code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
# Download Patent DB & Adding Similarity Data
The similarity data on its own provides data on patent doc2vec vectors, and some pre-calculated similarity scores. However, it is much more useful in conjunction with a dataset containing other patent metadata. To achieve this it is useful to download a patent dataset and ... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Tra... | github_jupyter |
# Reconstructing MNIST images using Autoencoder
Now that we have understood how autoencoders reconstruct the inputs, in this section we will learn how autoencoders reconstruct the images of handwritten digits using the MNIST dataset.
In this chapter, we use keras API from the tensorflow for building the models. So ... | github_jupyter |
# Ch 2: Supervised Learning
2.1: Classification and regression
----
Code for Chapter 2 by authors can be found here:
https://github.com/amueller/introduction_to_ml_with_python/blob/master/02-supervised-learning.ipynb
Two major types of supervised learning:
* classification: goal is to predict a class label (discr... | github_jupyter |
```
# default_exp core
```
# Few-shot Learning with GPT-J
> API details.
```
# export
import os
import pandas as pd
#hide
from nbdev.showdoc import *
import toml
s = toml.load("../.streamlit/secrets.toml", _dict=dict)
```
Using `GPT_J` model API from [Nlpcloud](https://nlpcloud.io/home/token)
```
import nlpcloud
c... | github_jupyter |
# Synthetic Images from simulated data
## Authors
Yi-Hao Chen, Sebastian Heinz, Kelle Cruz, Stephanie T. Douglas
## Learning Goals
- Assign WCS astrometry to an image using ```astropy.wcs```
- Construct a PSF using ```astropy.modeling.model```
- Convolve raw data with PSF using ```astropy.convolution```
- Calculate... | github_jupyter |
# Candlestick Upside Gap Two Crows
https://www.investopedia.com/terms/u/upside-gap-two-crows.asp
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import talib
import warnings
warnings.filterwarnings("ignore")
# yahoo finance is used to fetch data
import yfinance as yf
yf.pdr_override()
# ... | github_jupyter |
# Slope Analysis
This project use the change of holding current slope to identify drug responders.
## Analysis Steps
The `getBaselineAndMaxDrugSlope` function smoothes the raw data by the moving window decided by `filterSize`, and analyzes the smoothed holding current in an ABF and returns baseline slope and drug sl... | github_jupyter |
# TensorFlow Regression Example
## Creating Data
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
# 1 Million Points
x_data = np.linspace(0.0,10.0,1000000)
noise = np.random.randn(len(x_data))
# y = mx + b + noise_levels
b = 5
y_true = (0.5 * x_data ) + 5 + noise
my_data... | github_jupyter |
# Project: Part of Speech Tagging with Hidden Markov Models
---
### Introduction
Part of speech tagging is the process of determining the syntactic category of a word from the words in its surrounding context. It is often used to help disambiguate natural language phrases because it can be done quickly with high accu... | github_jupyter |
```
import json
import itertools
import copy
import random
def filter_lexicon(lexicon):
keys_to_hold = "yellow,red,green,cyan,purple,blue,gray,brown".split(",")
deleted_keys = set()
for k in lexicon.keys():
if k not in keys_to_hold:
deleted_keys.add(k)
for k in deleted_keys:
... | github_jupyter |
# Image classification training with image format
1. [Introduction](#Introduction)
2. [Prerequisites and Preprocessing](#Prerequisites-and-Preprocessing)
1. [Permissions and environment variables](#Permissions-and-environment-variables)
2. [Prepare the data](#Prepare-the-data)
3. [Fine-tuning The Image Classificat... | github_jupyter |
```
%matplotlib inline
```
단일 머신을 이용한 모델 병렬화 실습 예제
===================================================
**저자** : `Shen Li <https://mrshenli.github.io/>`_
**번역** : `안상준 <https://github.com/Justin-A>`_
모델 병렬 처리는 분산 학습 기술에 범용적으로 사용되고 있습니다.
이전 튜토리얼들에서는 'DataParallel' `<https://pytorch.org/tutorials/beginner/blitz/data_p... | github_jupyter |
## PySpark Data Engineering Practice (Sandboxing)
### Olympic Athlete Data
This notebook is for data engineering practicing purposes.
During this notebook I want to explore data by using and learning PySpark.
The data is from: https://www.kaggle.com/mysarahmadbhat/120-years-of-olympic-history
```
## Imports
from pysp... | github_jupyter |
# Summed Likelihood Analysis with Python
This sample analysis shows a way of performing joint likelihood on two data selections using the same XML model. This is useful if you want to do the following:
* Coanalysis of Front and Back selections (not using the combined IRF)
* Coanalysis of separate time intervals
* Coa... | github_jupyter |
## The Golden Standard
In the previous session, we saw why and how association is different from causation. We also saw what is required to make association be causation.
$
E[Y|T=1] - E[Y|T=0] = \underbrace{E[Y_1 - Y_0|T=1]}_{ATET} + \underbrace{\{ E[Y_0|T=1] - E[Y_0|T=0] \}}_{BIAS}
$
To recap, association becomes ... | github_jupyter |
```
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from scipy.stats import poisson, norm
def compute_scaling_ratio(mu_drain,mu_demand,drift_sd,init_state):
drain_time = init_state/(mu_drain-mu_demand)
accum_std = drift_sd*np.sqrt(drain_time)
ratio = accum_std/init_state
retur... | github_jupyter |
```
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
num_epochs = 100
total_series_length = 50000
truncated_backprop_length = 15
state_size = 4
num_classes = 2
echo_step = 3
batch_size = 5
num_batches = total_series_length//batch_size//truncated_backprop_length
from numpy import *
from matplo... | github_jupyter |
<!--NOTEBOOK_HEADER-->
*This notebook contains course material from [CBE30338](https://jckantor.github.io/CBE30338)
by Jeffrey Kantor (jeff at nd.edu); the content is available [on Github](https://github.com/jckantor/CBE30338.git).
The text is released under the [CC-BY-NC-ND-4.0 license](https://creativecommons.org/lic... | github_jupyter |
# Ridge Regressor with StandardScaler
### Required Packages
```
import warnings
import numpy as np
import pandas as pd
import seaborn as se
import matplotlib.pyplot as plt
from sklearn.linear_model import Ridge
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from skl... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from latency import run_latency, run_latency_changing_topo, run_latency_per_round, run_latency_per_round_changing_topo, nodes_latency
import sys
sys.path.append('..')
from utils import create_mixing_matrix, load_data, run, consensus
```
# Base case
```
# IID ca... | github_jupyter |
```
# import customizing_motif_vec
import extract_motif
import motif_class
import __init__
import json_utility
from importlib import reload
reload(__init__)
reload(extract_motif)
# reload(customizing_motif_vec)
reload(motif_class)
import plot_glycan_utilities
reload(plot_glycan_utilities)
import matplotlib.pyplot as pl... | github_jupyter |
# Passive and active colloidal chemotaxis in a microfluidic channel: mesoscopic and stochastic models
**Author:** Pierre de Buyl
*Supplemental information to the article by L. Deprez and P. de Buyl*
This notebook reports the characterization of the diffusion coefficients for a rigid dimer
confined between plates.
... | github_jupyter |
# Project for Machine Learning and Statistics - December 2021
## Submitted by Sinéad Duffy, ID 10016151
***
## Notebook 2 - Scipy-stats.ipynb
### Brief - write an overview of the SciPy.stats library, outline (using examples) the package and complete an example hypothesis using ANOVA
***
:
def __init__(self, learning_rate=0.1):
self.weights_0_1 = np.random.normal(0.0, 2 ** -0.5, (2, 3))
self.weights_1_2 = np.random.normal(0.0, 1, (1, 2))
self.sigmoid_mapper = np.vectorize(self.sigmoid)
self.learning_rate = np.ar... | github_jupyter |
```
import sqlite3 as sl
import pandas as pd # type: ignore
COLORS_by_TYPE = {
'fire': 'red',
'water': '#09E1FF',
'normal': '#1DFDA8',
'poison': '#B918FF',
'electric': 'yellow',
'ground': '#FF9C15',
'fairy': '#FF69B4',
'grass': '#34FF5C',
'bug': '#90EE38',
'psychic': '#B71ECF'... | github_jupyter |
#### Import Dependencies
```
import os
import gc
gc.enable()
import math
import json
import time
import random
import multiprocessing
import warnings
warnings.filterwarnings("ignore", category=UserWarning)
import numpy as np
import pandas as pd
from tqdm import tqdm, trange
from sklearn import model_selection
import... | github_jupyter |
```
import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
%matplotlib inline
style.use('ggplot')
x = [20,30,50]
y = [ 10,50,13]
x2 = [4,10,47,]
y2= [56,4,30]
plt.plot(x, y, 'r', label='line one', linewidth=5)
plt.plot(x2, y2, 'c', label ='line two', linewidth=5)
plt.title('Interactive plot'... | github_jupyter |
# Gas Price Prediction with Recurrent Neural Networks (Hourly, Window 2)
This notebook contains the generic RNN model used in the thesis project. The experiment includes two extracted datasets of a predefined gas station. The first dataset contains the daily maximum prices, while the other contains data of hourly gran... | github_jupyter |
# Svenskt Kvinnobiografiskt lexikon part 5
version part 5 - 0.1
Check SKBL women if Alvin has an authority for the women
* this [Jupyter Notebook](https://github.com/salgo60/open-data-examples/blob/master/Svenskt%20Kvinnobiografiskt%20lexikon%20part%205.ipynb)
* [part 1](https://github.com/salgo60/open-data-exam... | github_jupyter |
[[source]](../api/alibi.explainers.shap_wrappers.rst)
# Tree SHAP
<div class="alert alert-info">
Note
To enable SHAP support, you may need to run:
```bash
pip install alibi[shap]
```
</div>
## Overview
The tree SHAP (**SH**apley **A**dditive ex**P**lanations) algorithm is based on the paper [From local explan... | github_jupyter |
# Using PyTorch with TensorRT through ONNX:
TensorRT is a great way to take a trained PyTorch model and optimize it to run more efficiently during inference on an NVIDIA GPU.
One approach to convert a PyTorch model to TensorRT is to export a PyTorch model to ONNX (an open format exchange for deep learning models) and... | github_jupyter |
Added custom loss function base on @kyakvolev 's work. Credit to the author.
The forum post is here: https://www.kaggle.com/c/m5-forecasting-uncertainty/discussion/139515
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from tqdm.auto import tqdm as tqdm
from ipywidgets import widgets, inte... | github_jupyter |
# Analysis of NFL csv data for analysis
```
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from math import pi
# import seaborn as sns
# import matplotlib as plt
data_dir = '../seasonData/'
```
use 2009 data as a test set
```
df_2009 = pd.read_csv(data_dir+... | github_jupyter |
# Week 3
## Introduction to Solid State
```
import numpy as np
import matplotlib.pyplot as plt
import os
import subprocess
from polypy.read import History
from polypy.msd import MSD
from polypy import plotting
def get_diffusion(file, atom):
with open(file) as f:
y = False
for line in f:
... | github_jupyter |
```
import pywt
import numpy as np
import pandas as pa
import sqlite3, os
from skimage.restoration import denoise_wavelet
import matplotlib.pyplot as plt
import warnings
import ruptures as rpt
from scipy.signal import savgol_filter, medfilt
import numpy as np
import pylab as pl
from scipy.signal import hilbert
from sci... | github_jupyter |
```
import pandas as pd
import utils
import matplotlib.pyplot as plt
import random
import plotly.express as px
import numpy as np
random.seed(9000)
plt.style.use("seaborn-ticks")
plt.rcParams["image.cmap"] = "Set1"
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=plt.cm.Set1.colors)
%matplotlib inline
```
In this ... | github_jupyter |
# CaseLaw dataset to assist with Law-Research - EDA
---
<dl>
<dt>Acquiring the dataset</dt>
<dd>We initially use dataset of all cases in USA to be able to train it and as a proof of concept.</dd>
<dd>The dataset is available in XML format, which we will put in mongodb or firebase format based on how unstructured ... | github_jupyter |
## HOWTO estimate parameter-errors using Monte Carlo - an example with python
Will Clarkson, Sat March 8th 2014
UPDATED Sun March 14th 2021 with more recent system version and a few other minor style updates (now runs on python 3 and should be backwards-compatible to python 2.7).
I have started the process of updat... | github_jupyter |
# Fine tuning Marian-NMT en-ru model
## Установка зависимостей
```
!pip install datasets transformers[sentencepiece]
!pip install sacrebleu
!pip install accelerate
!pip install openpyxl
!apt install git-lfs
!pip install matplotlib
# загрузим репозиторий; нужен для предобработки
!git clone https://github.com/eleldar/T... | github_jupyter |
```
from lenslikelihood.power_spectra import *
mass_function_model = 'rodriguezPuebla2016'
normalization = 'As'
pivot_string = '1'
pivot = 1.0
structure_formation_interp_As = load_interpolated_mapping(mass_function_model, pivot_string)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
impo... | github_jupyter |
# Training Neural Networks
The network we built in the previous part isn't so smart, it doesn't know anything about our handwritten digits. Neural networks with non-linear activations work like universal function approximators. There is some function that maps your input to the output. For example, images of handwritt... | github_jupyter |
# Anchor Boxes
:label:`sec_anchor`
Object detection algorithms usually sample a large number of regions in the input image, determine whether these regions contain objects of interest, and adjust the edges of the regions so as to predict the ground-truth bounding box of the target more accurately. Different models may... | github_jupyter |
# "Analise casos de SRAG em crianças e adolecentes"
> "Dados dos casos de hospitalizações por SRAG do opendatasus"
- toc: true
- branch: master
- badges: false
- comments: false
- numbersections: true
- categories: [srag]
- image: images/some_folder/your_image.png
- hide:false
- search_exclude: true
- metadata_key1: m... | github_jupyter |
TSG034 - Livy logs
==================
Description
-----------
Steps
-----
### Parameters
```
import re
tail_lines = 500
pod = None # All
container = 'hadoop-livy-sparkhistory'
log_files = [ '/var/log/supervisor/log/livy*' ]
expressions_to_analyze = [
re.compile(".{17} WARN "),
re.compile(".{17} ERROR ")
... | github_jupyter |
```
from bs4 import BeautifulSoup
import requests
from urllib.parse import urljoin
import re
import numpy as np
import pandas as pd
import json
```
## Dataset Generation
Stats scraped from basketball reference
```
nba_champion_url = 'https://www.basketball-reference.com/playoffs/'
nba_team_stats_url = 'https://www.b... | github_jupyter |
# Taxi Price Prediction Competition - Team 40 - Aditya Sidharta
## Overall Pipeline
In this Taxi price prediction competition, we were asked to build a model which is able to predict the price of a taxi ride, by predicting the duration and the trajectory length of the taxi ride. Then, we will sum the values of the tw... | github_jupyter |
# Training and Evaluating Machine Learning Models in cuML
This notebook explores several basic machine learning estimators in cuML, demonstrating how to train them and evaluate them with built-in metrics functions. All of the models are trained on synthetic data, generated by cuML's dataset utilities.
1. Random Fores... | github_jupyter |
# Radiative Cores & Convective Envelopes
Analysis of how magnetic fields influence the extent of radiative cores and convective envelopes in young, pre-main-sequence stars.
Begin with some preliminaries.
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d
... | github_jupyter |
DIFAX Replication
=================
This example replicates the traditional DIFAX images for upper-level
observations.
By: Kevin Goebbert
Observation data comes from Iowa State Archive, accessed through the
Siphon package. Contour data comes from the GFS 0.5 degree analysis.
Classic upper-level data of Geopotential ... | github_jupyter |
<a href="https://colab.research.google.com/github/Cknowles11/DS-Unit-2-Applied-Modeling/blob/master/Copy_of_LS_DS_233_assignment.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Lambda School Data Science
*Unit 2, Sprint 3, Module 3*
---
# Permut... | github_jupyter |
```
# Decorator Introduction
def func():
return 1
print(func())
print(func) # that means we can assign this function to a variable
def hello():
return "Hello"
greet = hello
print(hello)
print(greet())
# Delete hello
del hello
try:
print(hello())
except NameError:
print("hello() is not defined")
prin... | github_jupyter |
# Section 2 - Neural Networks
## Lesson 1 - Introduction to Neural Networks
### 27. The Gradient Descent Algorithm
```
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
#Some helper functions for plotting and drawing lines
def plot_points(X, y):
admitted = X[np.argwhere(y==1)]
rejected... | github_jupyter |
```
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
import numpy as np
import pandas as pd
# Hyperparameters
input_size = 28 * 28 # 784
num_classes = 10
num_epochs = 5
batch_size = 100
lr = 0.01
# MNIST dataset (images and labels)
train_d... | github_jupyter |
```
!pip install splinter
#Import Dependencies
from splinter import Browser
from bs4 import BeautifulSoup
import requests
import re
import pandas as pd
import pymongo
from selenium import webdriver
from selenium.webdriver.chrome.service import Service
```
NASA Mars News¶
Scrape the NASA Mars News Site and collect the... | github_jupyter |
# Creating a Sentiment Analysis Web App
## Using PyTorch and SageMaker
_Deep Learning Nanodegree Program | Deployment_
---
Now that we have a basic understanding of how SageMaker works we will try to use it to construct a complete project from end to end. Our goal will be to have a simple web page which a user can u... | github_jupyter |
# Description
This notebook contains the interpretation of a cluster (which features/latent variables in the original data are useful to distinguish traits in the cluster).
See section [LV analysis](#lv_analysis) below
# Modules loading
```
%load_ext autoreload
%autoreload 2
import pickle
import re
from pathlib imp... | github_jupyter |
<a href="https://colab.research.google.com/github/z-arabi/notebooks/blob/main/01_introduction.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
# Uncomment and run this cell if you're on Colab or Kaggle
!git clone https://github.com/nlp-with-trans... | github_jupyter |
# Lecture 55: Adversarial Autoencoder for Classification
## Load Packages
```
%matplotlib inline
import os
import math
import torch
import itertools
import torch.nn as nn
import torch.optim as optim
from IPython import display
import torch.nn.functional as F
import matplotlib.pyplot as plt
import torchvision.datasets... | github_jupyter |
# LightGBM
## Single Prediction
```
from backend.api.prediction import initialize_pipeline
config_path = "/home/joseph/Coding/ml_projects/earthquake_forecasting/backend/config.yml"
lgb_pipeline = initialize_pipeline(config_path, "lightgbm")
lgb_pipeline
import pandas as pd
data = {
"building_id": ... | github_jupyter |
```
from collections import defaultdict
import pyspark.sql.types as stypes
import operator
import math
d = sc.textFile("gs://lbanor/dataproc_example/data/2017-11-01").zipW
r = (sc.textFile("gs://lbanor/dataproc_example/data/2017-11-01").zipWithIndex()
.filter(lambda x: x[1] > 0)
.map(lambda x: x[0].split(',')... | github_jupyter |
# SQL
```
import psycopg2
import sys, os
import numpy as np
import pandas as pd
import example_psql as creds
import pandas.io.sql as psql
# Create connection to postgresql
import example_psql as creds
from sqlalchemy import create_engine
engine = create_engine(f'postgresql://{creds.PGUSER}:{creds.PGPASSWORD}@{creds.PG... | github_jupyter |
##### Copyright 2021 The TensorFlow Federated 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 applicab... | github_jupyter |
<em><sub>This page is available as an executable or viewable <strong>Jupyter Notebook</strong>:</sub></em>
<br/><br/>
<a href="https://mybinder.org/v2/gh/JetBrains/lets-plot/v1.5.2demos1?filepath=docs%2Fexamples%2Fjupyter-notebooks%2Fmap_titanic.ipynb"
target="_parent">
<img align="left"
src="https://mybi... | github_jupyter |
```
import os
import json
import tensorflow as tf
import numpy as np
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib import cm
from tensor2tensor import problems
from tensor2tensor import models
from tensor2tensor.bin import t2t_decoder # To register the h... | 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 |
# T & T Lab 8 - 27th Jan
## Manish Ranjan Behera - 1828249
### WAP TO PRINT THIS PATTERN AND TAKE THE NO OF LINES AS INPUT FROM USER
```
n=int(input("Enter Size:"))
for i in range(n,0,-1):
if i==n:
print("*"*((2*n)-1... | github_jupyter |
# Feature Engineering and Labeling
We'll use the price-volume data and generate features that we can feed into a model. We'll use this notebook for all the coding exercises of this lesson, so please open this notebook in a separate tab of your browser.
Please run the following code up to and including "Make Factor... | github_jupyter |
# Introduction to Linear Algebra
This is a tutorial designed to introduce you to the basics of linear algebra.
Linear algebra is a branch of mathematics dedicated to studying the properties of matrices and vectors,
which are used extensively in quantum computing to represent quantum states and operations on them.
This... | github_jupyter |
```
import gym
import numpy as np
import torch
import wandb
import pandas as pd
import argparse
import pickle
import random
import sys
sys.path.append('/Users/shiro/research/projects/rl-nlp/can-wikipedia-help-offline-rl/code')
from decision_transformer.evaluation.evaluate_episodes import (
evaluate_episode,
... | github_jupyter |
Author: Vo, Huynh Quang Nguyen
# Acknowledgments
The contents of this note are based on the lecture notes and the materials from the sources below. All rights reserved to respective owners.
1. **Deep Learning** textbook by Dr Ian Goodfellow, Prof. Yoshua Bengio, and Prof. Aaron Courville. Available at: [Deep Learnin... | github_jupyter |
<a href="https://colab.research.google.com/github/andrewcgaitskell/dmtoolnotes/blob/main/Lists%2C_Arrays%2C_Tensors%2C_Dataframes%2C_and_Datasets.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
https://colab.research.google.com/github/tensorpig/lear... | github_jupyter |
# Movie Review Text Classification with Text processing
This tutorial: https://www.tensorflow.org/tutorials/keras/text_classification
```
!pip install -q tf-nightly
import tensorflow as tf
from tensorflow import keras
!pip install -q tfds-nightly
import tensorflow_datasets as tfds
tfds.disable_progress_bar()
import ... | github_jupyter |
```
import pandas as pd
import numpy as np
from analysis_utils import *
PAREDAO = "paredao13"
CAND1_PATH = "data/paredao13/flay.csv"
CAND2_PATH = "data/paredao13/thelma.csv"
CAND3_PATH = "data/paredao13/babu.csv"
DATE = 3
IGNORE_HASHTAGS = ["#bbb20", "#redebbb", "#bbb2020"]
candidate1_df = pd.read_csv(CAND1_PATH)
candi... | github_jupyter |
```
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 27 23:01:16 2015
@author: yilin
"""
# useful code: https://www.kaggle.com/cast42/rossmann-store-sales/xgboost-in-python-with-rmspe-v2/code
import pandas as pd
import numpy as np
import re
from dateutil.parser import parse
import random
import matplotlib.pyplot as plt
... | github_jupyter |
## Installation
```
!pip install -q --upgrade transformers datasets tokenizers
!pip install -q emoji pythainlp sklearn-pycrfsuite seqeval
!rm -r thai2transformers thai2transformers_parent
!git clone -b dev https://github.com/vistec-AI/thai2transformers/
!mv thai2transformers thai2transformers_parent
!mv thai2transfo... | github_jupyter |
# Pre-Processing Methods
```
%%capture
!pip3 install sparqlwrapper
# Common methods to retrieve data from Wikidata
import time
from SPARQLWrapper import SPARQLWrapper, JSON
import pandas as pd
import urllib.request as url
import json
from SPARQLWrapper import SPARQLWrapper
wiki_sparql = SPARQLWrapper("https://quer... | github_jupyter |
# Computer Vision Nanodegree
## Project: Image Captioning
---
In this notebook, you will train your CNN-RNN model.
You are welcome and encouraged to try out many different architectures and hyperparameters when searching for a good model.
This does have the potential to make the project quite messy! Before subm... | github_jupyter |
# Appendix E: Validation of FDR’s control of false positive node proportion
This appendix contains RFT and FDR results (Fig.E1) from six experimental datasets and a total of eight different analyses (Table E1) that were conducted but were not included in the main manuscript. The datasets represent a variety of biomec... | github_jupyter |
```
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import numexpr as ne
from scipy.ndimage import correlate1d
from dphutils import scale
import scipy.signal
from timeit import Timer
import pyfftw
# test monkey patching (it doesn't work for rfftn)
a = pyfftw.empty_aligned((512, 512), dtype='comp... | github_jupyter |
# Exploratory Data Analysis
Statistical functions can be found here: https://nbviewer.org/github/AllenDowney/empiricaldist/blob/master/empiricaldist/dist_demo.ipynb
```
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
sns.set()
```
## Question: What's t... | github_jupyter |
# Using matplotlib basemap to project California data
```
%matplotlib inline
import pandas as pd, numpy as np, matplotlib.pyplot as plt
from geopandas import GeoDataFrame
from mpl_toolkits.basemap import Basemap
from shapely.geometry import Point
# define basemap colors
land_color = '#F6F6F6'
water_color = '#D2F5FF'
c... | github_jupyter |
## Summarize all common compounds and their percent strong scores
```
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(patchwork))
source("viz_themes.R")
source("plotting_functions.R")
source("data_functions.R")
results_dir <- file.... | github_jupyter |
# Parameter Values
In this notebook, we explain how parameter values are set for a model. Information on how to add parameter values is provided in our [online documentation](https://pybamm.readthedocs.io/en/latest/tutorials/add-parameter-values.html)
## Setting up parameter values
```
%pip install pybamm -q # in... | github_jupyter |
# Talks markdown generator for academicpages
Adapted from generator in academicpages
Takes a TSV of talks with metadata and converts them for use with [academicpages.github.io](academicpages.github.io). This is an interactive Jupyter notebook ([see more info here](http://jupyter-notebook-beginner-guide.readthedocs.io... | github_jupyter |
# Under and over fitting
> Validation and learning curves
- toc: true
- badges: false
- comments: true
- author: Cécile Gallioz
- categories: [sklearn]
# Underfitting vs. Overfitting - Actual vs estimated function
[scikit-learn documentation](https://scikit-learn.org/stable/auto_examples/model_selection/plot_underfit... | github_jupyter |
# **Spit some [tensor] flow**
We need to learn the intricacies of tensorflow to master deep learning
`Let's get this over with`
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
import cv2
print(tf.__version__)
```
## A time series is just a TxD matrix right?
so in... | github_jupyter |
# Classifying Ionosphere structure using K nearest neigbours algorithm
<hr>
### Nearest neighbors
Amongst the standard machine algorithms, Nearest neighbors is perhaps one of the most intuitive algorithms. To predict the class of a new sample, we look through the training dataset for the samples that are most similar ... | github_jupyter |
# Notebook 4: Quantum operations and distance
In this notebook we will be taking a closer look at quantum operations, i.e. parts of a quantum circuit that are _not necessarily_ unitary.
```
import numpy as np
# Import cirq, install it if it's not installed.
try:
import cirq
except ImportError:
print("install... | github_jupyter |
```
#load watermark
%load_ext watermark
%watermark -a 'Gopala KR' -u -d -v -p watermark,numpy,pandas,matplotlib,nltk,sklearn,tensorflow,theano,mxnet,chainer,seaborn,keras,tflearn,bokeh,gensim
from preamble import *
%matplotlib inline
```
## Algorithm Chains and Pipelines
```
from sklearn.svm import SVC
from sklearn.d... | github_jupyter |
[](https://colab.research.google.com/github/jfcrenshaw/pzflow/blob/main/examples/marginalization.ipynb)
If running in Colab, to switch to GPU, go to the menu and select Runtime -> Change runtime type -> Hardware accelerator -> GPU.
In addition,... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from scipy import ndimage as ndi
import os
from PIL import Image
import PIL.ImageOps
from skimage.morphology import watershed
from skimage.feature import peak_local_max
from skimage.filters import threshold_otsu
from skimage.morphology import binary_closing
f... | github_jupyter |
# Rerank with MonoT5
```
!nvidia-smi
from pygaggle.rerank.base import Query, Text
from pygaggle.rerank.transformer import MonoT5
from trectools import TrecRun
import ir_datasets
monoT5Reranker = MonoT5()
DIR='/mnt/ceph/storage/data-in-progress/data-teaching/theses/wstud-thesis-probst/retrievalExperiments/runs-ecir22... | github_jupyter |
# Python for Policy Analysts
## Session 0: Setting Up Python
Created by: O Downs (odowns@berkeley.edu)
Instructor Edition
### Goals:
* Getting you started with Python!
* Download Anaconda, which will facilitate your Python use
* Understand Terminal commands
* Learn how to `pip install`
* Learn how to start up a J... | github_jupyter |
# Hurricane Ike Maximum Water Levels
Compute the maximum water level during Hurricane Ike on a 9 million node triangular mesh storm surge model. Plot the results with Datashader.
```
import xarray as xr
import numpy as np
import pandas as pd
import hvplot.xarray
import fsspec
from dask.distributed import Client, prog... | github_jupyter |
# GitHub : Le réseau social des développeurs grâce à Git
_Auteur_: Hugo Ducommun
_Date_: 30 Mai 2019
_GitHub_ est un plateforme de projets de jeunes développeurs motivés qui souhaient publier leur travail de manière libre (OpenSource). _GitHub_ est connu pour être pratique lorsqu'on travaille en équipe. Il permet à ... | github_jupyter |
```
import numpy as np
from nose.tools import assert_almost_equal, assert_almost_equals, assert_equal
```
Ответами на задачи являются функции. Они будут проверены автоматическими тестами на стороне сервера.
Некоторые тесты выполняются локально для самопроверки.
### Вопросы для самоконтроля
Эта часть задания не оце... | github_jupyter |
# [Applied Statistics](https://lamastex.github.io/scalable-data-science/as/2019/)
## 1MS926, Spring 2019, Uppsala University
©2019 Raazesh Sainudiin. [Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
# 11. Non-parametric Estimation and Testing
### Topics
- Non-parametric... | github_jupyter |
<a href="https://colab.research.google.com/github/davemcg/scEiaD/blob/master/colab/cell_type_ML_labelling.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Auto Label Retinal Cell Types
## tldr
You can take your (retina) scRNA data and fairly qui... | github_jupyter |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.