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# Store tracts and points in PostGIS
...for a fast spatial-join of points to tracts.
First, install postgres, postgis, and psycopg2. Then create the database from command prompt if it doesn't already exist:
```
createdb -U postgres points_tracts
psql -U postgres -d points_tracts -c "CREATE EXTENSION postgis;"
```
M... | github_jupyter |
# Load PyTorch model
In this tutorial, you learn how to load an existing PyTorch model and use it to run a prediction task.
We will run the inference in DJL way with [example](https://pytorch.org/hub/pytorch_vision_resnet/) on the pytorch official website.
## Preparation
This tutorial requires the installation of... | github_jupyter |
# Chapter 4 : Statistics and Linear Algebra
# basic descriptive statistics
```
%matplotlib inline
import numpy as np
from scipy.stats import scoreatpercentile
import pandas as pd
data = pd.read_csv("co2.csv", index_col=0, parse_dates=True)
co2 = np.array(data.co2)
print("The statistical valus for amounts of co2 in... | github_jupyter |
```
import pandas as pd
import numpy as np
df = pd.read_csv('Z_sani.csv')
# 1: oh 2: or 3:mm 4: std 5:target
encode_list = [3,3,3,1,3,1,1,1,1,1,1,1,1,1,1,1,1,3,3,
1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
4,4,4,4,4,4,4,3,3,3,4,4,3,4,4,1,2,5]
df.head()
from sklearn.model_selection import train_test_spli... | 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 |
##**Gender-speech-duration-calculator**
Here you will find a tool to calculate the percentage of time of female voice speech and male voice speech in a video/movie. You can either choose to paste a youtube link or you can upload your video. Install the program, choose one option and calculate the percentage of female a... | github_jupyter |
# SciPy
(documentação oficial: [docs.scipy.org](docs.scipy.org))
<img src="img/scipy.png" alt="ícone do pacote scipy - uma cobra branca desenhada num círculo azul" width=350>
O pacote **SciPy** é uma coleção de algoritmos e funções matemáticas construídos sobre o pacote <b><a style color="red">Numpy</a></b> do Python... | github_jupyter |
# En este ejercicio vamos a optimizar parámetros #
(Credits to https://github.com/codiply/blog-ipython-notebooks/blob/master/scikit-learn-estimator-selection-helper.ipynb )
Para optimizar los parámetros usaremos un GridSearch.
Y comparar clasificadores.
<div class="alert alert-danger" role="alert">
Este ejemplo e... | github_jupyter |
<a href="https://colab.research.google.com/github/ariG23498/G-SimCLR/blob/master/Imagenet_Subset/Vanilla_SimCLR/Linear_Evaluation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Imports and setup
```
import tensorflow as tf
print(tf.__version__)... | github_jupyter |
# Evolutionnary Hierarchical Dirichlet Processes for Multiple Correlated Time Varying Corpora
## Introduction
-----------------
Le notebook suivant est l'implémentation du code de l'article EvoHDP, réalisé par J.Zhang,Y.Song & al et est testé : <br\>
- sur les données synthétiques indiqués par l'article
- sur des co... | github_jupyter |
## 1. Importing important price data
<p>Every time I go to the supermarket, my wallet weeps a little. But how expensive is food around the world? In this notebook, we'll explore time series of food prices in Rwanda from the <a href="https://data.humdata.org/dataset/wfp-food-prices">United Nations Humanitarian Data Exch... | github_jupyter |
```
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from decimal import *
import scipy.special
import scipy.stats
import scipy
import numpy
import math
import itertools
import sys
sys.version
```
# Out-of-band P2W attack evaluation
## P2W attack success probability within $ N $ total blocks (no ... | github_jupyter |
```
import re
from tqdm import tqdm
from collections import defaultdict, Counter, UserDict
from itertools import product
from cached_property import cached_property
from litecoder.models import session, City
from litecoder import logger
def keyify(text):
text = text.lower()
text = text.strip()
text ... | github_jupyter |
```
%pylab inline
from numpy.lib.recfunctions import append_fields
### Excluding the AGB stars with dust spheres around.
x = np.load("../data/GDR3/gaiadr3_0ext.npy")
cut = (x['log_lum'] <5.) & (x['log_lum'] >3.) & (x['log_teff'] <3.7) & (x['gaia_g'] > 0.5)
print(len(x), len(x[cut]))
x = np.load("../data/TMASS/2mass_0ex... | github_jupyter |
# Circuit optimization using PatternManager - example of QAOA for MaxCut
This notebook provides an example of minimizing the duration of a quantum circuit. In this notebook, a quantum circuit implementing an instance of Q.A.O.A. is used and the `PatternManager` tool will be used to minimize the duration of this circui... | github_jupyter |
# Simple Reinforcement Learning in Tensorflow Part 1:
## The Multi-armed bandit
This tutorial contains a simple example of how to build a policy-gradient based agent that can solve the multi-armed bandit problem. For more information, see this [Medium post](https://medium.com/@awjuliani/super-simple-reinforcement-lear... | github_jupyter |
# B2: TCA 13C MFA demo
# Intro
# Setup
First, we need to set the path and environment variable properly:
```
quantmodelDir = '/users/hgmartin/libraries/quantmodel'
```
This is the only place where the jQMM library path needs to be set.
```
%matplotlib inline
import sys, os
pythonPath = quantmodelDir+"/code/core"... | github_jupyter |
```
import pandas as pd
com2 = pd.read_csv('artist_m_extracted.csv')
com2.shape
com4 = com2.copy()
com4.lyricist_m = com4.lyricist_m.str.replace("'", '', regex=False).str.replace("[", '', regex=False).str.replace("]", '', regex=False)
com4.composer_m = com4.composer_m.str.replace("'", '', regex=False).str.replace("[", ... | github_jupyter |
## Largest Product of Three from List
Given a list of integers, return the largest product that can be made by multiplying any three integers. For example, if the list is [-10,-10,5,2] you should return 500. You can assume that the list has at least three integers.
```
# If the list is all positive, then it's trivial... | github_jupyter |
# 1.PaddleGAN实现精准唇形合成-- 物理学界大佬们再次合唱
## 1.1 宋代著名诗人苏轼「动起来」的秘密
坐拥百万粉丝的**独立艺术家大谷Spitzer老师**利用深度学习技术使**宋代诗人苏轼活过来，穿越千年，为屏幕前的你们亲自朗诵其著名古诗~** [点击量](https://www.bilibili.com/video/BV1mt4y1z7W8)近百万，同时激起百万网友热议，到底是什么技术这么牛气？
![](https://ai-studio-static-online.cdn.bcebos.com/c21d8a1de3084b6ca599bc2cda373d3fef4b1a0ae98646f4b629dae1... | github_jupyter |
```
import pandas as pd
import datetime as dt
from pathlib import Path
import json
print("Importing Complete")
# Let's take a look at the past sp500 tickers.
def get_sp500_constituents_records(filepath):
'''Gets SP500 constituents records from the specified filepath.
Args:
filepath: string of where the SP50... | github_jupyter |
```
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import ram
tf.enable_eager_execution()
train = ram.dataset.train('~/data/mnist/')
batch_size = 5
ram_model = ram.RAM(batch_size=batch_size)
optimizer = tf.train.MomentumOptimizer(momentum=0.9, learning_rate=0.001)
batch =... | github_jupyter |
```
import json
import numpy as np
import pandas as pd
from sklearn.feature_extraction import text
from sklearn.linear_model import LogisticRegression
import sklearn.model_selection as modsel
import sklearn.preprocessing as preproc
```
## Load and prep Yelp reviews data
```
## Load Yelp Business data
biz_f = open('da... | github_jupyter |
```
import seaborn as sns; sns.set(color_codes=True)
tips = sns.load_dataset("tips")
print(tips[:5])
print(len(tips))
ax = sns.regplot(x="total_bill", y="tip", data=tips)
import matplotlib.pyplot as plt
g = sns.FacetGrid(tips, hue="sex", size=6, aspect=2)
g.map(plt.scatter, "total_bill", "tip")
g.add_legend()
import ra... | github_jupyter |
# Generating CLEAN Results
## Load environment
```
%matplotlib inline
import sys
# Directories and paths
lib_path = '/gpfswork/rech/xdy/uze68md/GitHub/'
data_path = '/gpfswork/rech/xdy/uze68md/data/'
model_dir = '/gpfswork/rech/xdy/uze68md/trained_models/model_cfht/'
# Add library path to PYTHONPATH
path_alphatrans... | github_jupyter |
# Importing the libraries
```
import numpy as np
import pandas as pd
import statsmodels.formula.api as sm
```
# Load Data
```
dataset=pd.read_csv('OnlineRetail.csv',encoding='latin1')
dataset.head()
dataset.describe()
dataset.info()
```
# Data Preprocessing
We are going to analysis the Customers based on below 3 ... | github_jupyter |
```
import time
import toml
import numpy as np
import matplotlib.pyplot as plt
from ref_trajectory import generate_trajectory as traj
%matplotlib inline
```
There are a lot of configuration parameters. It is a good idea to separate it from the main code. At some point you will be doing parameter tuning.
We will use ... | github_jupyter |
# Lab One - Climatic Averages
## *Analyzing the Global Temperatures Divergence from Average from 1880 - 2018*
In this lab we learn part 1 basics of Python (the programming commands) for data analysis through utilizing the Jupyter environment (this display) to analyze data.
You will learn how to:
- Use Jupyter
-... | github_jupyter |
# AutoGluon Tabular with SageMaker
[AutoGluon](https://github.com/awslabs/autogluon) automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text... | github_jupyter |
```
import plaidml.keras
plaidml.keras.install_backend()
import os
os.environ["KERAS_BACKEND"] = "plaidml.keras.backend"
# Importing useful libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layer... | github_jupyter |
# Q-learning applied to FrozenLake
#### **Remember**: Q-learning is a model free, off-policy algorithm that can be used to find an optimal action using a Q function. Q can be represented as a table that contains a value for each pair state-action
To review Q-learning watch [Q learning explained by Siraj](https://... | github_jupyter |
<img style="float: center;" src="images/CI_horizontal.png" width="600">
<center>
<span style="font-size: 1.5em;">
<a href='https://www.coleridgeinitiative.org'>Website</a>
</span>
</center>
Rayid Ghani, Frauke Kreuter, Julia Lane, Adrianne Bradford, Alex Engler, Nicolas Guetta Jeanrenaud, Graham Henke,... | github_jupyter |
# Object oriented programming
# Lab 03
## February 23, 2018
## 1. Basic exercises
### 1.1 Define a class named A with a contructor that takes a single parameter and stores it in an attribute named `value`. Add a `print_value` method to the class.
Instantiate the class and call the `print_value` method.
### 1.2 R... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
import sys
import casadi as ca
import os
import matplotlib.pyplot as plt
sys.path.insert(0, '../../src')
from pymoca.backends.xml import model, sim_scipy, analysis
from pymoca.backends.xml import parser as parse_xml
from pymoca.backends.xml.generator import gen... | github_jupyter |
# Interactive Widget: Front End Code: Bagging Classifier
This is our official final version of the widget.
Throughout this workbook, we used steps from the following web pages to inform our widgets.
- https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20Basics.html
- https://ipywidgets.readthedocs.io/en/late... | github_jupyter |
```
import pandas as pd
import numpy as np
df = pd.read_csv('WeNoGetPresident.csv')
del df['Unnamed: 0'] #Delete column
df.rename(columns={"created_at":"Time_Posted",
"text":"Tweet",
"source":"Tweet_Source",
"description":"Bio"}, inplace=True) #Rename Created_at, Tw... | github_jupyter |
# Using an SBML model
## Getting started
### Installing libraries
Before you start, you will need to install a couple of libraries:
The [ModelSeedDatabase](https://github.com/ModelSEED/ModelSEEDDatabase) has all the biochemistry we'll need. You can install that with `git clone`.
The [PyFBA](http://linsalrob.... | github_jupyter |
# JavaScript and HTML Tricks in a Jupyter Notebook
Normally I use [JSFiddle](https://jsfiddle.net/) to mock up JavaScript concepts but at work we use Jupyter Notebooks for design and documentation so it is handy to be able to demonstrate new web client features within a particular notebook.
### Custom CSS
Use `%%htm... | github_jupyter |
```
import pandas as pd
import numpy as np
import time
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import preprocessing as pp
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import accuracy_score
from sklearn import preprocessing
import xgboost as xgb
from sklearn.ensembl... | github_jupyter |
## MinMaxScaler
```
from pandas import Series
from sklearn.preprocessing import MinMaxScaler
data = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0]
series = Series(data)
print(series)
values = series.values
values = values.reshape((len(values), 1))
print(values)
print(values.shape)
scaler = MinMaxScaler(... | github_jupyter |
## Tools for CSV FIle Processing
### Gather Phase Tools
This importable notebook provides the tooling necessary to handle the processing for the **Gather Phases** in the ETL process for the NOAA HDTA project. This tooling supports Approaches 1 and 2 using **CSV files**.
Each of the process phases require a dictiona... | github_jupyter |
```
# Copyright 2020 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 |
# Getting Started with CLX and Streamz
This is a guide on how [CLX](https://github.com/rapidsai/clx) and [Streamz](https://streamz.readthedocs.io/en/latest/) can be used to build a streaming inference pipeline.
Streamz has the ability to read from [Kafka](https://kafka.apache.org/) directly into [Dask](https://dask.o... | github_jupyter |
# About OCR approach1:
Through ocr1.py script we are targeting to train a small Convolutional Neurl Network (CNN) with the data we generated using random_string_data_gen.py. Network should be able to recognize the random string in a given image and provide it as ouput.
In the first phase we will be testing it using ... | github_jupyter |
# TV Script Generation
In this project, I have tried to generate my own [Seinfeld](https://en.wikipedia.org/wiki/Seinfeld) TV scripts using RNNs. I have used part of the [Seinfeld dataset](https://www.kaggle.com/thec03u5/seinfeld-chronicles#scripts.csv) of scripts from 9 seasons. The Neural Network will generate a ne... | github_jupyter |
```
import panel as pn
pn.extension('plotly')
```
The ``HoloViews`` pane renders HoloViews plots with one of the plotting backends supported by HoloViews. It supports the regular HoloViews widgets for exploring the key dimensions of a ``HoloMap`` or ``DynamicMap``, but is more flexible than the native HoloViews widget... | github_jupyter |
```
import pandas as pd
#Loading data from the Github repository to colab notebook
filename = 'https://raw.githubusercontent.com/PacktWorkshops/The-Data-Science-Workshop/master/Chapter15/Dataset/crx.data'
# Loading the data using pandas
credData = pd.read_csv(filename,sep=",",header = None,na_values = "?")
credData.he... | github_jupyter |
# scatter_selector widget
A set of custom matplotlib widgets that allow you to select points on a scatter plot as use that as input to other interactive plots. There are three variants that differ only in what they pass to their callbacks:
1. {obj}`.scatter_selector`: callbacks will receive `index, (x, y)` where `ind... | github_jupyter |
```
# Dependencies and Setup
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import requests
import time
from scipy.stats import linregress
# Import API key
from api_keys import weather_api_key
# Incorporated citipy to determine city based on latitude and longitude
from citipy import citipy
# ... | github_jupyter |
<h4>Unit 1 <h1 style="text-align:center"> Chapter 4</h1>
---
## Normalization
> Normalization is the task of putting words/tokens in a standard format.Normalization is benefecial despite the spelling information that is lost.
#### Case folding
---
> Mapping everything to the same case is called case folding.
... | github_jupyter |
# Calculating Thermodynamics Observables with a quantum computer
```
# imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from functools import partial
from qiskit.utils import QuantumInstance
from qiskit import Aer
from qiskit.algorithms import NumPyMinimumEigensolver, VQE
from qiskit_na... | github_jupyter |
# PICES Regional Ecosystem Tool
## Data acquisition, analysis, plotting & saving to facilitate IEA report
### Developed by Chelle Gentemann (cgentemann@gmail.com) & Marisol Garcia-Reyes (marisolgr@gmail.com)
***
***
# Instructions
## To configure:
### In the cell marked by <b>`* Configuration *`</b>, specify: Regio... | github_jupyter |
# August 2021 CVE Data
This notebook will pull all [JSON Data](https://nvd.nist.gov/vuln/data-feeds#JSON_FEED) from the NVD and performs some basic data analysis of CVEd data.
## Getting Started
### Collecting Data
This cell pulls all JSON files from the NVD that we will be working with.
```
%%capture
!mkdir -p js... | github_jupyter |
## Explore The Data: Explore Continuous Features
Using the Titanic dataset from [this](https://www.kaggle.com/c/titanic/overview) Kaggle competition.
This dataset contains information about 891 people who were on board the ship when departed on April 15th, 1912. As noted in the description on Kaggle's website, some p... | github_jupyter |
# Get vaccine coverage by ZIP Codes data from CDPH
```
%load_ext lab_black
import pandas as pd
import datetime as dt
import json
import os
import glob
import urllib.request
pd.options.display.max_columns = 50
pd.options.display.max_rows = 1000
pd.set_option("display.max_colwidth", None)
today = dt.datetime.today().str... | github_jupyter |
## This notebook contains prototyping work for implementing the viterbi decode algorithm
```
import numpy as np
import librosa
import matplotlib.pyplot as plt
def redistribute_trans_table(A):
for i in range(5,A.shape[1]):
current_col = A[:,i]
idx = (-current_col).argsort()[:2]
second_max_v... | github_jupyter |
# Two Degree-of-Freedom Caldera Model
## Introduction
Dynamical matching is an interesting chemical dynamical phenomenon that occurs in a variety of organic chemical reactions. A caldera PES arises in many organic chemical reactions, such as the vinylcyclopropane-cyclopentene rearrangement \cite{baldwin2003,gold1... | github_jupyter |
```
import numpy as np
import pandas as pd
amplifiers = np.genfromtxt('amplifiers_0.csv',delimiter=',').astype(int)
print(amplifiers)
normals = 1-amplifiers
print(normals)
weights_biased = np.atleast_2d(np.genfromtxt('weights-biased_0.csv', delimiter=','))
weights_unbiased = np.atleast_2d(np.genfromtxt('weights-unbiase... | github_jupyter |
# Example: CanvasXpress scatter2d Chart No. 4
This example page demonstrates how to, using the Python package, create a chart that matches the CanvasXpress online example located at:
https://www.canvasxpress.org/examples/scatter2d-4.html
This example is generated using the reproducible JSON obtained from the above p... | github_jupyter |
# MNIST
```
import torch
from torch import nn, optim
from torchvision import datasets, transforms
import numpy as np
%matplotlib inline
from matplotlib import pyplot as plt
```
### Description
Classification of hand-written digits (MNIST dataset) using a simple multi-layer perceptorn architecture implemented in PyTo... | github_jupyter |
# MLflow Training Tutorial
This `train.pynb` Jupyter notebook is an example for using elastalert with mlflow together.
> This is the Jupyter notebook version of the `train.py` example
```
from sklearn.svm import OneClassSVM
ES_URL = "http://192.168.122.3:9200"
ES_INDEX = "logs-endpoint-winevent-sysmon-*"
COLUMNS = ... | github_jupyter |
## Baysian Ridge and Lasso regression
```
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import norm
import pymc
import sys
%matplotlib inline
n = 10000
x1 = norm.rvs(0, 1, size=n) + norm.rvs(0, 10**-3, size=n)
x2 = -x1 + norm.rvs(0, 10**-3, size=n)
x3 = norm.rvs(0, 1, size=n)
X = np.column_sta... | github_jupyter |
<a href="https://hub.callysto.ca/jupyter/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fcallysto%2Fcurriculum-notebooks&branch=master&subPath=Mathematics/CombinedLogLaw/combined-log... | github_jupyter |
# Confounding Example: Finding causal effects from observed data
Suppose you are given some data with treatment and outcome. Can you determine whether the treatment causes the outcome, or the correlation is purely due to another common cause?
```
import os, sys
sys.path.append(os.path.abspath("../../"))
import numpy ... | github_jupyter |
# 수학
## 1) 나머지 연산
C++
- int: 2^31-1
- long long: 2^63-1
- 10^18 정도, 따라서 정답을 나눈 값을 return하도록 함
- **답을 M으로 나눈 나머지 출력**
1) 덧셈 곱셈(https://www.acmicpc.net/problem/10430)
~~~
# mod = %
(A + B) % M = {(A % C) + (B % C)} % C
(A X B) % M = {(A % C) X (B % C)} % C
~~~
2) 뺄셈: 더해주고 나눔
~~~
0 <= A % C <= C
0 <= B % C <= C
-C < A ... | github_jupyter |
# Serve a Pytorch model trained on SageMaker
The model for this example was trained using this sample notebook on sagemaker - https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/pytorch_mnist/pytorch_mnist.ipynb
It is certainly easiler to do estimator.deploy() using the standard Sage... | github_jupyter |
```
import pandas as pd
import itertools
file = 'legacy_data/Amtsblatt_1918.xlsx'
res_type_scheme, _ = SkosConceptScheme.objects.get_or_create(dc_title='res_type')
archiv, _ = Institution.objects.get_or_create(
written_name='Wiener Stadt- und Landesarchiv',
abbreviation="WStLA",
institution_type="Archiv"
)
... | github_jupyter |
## Dependencies
```
import json, glob
from tweet_utility_scripts import *
from tweet_utility_preprocess_roberta_scripts import *
from transformers import TFRobertaModel, RobertaConfig
from tokenizers import ByteLevelBPETokenizer
from tensorflow.keras import layers
from tensorflow.keras.models import Model
```
# Load ... | github_jupyter |
# Inference only Text Models in `arcgis.learn`
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc">
<ul class="toc-item">
<li><span><a href="#Introduction" data-toc-modified-id="Introduction-1">Introduction</a></span></li>
<li><span><a href="#Transformer-Basics" data-toc-modified-id="Transformer-B... | 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 ... | github_jupyter |
```
import numpy as np
import xarray as xr
from matplotlib import pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (8,5)
from scipy.interpolate import *
x = np.linspace(0, 2*np.pi, 11)[0:-1]
y = np.sin(x)
# analytic derivative:
dydxa = np.cos(x)
# numpy derivatives:
dydx1 = np.diff(y)/np.diff(x)
np.s... | github_jupyter |
# MNIST Dataset & Database
In the [MNIST tutorial](https://github.com/caffe2/caffe2/blob/master/caffe2/python/tutorials/MNIST.ipynb) we use an lmdb database. You can also use leveldb or even minidb by changing the type reference when you get ready to read from the dbs. In this tutorial, we will go over how to download... | github_jupyter |
# The Schrödinger Equation
>The underlying physical laws necessary for the mathematical theory of a large part of physics and the whole of chemistry are thus completely known, an... | github_jupyter |
# Machine Learning Foundation
## Section 2, Part d: Regularization and Gradient Descent
## Introduction
We will begin with a short tutorial on regression, polynomial features, and regularization based on a very simple, sparse data set that contains a column of `x` data and associated `y` noisy data. The data file i... | github_jupyter |
## Please input your directory for the top level folder
folder name : SUBMISSION MODEL
```
dir_ = 'INPUT-PROJECT-DIRECTORY/submission_model/' # input only here
```
#### setting other directory
```
raw_data_dir = dir_+'2. data/'
processed_data_dir = dir_+'2. data/processed/'
log_dir = dir_+'4. logs/'
model_dir = dir_... | github_jupyter |
```
from __future__ import print_function
import os
import pandas as pd
import numpy as np
%matplotlib inline
from matplotlib import pyplot as plt
#Read dataset into pandas DataFrame
df = pd.read_csv('datasets/chemical-concentration-readings.csv')
#Let's see the shape of the dataset
print('Shape of the dataset:', df.sh... | github_jupyter |
```
import sys
sys.path.append(r"D:\work\nlp")
from fennlp.datas import dataloader
import tensorflow as tf
from fennlp.datas.checkpoint import LoadCheckpoint
from fennlp.datas.dataloader import TFWriter, TFLoader
from fennlp.metrics import Metric
from fennlp.metrics.crf import CrfLogLikelihood
from fennlp.models import... | github_jupyter |
<img src="https://upload.wikimedia.org/wikipedia/commons/4/47/Logo_UTFSM.png" width="200" alt="utfsm-logo" align="left"/>
# MAT281
### Aplicaciones de la Matemática en la Ingeniería
## Módulo 03
## Laboratorio Clase 02: Visualización Imperativa
### Instrucciones
* Completa tus datos personales (nombre y rol USM) e... | 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 to... | github_jupyter |
```
import pyspark
import os
from datetime import date
import functools
from IPython.core.display import display, HTML
#import findspark
#findspark.init()
from pyspark.sql import SparkSession
import pyspark.sql.functions as F
import pyspark.sql.types as T
from pyspark.sql.functions import to_timestamp, count, isnan, ... | github_jupyter |
# Table of Contents
<div class="toc" style="margin-top: 1em;"><ul class="toc-item" id="toc-level0"><li><span><a href="http://localhost:8889/notebooks/19-full-res-model-all-angles-vertical-cut-no-bbox.ipynb#Load-libraries" data-toc-modified-id="Load-libraries-1"><span class="toc-item-num">1 </span>Load libra... | github_jupyter |
<small><small><i>
All the IPython Notebooks in this **Python Examples** series by Dr. Milaan Parmar are available @ **[GitHub](https://github.com/milaan9/90_Python_Examples)**
</i></small></small>
# Python Program to Make a Simple Calculator
In this example you will learn to create a simple calculator that can add, s... | github_jupyter |
# COVID-19 Analysis
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Read the data
confirmed = pd.read_csv('data/time_series_covid19_confirmed_global.csv')
confirmed.rename(columns={'Country/Region':'country'}, inplace=True)
confirmed = confirmed.drop(columns=['Province/State'])
confirmed ... | github_jupyter |
# Building Model for MALARIA DETECTION
### Lets take a look at how we are gonna make our model
#### Step 1: Loading and Splitting of the Dataset
- The first step is to load the data and scaling the images to binary 0 and 1 from Parasitized and Uninfected.
- Then we will resize the images to 50 x 50
- After that s... | github_jupyter |
```
##########################################################
# Relative Imports
##########################################################
import sys
from os.path import isfile
from os.path import join
def find_pkg(name: str, depth: int):
if depth <= 0:
ret = None
else:
d = [".."] * depth
... | github_jupyter |
```
from dataretrieval import nwis
```
The dataRetrieval package was created as a python equivalent to the R dataRetrieval tool.
The following shows python equivalents for methods outlined in the R dataRetrieval Vignette with the equivalent R code in comments
```
'''
library(dataRetrieval)
# Choptank River near Gree... | github_jupyter |
```
# default_exp inference
```
# Inference
> This contains the code required for inference.
```
# export
from fastai.learner import load_learner
from fastai.callback.core import GatherPredsCallback
from fastai.learner import Learner
from fastcore.basics import patch
from fastcore.meta import delegates
#export
@patc... | github_jupyter |
# Writing Down Qubit States
```
from qiskit import *
```
In the previous chapter we saw that there are multiple ways to extract an output from a qubit. The two methods we've used so far are the z and x measurements.
```
# z measurement of qubit 0
measure_z = QuantumCircuit(1,1)
measure_z.measure(0,0);
# x measureme... | github_jupyter |
# Archivos y Bases de datos
La idea de este taller es manipular archivos (leerlos, parsearlos y escribirlos) y hacer lo mismo con bases de datos estructuradas.
## Ejercicio 1
Baje el archivo de "All associations with added ontology annotations" del GWAS Catalog.
+ https://www.ebi.ac.uk/gwas/docs/file-downloads
Desc... | github_jupyter |
# [deplacy](https://koichiyasuoka.github.io/deplacy/)을 사용한 문법 분석
## [Camphr-Udify](https://camphr.readthedocs.io/en/latest/notes/udify.html)로 분석
```
!pip install deplacy camphr 'unofficial-udify>=0.3.0' en-udify@https://github.com/PKSHATechnology-Research/camphr_models/releases/download/0.7.0/en_udify-0.7.tar.gz
impo... | github_jupyter |
# Lightweight Networks and MobileNet
We have seen that complex networks require significant computational resources, such as GPU, for training, and also for fast inference. However, it turns out that a model with significanly smaller number of parameters in most cases can still be trained to perform resonably well. In... | github_jupyter |
## This notebook Contains:
- Taking scraped input(HTML formatted code)
- Cleaning, Data Preprocessing and Feature Engineering on the data set
- Importing the Cleaned CSV File
```
# imoporting libraries
import pandas as pd
import os
from bs4 import BeautifulSoup
import re
# Reading the list of files inside the HTML_FIL... | github_jupyter |
# AutoEncoders
---
The following code was created by Aymeric Damien. You can find some of his code in <a href="https://github.com/aymericdamien">here</a>. We made some modifications for us to import the datasets to Jupyter Notebooks.
Let's call our imports and make the MNIST data available to use.
```
#from __future... | github_jupyter |
# 4장 판다스 데이터프레임 Part1
## 4.2 데이터프레임 인덱스
```
from pandas import DataFrame
data = [
["037730", "3R", 1510, 7.36],
["036360", "3SOFT", 1790, 1.65],
["005670", "ACTS", 1185, 1.28]
]
columns = ["종목코드", "종목명", "현재가", "등락률"]
df = DataFrame(data=data, columns=columns)
df
from pandas import DataFrame
data = [
... | github_jupyter |
```
import re
import os
from misc import *
import numpy as np
import pandas as pd
import pickle as pkl
import os.path as op
from tqdm import tqdm
from copy import deepcopy
import matplotlib.pyplot as plt
from matplotlib.patches import Patch
from scipy.stats import ks_2samp, median_test
from sklearn.metrics import roc_a... | github_jupyter |
# Torch Hub Detection Inference Tutorial
In this tutorial you'll learn:
- how to load a pretrained detection model using Torch Hub
- run inference to detect actions in a demo video
## NOTE:
At the moment tutorial only works if ran on local clone from the directory `pytorchvideo/tutorials/video_detection_example`
#... | github_jupyter |
## What is a Variable?
A variable is any characteristic, number, or quantity that can be measured or counted. The following are examples of variables:
- Age (21, 35, 62, ...)
- Gender (male, female)
- Income (GBP 20000, GBP 35000, GBP 45000, ...)
- House price (GBP 350000, GBP 570000, ...)
- Country of birth (China, ... | github_jupyter |
# Scrapy: part 1
**Scrapy** is a powerful web scraping framework for Python. A framework is still a library ("an API of functions") yet with more powerful built-in features. It can be described as the combination of all we learnt till now including requests, BeautifulSoup, lxml and RegEx. To install **Scrapy**, open t... | github_jupyter |
```
# Imports
import csv
import pandas as pd
import itertools
import math
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
import spacy
import string
import re
import nltk
import random
import praw
from google.colab import files
import seaborn as sns
import numpy as np
from sklearn.feature_... | github_jupyter |
# Supplementary Material: Analyse AdaptiveAttention
AdaptiveAttention is from the paper by Lu et al. (2017):
```
@inproceedings{lu2017knowing,
title={Knowing when to look: Adaptive attention via a visual sentinel for image captioning},
author={Lu, Jiasen and Xiong, Caiming and Parikh, Devi and Socher, Richard},
... | github_jupyter |
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