text stringlengths 2.5k 6.39M | kind stringclasses 3
values |
|---|---|
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
from sklearn.preprocessing import StandardScaler
from sklearn.mixture import GaussianMixture
from sklearn.decomposition import PCA
from sklearn import datasets, metrics
heart_disease = pd.read_excel('Proces... | github_jupyter |
```
# UN_Geosheme_Subregion = ['Australia and New Zealand','Caribbean','Central America','Central Asia','Eastern Africa','Eastern Asia','Eastern Europe','Melanesia','Micronesia','Middle Africa','Northern Africa','Northern America','Northern Europe','Polynesia','South America','South-Eastern Asia','Southern Africa','Sou... | github_jupyter |
# Challenge 2 - Padlock Secret
**Difficulty level**: 3 - beginner
One approach to find a password or a padlock secret combination is to use a brute force attack. Of course, for a small combination it is not a big deal, but for complex combination it could be almost impossible using the current computation power.
Her... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
This notebook discusses solutions for the exercises in the _crash course_.
## 01 Ray Crash Course - Tasks - Exercise 1
As currently written, the memory footprint of `estimate... | github_jupyter |
```
from fastai2.vision.all import *
```
# Checar el VerboseCallback
```
from fastai2.test_utils import VerboseCallback
class VerboseCallback(Callback):
"Callback that prints the name of each event called"
def __call__(self, event_name):
print(event_name)
super().__call__(event_name)
```
# Cr... | github_jupyter |
# Answers: Classes
Provided here are answers to the practice questions at the end of "Classes".
## Objects
**Objects Q1**.
```
# specific strings will differ
true_var = 'asdf123'.isalnum()
false_var = '!!!!'.isalnum()
```
**Objects Q2**.
```
days_summary = {}
for day in days_of_week:
days_summary[day] = site... | github_jupyter |
## Keypad Combinations
A keypad on a cellphone has alphabets for all numbers between 2 and 9, as shown in the figure below:
<img style="float: center;height:200px;" src="Keypad.png" alt="A cell phone keypad that has letters associated with each number 2 through 9"><br>
You can make different combinations of alphabet... | github_jupyter |
# 三星级复现项目：使用DDPG解决四轴飞行器速度控制
（这可能是史上最“偷懒”的三星级复现项目，改改任务环境就可以提交了 - -！应该没有更懒的了，O(∩_∩)O哈哈~）
# Step1 安装依赖
!pip uninstall -y parl # 说明：AIStudio预装的parl版本太老，容易跟其他库产生兼容性冲突，建议先卸载
!pip uninstall -y pandas scikit-learn # 提示：在AIStudio中卸载这两个库再import parl可避免warning提示，不卸载也不影响parl的使用
```
!pip uninstall -y parl # 说明：AIStudio预装的parl... | github_jupyter |
```
# python 3.6.8
# DLISIO v0.3.5
# numpy v1.16.2
# pandas v0.24.1
# lasio v0.25.1
from dlisio import lis
import pandas as pd
import os
import lasio
import numpy as np
def extract_wellname(f, find_wellname, manualwellname):
if find_wellname == "Yes":
records = f.wellsite_data()
inforec = records[0... | github_jupyter |
# SYS 611: Dice Fighters Example (w/ Binomial Process Gen.)
Paul T. Grogan <pgrogan@stevens.edu>
This example shows how to model the dice fighters example in Python using a binomial process generator.
## Dependencies
This example is compatible with Python 2 environments through use of the `__future__` library funct... | github_jupyter |
# Sampled Softmax
For classification and prediction problems a typical criterion function is cross-entropy with softmax. If the number of output classes is high the computation of this criterion and the corresponding gradients could be quite costly. Sampled Softmax is a heuristic to speed up training in these cases. (... | github_jupyter |
### Data Source
Dataset is derived from Fannie Mae’s [Single-Family Loan Performance Data](http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html) with all rights reserved by Fannie Mae. This processed dataset is redistributed with permission and consent from Fannie Mae. For the full raw da... | github_jupyter |
### Univariate linear regression using gradient descent
```
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
%matplotlib inline
data_train = np.zeros((2,20))
data_train[0] = [4, 5, 5, 7, 8, 8, 9, 11, 11, 12, 13, 14, ... | 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 |
# Using BagIt to tag oceanographic data
[`BagIt`](https://en.wikipedia.org/wiki/BagIt) is a packaging format that supports storage of arbitrary digital content. The "bag" consists of arbitrary content and "tags," the metadata files. `BagIt` packages can be used to facilitate data sharing with federal archive centers ... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Filter/filter_in_list.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" href="... | github_jupyter |
# Adversarial-Robustness-Toolbox for scikit-learn AdaBoostClassifier
```
from sklearn.ensemble import AdaBoostClassifier
from sklearn.datasets import load_iris
import numpy as np
from matplotlib import pyplot as plt
from art.estimators.classification import SklearnClassifier
from art.attacks.evasion import ZooAttack... | github_jupyter |
```
import os
import sys
import numpy as np
import PIL.Image
import torch
import torchvision
sys.path. append('../icnn_torch')
from icnn import reconstruct_stim
from utils import normalise_img, img_preprocess,img_deprocess, get_cnn_features
#load CNN model from torchvision
#net = torchvision.models.resnet50(pretraine... | github_jupyter |
```
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as tick
%matplotlib inline
model = 'Shake-ResNet-26 2x64d (Shake-Shake-Image)'
log_dir = os.path.join('results', model)
df = pd.read_json(os.path.join(log_dir, 'log'))
df.rename(columns={
'epoch': 'Epoch'... | github_jupyter |
<a href="https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/word_analogies_torch.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Solving word analogies using pre-trained word embeddings
Based on D2L 14.7
http://d2l.a... | github_jupyter |
# Napelemek temelésének előrejelzése gépi tanulási algoritmusok segítségével
## A feladat
`count félrevezet; cov,corr INF-et ad; quantile-t nem tudom használni,mint nyugodtan kivehetem mert úgyis nulla?
```
import math
import pandas as pd
import numpy as np
PATH_TO_TRAIN = '../data/raw/train15.csv'
DATE_FORMAT = '%Y%... | github_jupyter |
<!--BOOK_INFORMATION-->
<a href="https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv" target="_blank"><img align="left" src="data/cover.jpg" style="width: 76px; height: 100px; background: white; padding: 1px; border: 1px solid black; margin-right:10px;"></a>
*This notebook contains an ex... | github_jupyter |
# Stocks Analysis Demo
```
!/User/align_mlrun.sh
```
## Setup stocks project
```
from os import path
import os
import mlrun
# Set the base project name
project_name_base = 'stocks'
# Initialize the MLRun environment and save the project name and artifacts path
project_name, artifact_path = mlrun.set_environment(pro... | github_jupyter |
# Train a Smartcab to Drive
Goal: Construct an optimized Q-Learning driving agent that will navigate a Smartcab through its ideal environment towards a destination - without sacrificing on safety or reliability. Both of the evaluation metric is measured using a letter-grade system as follows:
| Grade | Safety | Rel... | github_jupyter |
```
import sys, os, glob
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import logging
from scipy.signal import find_peaks
from scipy.interpolate import UnivariateSpline, interp1d
from scipy import stats
from statsmodels.stats.multicomp import pairwise_tuk... | github_jupyter |
# Translation simple ecoder-decocer over the b3 dataset
```
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchtext import data
import pandas as pd
import unicodedata
import string
import re
import random
import copy
from contra_qa.plots.functions import simple_step_plot
i... | github_jupyter |
```
# Running %env without any arguments
# lists all environment variables
# The line below sets the environment
# variable CUDA_VISIBLE_DEVICES
%env CUDA_VISIBLE_DEVICES =
import numpy as np
from datetime import datetime
import pandas as pd
import io
import time
import bson # this is installed... | github_jupyter |
# Make sure this SageMakerNotebookExecutionRole has access to Kendra
```
import boto3
import sagemaker
import pandas as pd
sess = sagemaker.Session()
bucket = sess.default_bucket()
role = sagemaker.get_execution_role()
region = boto3.Session().region_name
sm = boto3.Session().client(service_name='sagemaker', regio... | github_jupyter |
# Working with time series data
```
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.options.display.max_rows = 8
```
## Case study: air quality data of European monitoring stations (AirBase)
[AirBase](http://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-q... | github_jupyter |
# Simple Naive Bayes Classifier
## T1. Load a dataset
The following code loads a dataset consisting of text messages and spam-ham labels.
```
from typing import List, Tuple, Dict, Iterable, Set
from collections import defaultdict
import re
import math
import pandas as pd
url = 'https://raw.githubusercontent.com/mle... | github_jupyter |
<a href="https://colab.research.google.com/github/yanin2020/Curso-de-Python/blob/master/Phyton_curso.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
[CURSO FRECODECAMP](https://www.youtube.com/watch?v=DLikpfc64cA&ab_channel=freeCodeCampEspa%C3%B1ol)... | github_jupyter |
# Module 1: **Data Science - Basic Data Understanding**
Course website: [SHALA-2020](https://shala2020.github.io/)
Instructors: Sudhakar Kumar, Rishav Arjun, and Sahar Nasser
---
## Plotting mathematical functions
---
```
# Loading the libraries
import pandas as pd
import numpy as np
import seaborn as sns
import... | github_jupyter |
> This is one of the 100 recipes of the [IPython Cookbook](http://ipython-books.github.io/), the definitive guide to high-performance scientific computing and data science in Python.
# 4.7. Implementing an efficient rolling average algorithm with stride tricks
Stride tricks can be useful for local computations on arr... | github_jupyter |
```
import pandas as pd
from pybatfish.client.commands import *
from pybatfish.datamodel import *
from pybatfish.question import bfq, list_questions, load_questions
pd.set_option("display.width", 300)
pd.set_option("display.max_columns", 20)
pd.set_option("display.max_rows", 1000)
pd.set_option("display.max_colwidt... | github_jupyter |
```
library(caret, quiet=TRUE);
library(base64enc)
library(httr, quiet=TRUE)
```
# Build a Model
```
set.seed(1960)
create_model = function() {
model <- train(Species ~ ., data = iris, method = "rpart" , preProcess = c("expoTrans"))
return(model)
}
# dataset
model = create_model()
pred <- predict(mo... | github_jupyter |
```
#hide
%load_ext autoreload
%autoreload 2
# default_exp latent_factor_fxns
```
# Latent Factor Functions
> This module contains the update and forecast functions to work with a latent factor DGLM. There are two sets of functions: The first works with the latent_factor class in PyBATS, which represents latent facto... | github_jupyter |
```
from ei_net import * # import the .py file but you can find all the functions at the bottom of this notebook
from utilities import show_values
import matplotlib.pyplot as plt
%matplotlib inline
##########################################
############ PLOTTING SETUP ##############
EI_cmap = "Greys"
where_to_save_pngs... | github_jupyter |
# Source Code
### Libraries :-
**Selenium Driver**
```
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support.ui import Select
from selenium.common.exceptions import NoSuchElementException
```
**Speech**
```
import pyttsx3 #It works offline
from gtts import g... | github_jupyter |
# Investigate Web Application Firewall (WAF) Data </br>
**Author:** Vani Asawa <br/>
**Date:** December 2020 </br>
**Notebook Version:** 1.0 <br/>
**Python Version:** Python 3.6 <br/>
**Required Packages:** msticpy, pandas, kqlmagic <br/>
**Data Sources Required:** WAF data (AzureDiagnostics) <br/>
## What is the pur... | github_jupyter |
```
# Comparing fiTQun's results with the fully supervised ResNet-18 classifier on the varying position dataset
# Naming convention: first particle type is which file it is from, last particletype is what the hypothesis is
## Imports
import sys
import os
import time
import math
import random
import pdb
import h5py
#... | github_jupyter |
## Conditional Independence
Two random variable $X$ and $Y$ are conditiaonly independent given $Z$, denoted by $X \perp \!\! \perp Y \mid Z$ if
$$p_{X,Y\mid Z} (x,y\mid z) = p_{X\mid Z}(x\mid z) \, p_{Y\mid Z}(y\mid z)$$
In general Marginal independence doesn't imply conditional independence and vice versa.
### Ex... | github_jupyter |
```
#default_exp eda
#hide
import transformers
import torch
import torch.nn as nn
import torch.optim as optim
import pandas as pd
import Hasoc.config as config
import Hasoc.utils.utils as utils
import Hasoc.utils.engine as engine
import Hasoc.model.model as model
import Hasoc.dataset.dataset as dataset
from functool... | github_jupyter |
# Topic Modeling wiht Latent Semantic Analysis
Latent Semantic Analysis (LSA) is a method for reducing the dimnesionality of documents treated as a bag of words. It is used for document classification, clustering and retrieval. For example, LSA can be used to search for prior art given a new patent application. In thi... | github_jupyter |
# Transfer Learning experiments
```
import os
import torch
import mlflow
import numpy as np
from torch import nn
from torch import optim
from collections import OrderedDict
import torch.nn.functional as F
from torchvision import datasets, transforms, models
```
## Transfer Learning with DenseNet
### Loading data
``... | github_jupyter |
<a href="https://colab.research.google.com/github/wileyw/DeepLearningDemos/blob/master/sound/simple_audio_working_vggish_dataset.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
##### Copyright 2020 The TensorFlow Authors.
```
#@title Licensed under... | github_jupyter |
## CNN WITH CLASSES FROM [HERE](https://github.com/hunkim/DeepLearningZeroToAll/blob/master/lab-10-6-mnist_nn_batchnorm.ipynb)
```
import os
import numpy as np
from scipy import ndimage
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.examples.tutorials.mni... | github_jupyter |
```
import os
import pandas as pd
import numpy as np
pd.options.display.max_rows = 500
pd.options.display.max_columns = 500
from matplotlib import pyplot as plt
%matplotlib inline
# Vega lite spec builders
import vincent
import altair
import vega
help(vega)
def replace_misspelled(region_name, misspelled, replace):
... | github_jupyter |
# Programación lineal, algoritmo símplex
## Introducción
El método de programación lineal ha sido un método sumamente utilizado para matemática avanzada y ciencias avanzadas,
brindando solución a problemas de máximos y mínmos, ya que este algoritmo nos presenta distintos métodos de solución,
siendo el más utilizado e... | github_jupyter |
```
import pickle
import pandas as pd
import os
import json
import glob
import numpy as np
from optimizers.utils import Model, Architecture
from nasbench_analysis.search_spaces.search_space_1 import SearchSpace1
from nasbench_analysis.search_spaces.search_space_2 import SearchSpace2
from nasbench_analysis.search_spac... | github_jupyter |
Lambda School Data Science, Unit 2: Predictive Modeling
# Regression & Classification, Module 1
## Objectives
- Clean data & remove outliers
- Use scikit-learn for linear regression
- Organize & comment code
## Setup
#### If you're using [Anaconda](https://www.anaconda.com/distribution/) locally
Install required P... | github_jupyter |
```
# Useful for debugging
%load_ext autoreload
%autoreload 2
# Nicer plotting
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
matplotlib.rcParams['figure.figsize'] = (8,4)
```
# Autophase and Autophase and Scale examples
```
from impact import Impac... | github_jupyter |
# Introduction: Home Credit Default Risk Competition
This notebook is intended for those who are new to machine learning competitions or want a gentle introduction to the problem. I purposely avoid jumping into complicated models or joining together lots of data in order to show the basics of how to get started in mac... | github_jupyter |
<a href="https://colab.research.google.com/github/mbonyani/Spine_Segmentation/blob/main/step2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!ls -lha kaggle.json
!pip install -q kaggle
!mkdir -p ~/.kaggle
!cp kaggle.json ~/.kaggle/
!chmod ... | github_jupyter |
---
# __Python Pandas__
Data Structures, Inspection, Cleanign, Indexing, Slicing, merging, concatenating
---
Code examples on the most frequently used functions - Collected, Created and Edited by __Pawel Rosikiewicz__ www.SimpleAI.ch
## CONTENT
* __CREAETING SERIES & DATA FRAME__
</br>
* __LOADING/... | github_jupyter |
```
%tensorflow_version 1.x
# Clone git
%rm -rf archlectures
!git clone https://github.com/armaank/archlectures
%cd archlectures/generative/
%%sh
chmod 755 get_models.sh
./get_models.sh
from IPython.display import Javascript
display(Javascript('''google.colab.output.setIframeHeight(0, true, {maxHeight: 200})'''))
!pip ... | github_jupyter |
```
"""
You can run either this notebook locally (if you have all the dependencies and a GPU) or on Google Colab.
Instructions for setting up Colab are as follows:
1. Open a new Python 3 notebook.
2. Import this notebook from GitHub (File -> Upload Notebook -> "GITHUB" tab -> copy/paste GitHub URL)
3. Connect to an in... | github_jupyter |
```
# Copyright 2021 Google LLC
#
# 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 agreed to in writi... | github_jupyter |
## Make 3d model sections
```
import telluricpy, numpy as np, gc
import scipy
import VTKUtil as pvtkUtil
%matplotlib qt
def simpeg2vtk(mesh,modDict):
from vtk import vtkRectilinearGrid as rectGrid, vtkXMLRectilinearGridWriter as rectWriter, VTK_VERSION
from vtk.util.numpy_support import numpy_to_vtk
... | github_jupyter |
# Inference and Validation
Now that you have a trained network, you can use it for making predictions. This is typically called **inference**, a term borrowed from statistics. However, neural networks have a tendency to perform *too well* on the training data and aren't able to generalize to data that hasn't been seen... | github_jupyter |
<a href="https://colab.research.google.com/github/Tessellate-Imaging/monk_v1/blob/master/study_roadmaps/2_transfer_learning_roadmap/5_exploring_model_families/4_resnet/8)%20Comparing%20resnet%20v1%20and%20v2%20variants%20-%20mxnet%20backend.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/cola... | github_jupyter |
```
from PIL import Image
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import torch
import numpy as np
import cv2
from samples.CLS2IDX import CLS2IDX
```
# Auxiliary Functions
```
from baselines.ViT.LVViT_LRP import lvvit_small_patch16_224 as vit_LRP
from baselines.ViT.ViT_explanation_g... | github_jupyter |
# Demo for 2d DOT
```
import chainer
from chainer import Variable, optimizers, serializers, utils
from chainer import Link, Chain, ChainList
import chainer.functions as F
import chainer.links as L
from chainer import cuda
#import numpy as xp
gpu_device = 0
cuda.get_device(gpu_device).use()
import numpy as np
import ... | github_jupyter |
```
import sys
sys.path.append('../../pyutils')
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import metrics
import utils
```
# Bernoulli Distribution
$$X \sim B(p)$$
$X$ is a single binary random variable.
Parameters:
- $p \in [0, 1]$: probability that X takes the value $1$
$$P(X=0) = 1... | github_jupyter |
### Gluon Implementation in Recurrent Neural Networks
```
import sys
sys.path.insert(0, '..')
import d2l
import math
from mxnet import autograd, gluon, init, nd
from mxnet.gluon import loss as gloss, nn, rnn
import time
(corpus_indices, char_to_idx, idx_to_char,
vocab_size) = d2l.load_data_time_machine()
```
### D... | github_jupyter |
```
import os
os.chdir('/home/enis/projects/nna/src/nna/exp/megan/run-2/')
# import run
# import nna
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
import torchaudio
torchaudio.set_audio_backend("sox_io")
import numpy as np
from pathlib import Path
from collecti... | github_jupyter |
# bqplot https://github.com/bloomberg/bqplot
## A Jupyter - d3.js bridge
bqplot is a jupyter interactive widget library bringing d3.js visualization to the Jupyter notebook.
- Apache Licensed
bqplot implements the abstractions of Wilkinson’s “The Grammar of Graphics” as interactive Jupyter widgets.
bqplot provides... | github_jupyter |
# Stochastic gradient descent (SGD)
SGD is an incremental gradient descent algorithm which modifies its weights, in an effort to reach a local minimum.
The cuML implementation takes only numpy arrays and cuDF datasets as inputs.
- In order to convert your dataset into a cuDF dataframe format please refer the [cuD... | github_jupyter |
# Unit 4: Neighborhood-based Collaborative Filtering for Rating Prediction
In this section we generate personalized recommendations for the first time. We exploit rating similarities among users and items to identify similar users and items that assist in finding the relevant items to recommend for each user.
This de... | github_jupyter |
```
import pandas as pd
%matplotlib inline
players = pd.read_csv('players.csv')
matches = pd.read_csv('match.csv')
heroes = pd.read_csv('hero_names.csv')
items = pd.read_csv('item_ids.csv')
items.info()
hero_lookup = dict(zip(heroes['hero_id'], heroes['localized_name']))
hero_lookup[0] = 'Unknown'
players['her... | github_jupyter |
```
BRANCH = 'v1.0.2'
"""
You can run either this notebook locally (if you have all the dependencies and a GPU) or on Google Colab.
Instructions for setting up Colab are as follows:
1. Open a new Python 3 notebook.
2. Import this notebook from GitHub (File -> Upload Notebook -> "GITHUB" tab -> copy/paste GitHub URL)
3... | github_jupyter |
# The Spinning Effective One-Body Initial Condition Solver
## Author: Tyler Knowles
## This module documents the reduced spinning effective one-body initial condition solver as numerically implemented in LALSuite's SEOBNRv3 gravitational waveform approximant. That is, we follow Section IV A of [Buonanno, Chen, and D... | github_jupyter |
## Analysis of Gene Expression Data via Arrays using Bioconductor/R - I
Today, this notebook constitutes your in-class activity and homework. Over the next 3 days, we will be constructing your own gene expression analysis pipeline, using available tools in R, and available data from the gene expression omnibus (GEO): ... | github_jupyter |
postcode strings can be converted to the following formats via the `output_format` parameter:
* `compact`: only number strings without any seperators or whitespace, like "2611ET"
* `standard`: postcode strings with proper whitespace in the proper places. Note that in the case of postcode, the compact format is the sam... | github_jupyter |
# Business and Data Understanding
## Airports Weather Data 2016
### Import Airports and their latitude/longitude. 10 US airports with the most weather related delays
```
from pyspark.sql import SQLContext
import numpy as np
from io import StringIO
import requests
import json
import pandas as pd
# @hidden_cell
# Th... | github_jupyter |
```
import bert
from bert import run_classifier
from bert import optimization
from bert import tokenization
from bert import modeling
import numpy as np
import json
import tensorflow as tf
import itertools
from unidecode import unidecode
import re
import sentencepiece as spm
# !git clone https://github.com/huseinzol05/... | github_jupyter |
```
import re
import numpy as np
import pickle
import import_ipynb
import import_ipynb
from normalizing import normalize
from gensim.models.keyedvectors import KeyedVectors
from gensim.test.utils import get_tmpfile
from gensim.scripts.glove2word2vec import glove2word2vec
import collections
from collections import de... | github_jupyter |
## Importance Sampling and Particle filter
```
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
from scipy.stats import poisson
```
## Importance Sampling and resampling
Before we dive into the vast universe of nonlinear filtering, let us take a step back and review importance sampling... | github_jupyter |
```
#default_exp test
#export
from fastcore.imports import *
from collections import Counter
from contextlib import redirect_stdout
from nbdev.showdoc import *
from fastcore.nb_imports import *
```
# Test
> Helper functions to quickly write tests in notebooks
## Simple test functions
We can check that code raises a... | github_jupyter |
## <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Confidence-Intervals" data-toc-modified-id="Confidence-Intervals-1"><span class="toc-item-num">1 </span>Confidence Intervals</a></span><ul class="toc-item"><li><span><a href="#Agenda" data-toc... | github_jupyter |
# main function for decomposition
### Author: Yiming Fang
```
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from torch.optim.lr_scheduler import StepLR
import torchvision
import torchvision.transforms as transforms
from torchvision ... | github_jupyter |
```
from urllib.request import Request, urlopen
import urllib
import requests
import pandas as pd
from xlwt import Workbook
from bs4 import BeautifulSoup
import sys
import time
import random
url_list = ["https://www.google.com/search?q=Aachen+Hbf",
"https://www.google.com/search?q=Aalen+Hbf",
"https://www.google.com/s... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import sys
#sys.path.insert(1, '/home/ximo/Documents/GitHub/skforecast')
%config Completer.use_jedi = False
# Libraries
# ==============================================================================
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sk... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import nlppln
with nlppln.WorkflowGenerator(working_dir='/home/jvdzwaan/cwl-working-dir/') as wf:
wf.load(steps_dir='../ochre/cwl/')
print wf.list_steps()
in_dir = wf.add_input(in_dir='Directory')
ocr_dir_name = wf.add_input(ocr_dir_name='string')
gs_dir_name... | github_jupyter |
# Joining all processed data
This notebook joins all processed data and then saves it in a file for subsequent modeling.
```
# Last amended: 24th October, 2020
# Myfolder: C:\Users\Administrator\OneDrive\Documents\home_credit_default_risk
# Objective:
# Solving Kaggle problem: Home Credit Default Risk
# ... | github_jupyter |
```
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout, Bidirectional
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
### YOUR CODE HER... | github_jupyter |
# Implementing a Neural Network
In this exercise we will develop a neural network with fully-connected layers to perform classification, and test it out on the CIFAR-10 dataset.
```
# A bit of setup
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.neural_net import TwoLayerNet
%matplotlib ... | github_jupyter |
---
_You are currently looking at **version 1.0** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-data-analysis/resources/0dhYG) course resource._
---
# The Series Data Str... | github_jupyter |
```
import traytable as tt
import matplotlib.pyplot as plt
```
Download this notebook and try it out yourself [here](https://github.com/dennisbrookner/traytable/blob/main/docs/examples/0_simple_example.ipynb)
## Making a screen
First, initialize the screen with `screen()`. This function requires that you specify
* ... | github_jupyter |
#### New to Plotly?
Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
<br>You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-fo... | github_jupyter |
## SASPy Tabulation for Descriptive Statistics
This notebook demonstrates the usage of a powerful set of tools for descriptive statistics and nesting data in SASPy, powered by the TABULATE procedure.
```
import saspy
sas = saspy.SASsession(cfgname='default')
saspy.__version__
cars = sas.sasdata('cars', 'sashelp')
car... | github_jupyter |
# High-performance simulations with TFF
This tutorial will describe how to setup high-performance simulations with TFF
in a variety of common scenarios.
TODO(b/134543154): Populate the content, some of the things to cover here:
- using GPUs in a single-machine setup,
- multi-machine setup on GCP/GKE, with and without... | github_jupyter |
# Pure API Demonstration: Research Software
These notebooks demonstrate some uses of the API of Elsevier's *Pure* Current Research Information System (CRIS). This notebook demonstrates some requests for research software.
Research Software is currently recorded in Pure as a type of Research Output.
**Enter API detai... | github_jupyter |
```
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
i... | github_jupyter |
# Introduction to Scientific Python #
## Purpose of this tutorial ##
This tutorial aims to be a not-so-gentle but useful introduction to the python programming language. The intended audience is anyone with some familiarity with programming but limited experience with python for scientific work. Good computational sk... | github_jupyter |
```
import numpy as np
from keras.models import Model
from keras.layers import Input, Dense, RepeatVector
from keras.layers.merge import Add, Subtract, Multiply, Average, Maximum, Minimum, Concatenate, Dot
from keras import backend as K
import json
from collections import OrderedDict
def format_decimal(arr, places=6):
... | github_jupyter |
```
import numpy as np
import os,sys
sys.path.append('.')
sys.path.append('../RL_lib/Utils')
%load_ext autoreload
%load_ext autoreload
%autoreload 2
%matplotlib nbagg
import os
print(os.getcwd())
%%html
<style>
.output_wrapper, .output {
height:auto !important;
max-height:1000px; /* your desired max-height he... | github_jupyter |
```
%matplotlib inline
import pandas
import geopandas
import numpy as np
import matplotlib.pyplot as plt
import sys
from esda.adbscan import ADBSCAN, get_cluster_boundary, remap_lbls
```
- Set up three clusters
```
n = 100
np.random.seed(12345)
c1 = np.random.normal(1, 1, (n, 2))
c2 = np.random.normal(6, 1, (n, 2))
... | github_jupyter |
# __DATA 5600: Introduction to Regression and Machine Learning for Analytics__
## __Review of Basic Concepts in Asymptotic Theory__
<br>
<br>
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
```
<br>
## __The Law of Large Numbers__
***Definition***
The law which states that the larger a ... | github_jupyter |
For many users, it may be valuable to pull together some particular properties that can be summarized to a simple value per each species record and view them in a spreadsheet program of one kind or another in order to slice the data in various ways or run reports. This notebook runs through all of the data generated, m... | github_jupyter |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.