abeer Alghamdi
* Afrah Alharbi
```
# Import what ... | github_jupyter |
Should work if you have done git clone https://github.com/desihub/LSS.git and edited the part appending to the path or just made sure you are in LSS/Sandbox
```
import sys, os, glob, time
import numpy as np
import matplotlib.pyplot as plt
import fitsio
sys.path.append('../py') #this works if you are in the Sandbox dir... | github_jupyter |
# 光谱识别示例
本文件用于说明如何使用shining进行光谱识别
由于多进程原因，在jupyter中运行可能有问题，本文件以讲解使用方法为主，实际使用时请参考example1.py文件，cmd、pycharm中可以顺利运行，由于Spyder对多进程的支持问题，使用Spyder运行可能会出现某些print无法打印问题。
```
from shiningspectrum import pretreatment
from shiningspectrum import database
import os
import matplotlib.pyplot as plt
import numpy as np
from shinin... | github_jupyter |
# TV Script Generation
In this project, you'll generate your own [Simpsons](https://en.wikipedia.org/wiki/The_Simpsons) TV scripts using RNNs. You'll be using part of the [Simpsons dataset](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data) of scripts from 27 seasons. The Neural Network you'll build will gen... | github_jupyter |
```
import numpy as np
import itertools
import matplotlib.pyplot as plt
import seaborn as sns
import math
from numpy import genfromtxt
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import os
# os.environ["PATH"] += os.pathsep + '/usr/local/texlive/2019/bin/x86_64-darwin'
print(os.getenv("PATH"))... | github_jupyter |
# County-level earthquake risk maps
Several of our natural disasters are reported at the county level, so we'd like earthquake data to be available at the county level also. What we have is a USA-wide map with contours of earthquake risk, from [this source](https://geo.nyu.edu/catalog/stanford-rm034qp5477), and a map... | github_jupyter |
## Single cell RNA analysis
#### This notebook follows closely the excellent kallisto/bustool tutorials of the Pachter Lab. Please cite their work.
```
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import sys, collections, os, argparse
%matplotlib inline
from scipy.io import mmread
import pa... | github_jupyter |
```
# default_exp loops
```
# loops
> This module will include some useful interaction loops for types of RL agents. It'll be updated over time.
```
#hide
from nbdev import *
%nbdev_export
import gym
import numpy as np
from rl_bolts import buffers, env_wrappers, neuralnets
import torch
import torch.nn as nn
import t... | github_jupyter |
# 8 CLASSES AND OBJECT-ORIENTED PROGRAMMING
We now turn our attention to our major topic related to programming in Python: **using `classes` to organize programs around modules and data abstractions** in the context of **object-oriented programming.**
The key to <b>object-oriented programming</b> is thinking about <... | github_jupyter |
```
# Copyright 2021 Google LLC
# Use of this source code is governed by an MIT-style
# license that can be found in the LICENSE file or at
# https://opensource.org/licenses/MIT.
# Notebook authors: Kevin P. Murphy (murphyk@gmail.com)
# and Mahmoud Soliman (mjs@aucegypt.edu)
# This notebook reproduces figures for chap... | 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 in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file... | github_jupyter |
# Prediction of Porosity with SVM
```
# If you have installation questions, please reach out
import pandas as pd # data storage
import numpy as np # math and stuff
import sklearn
import datetime
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.model_... | github_jupyter |
# Import Libraries
```
import sys
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.decomposition import PCA
from sklearn import random_projection
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import fbeta_score, roc_curve, auc
from sklearn import svm
from s... | github_jupyter |
# Logistic Regression with a Neural Network mindset
Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize cats. This assignment will step you through how to do this with a Neural Network mindset, and will also hone your intuitions about deep learning.
*... | github_jupyter |
# demo.ipynb
基于完整的历史数据，获得低比例饰品池的较优筛选规则
输入：饰品的 `buff_meta` 字段
输出：是否将饰品加入池中 (True / False)
## 读取数据集
```
import os, json
from tqdm import tqdm
import pandas as pd
import numpy as np
dataset = []
for index in tqdm(range(20)):
with open('data_{}.json'.format(index), 'r', encoding='utf-8') as f:
dataset.ex... | github_jupyter |
```
import panel as pn
pn.extension()
```
The ``FileDownload`` widget allows downloading a file on the frontend by sending the file data to the browser either on initialization (if ``embed=True``) or when the button is clicked.
For more information about listening to widget events and laying out widgets refer to the ... | github_jupyter |
# <center>MobileNet - Pytorch
# Step 1: Prepare data
```
# MobileNet-Pytorch
import argparse
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR
from torchvision import datasets, transforms
from torch.autograd i... | github_jupyter |
```
import pandas as pd
import numpy as np
import logging
isTest = False
N = '200'
dataType = 'best'
pathTrain = "data/BEST&MOST" + N + "/train-" + dataType + N + ".arff"
pathDev = "data/BEST&MOST" + N + "/dev-" + dataType + N + ".arff"
pathTest = "data/BEST&MOST" + N + "/test-" + dataType + N + ".arff"
txtPath = "m... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import random
def seed_everything(seed=2020):
random.seed(seed)
np.random.seed(seed)
seed_everything(42)
warnings.filterwarnings("ignore")
%matplotlib inline
data = pd.read_csv("../../data/plasmaetc... | github_jupyter |
# Load & save dataset features
- Load the metadata of the FMA dataset
- Keep only tracks of specified genres
- Keep only tracks with top popularity
- Create the adjacency matrix
- Keep the biggest connected component if not fully connected
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
imp... | github_jupyter |
# RidgeRegression with Quantile Transformer
This code template is for the regression analysis using Ridge Regression and feature rescaling technique called Quantile Transformer
### Required Packages
```
import warnings
import numpy as np
import pandas as pd
import seaborn as se
import matplotlib.pyplot as plt
fr... | github_jupyter |
```
!pip install torch
import torch
print(torch.__version__)
!pip install gym pyvirtualdisplay > /dev/null 2>&1
!apt-get install -y xvfb python-opengl ffmpeg > /dev/null 2>&1
!apt-get update && apt-get install -y cmake libopenmpi-dev python3-dev zlib1g-dev
!apt-get install -y libxrender-dev
import gym
print(gym.__ve... | github_jupyter |
```
import os
from astropy.io import fits
from astropy.wcs import WCS
from astropy.modeling import models, fitting
import numpy as np
from scipy import optimize
from matplotlib import pyplot as plt
%matplotlib inline
#%matplotlib notebook
import aplpy
```
<h2> Open data file
```
home = os.path.expanduser("~")
fitsdi... | github_jupyter |
# Tutorial 1: Basics about Jupyter Notebooks
You only need to look at this tutorial if you are new to Jupyter Notebooks.
This page (and its file, `01_notebook_basics.ipynb`) is termed a *notebook*. Each notebook when opened in Jupyter, is a Python main module that can do anything a normal Python module can do. Specif... | github_jupyter |
# Travelling Salesman Problem (TSP)
If we have a list of city and distance between cities, travelling salesman problem is to find out the least sum of the distance visiting all the cities only once.
<img src="https://user-images.githubusercontent.com/5043340/45661145-2f8a7a80-bb37-11e8-99d1-42368906cfff.png" width="4... | github_jupyter |
<a href="https://colab.research.google.com/github/joaochenriques/MCTE_2022/blob/main/ChannelFlows/HystogramsPowerProduction/HeierTurbineModel.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import numpy as np
import matplotlib.pyplot as mpl
impo... | github_jupyter |
# Autoname
Problem:
1. In GDS different cells must have different names. Relying on the incrementals
naming convention can be dangerous when you merge masks that have different
cells build at different run times or if you Klayout for merging masks.
2. In GDS two cells cannot have the same name.
Solution: The decorat... | github_jupyter |
```
import os
import numpy as np
import copy
import time
import matplotlib.pyplot as plt
import scipy.stats as st
%matplotlib inline
import seaborn as sns
sns.set(style="ticks")
from curbside_models import *
```
## Initialization
```
Q = 3 # Maximum number of spaces at each candidate location
B = 200000 # ($) budge... | github_jupyter |
# Character Classification
This notebook contains all steps of OCR
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
import cv2
# Import Widgets
from ipywidgets import Button, Text, IntSlider, interact
from IPython.display import display, clear_output
# Import costume... | github_jupyter |
# Lists and Loops
This notebook is based on materials kindly provided by the [IN1900]( https://www.uio.no/studier/emner/matnat/ifi/IN1900/h19/) team.
## Lists
Python lists can contain `int`, `float`, `String` and other items.
We make a list by placing the items in square brackets, `[]`, separated by commas.
```
my_... | github_jupyter |
---
# Introduction to Matplotlib
---
# 1. Matplotlib in the Wild
A powerful plotting library that can generate a [wide range of plot types](https://matplotlib.org/stable/tutorials/introductory/sample_plots.html#sphx-glr-tutorials-introductory-sample-plots-py).
In this tutorial, we focus only on x/y plots.
## 1.1 ... | github_jupyter |
# Regulome Explorer Notebook
This notebook computes association scores between numerical features (Gene expression and Somatic copy number) of a list of genes and other features available in TCGA BigQuery tables. The specific statistical tests used between the features are described in the following link: https://git... | github_jupyter |
# Correlation analysis between the Cardano currency and Twitter
This project consists of a correlation analysis between the Cardano currency and tweets. In order to define the positiveness of a tweet (if the course of the cardano will go up or down), we realise a sentiment analysis of each tweet using the VADER algori... | github_jupyter |
<a href="https://colab.research.google.com/github/google/applied-machine-learning-intensive/blob/master/content/05_deep_learning/01_recurrent_neural_networks/colab.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#### Copyright 2020 Google LLC.
```
... | github_jupyter |
## Basic training functionality
```
from fastai.basic_train import *
from fastai.gen_doc.nbdoc import *
from fastai.vision import *
from fastai.distributed import *
```
[`basic_train`](/basic_train.html#basic_train) wraps together the data (in a [`DataBunch`](/basic_data.html#DataBunch) object) with a PyTorch model t... | github_jupyter |
# Fully-Connected Neural Nets
In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
import re
dimension = 400
vocab = "EOS abcdefghijklmnopqrstuvwxyz'"
char2idx = {char: idx for idx, char in enumerate(vocab)}
idx2char = {idx: char for idx, char in enumerate(vocab)}
def text2idx(text):
text = re.sub(r'[^a-z ]', '', text.lower()).strip()
c... | github_jupyter |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.