code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
### Import Library and Dataset
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datetime
pd.set_option('display.max_columns', None)
data_train = pd.read_excel('Data_Train.xlsx')
data_test = pd.read_excel('Data_Test.xlsx')
```
### Combining the Dataset
```
price_train = data_train.Pri... | github_jupyter |
```
# Import dependencies
import pandas as pd
import pathlib
# Identifying CSV file path
csv_path = pathlib.Path('../../Resources/Raw/COVID-19_Case_Surveillance_Public_Use_Data_with_Geography.csv')
# Reading and previewing CSV file
data_df = pd.read_csv(csv_path, low_memory=False)
data_df.head()
# Filtering for only C... | github_jupyter |
```
# Load dependencies
import numpy as np
import pandas as pd
from uncertainties import ufloat
from uncertainties import unumpy
```
# Biomass C content estimation
Biomass is presented in the paper on a dry-weight basis. As part of the biomass calculation, we converted biomass in carbon-weight basis to dry-weight ba... | github_jupyter |
```
!pip install -q efficientnet
import math, re, os, random
import tensorflow as tf, tensorflow.keras.backend as K
import numpy as np
import pandas as pd
import efficientnet.tfkeras as efn
from matplotlib import pyplot as plt
from kaggle_datasets import KaggleDatasets
from sklearn.metrics import f1_score, precision_sc... | github_jupyter |
## A Simple Pair Trading Strategy
**_Please go through the "building strategies" notebook before looking at this notebook._**
Let's build a aimple pair trading strategy to show how you can trade multiple symbols in a strategy. We will trade 2 stocks, Coca-Cola (KO) and Pepsi (PEP)
1. We will buy KO and sell PEP wh... | github_jupyter |
# Introduction
<div class="alert alert-info">
**Code not tidied, but should work OK**
</div>
<img src="../Udacity_DL_Nanodegree/031%20RNN%20Super%20Basics/SimpleRNN01.png" align="left"/>
# Neural Network
```
import numpy as np
import matplotlib.pyplot as plt
import pdb
```
**Sigmoid**
```
def sigmoid(x):
re... | github_jupyter |
<a id="title_ID"></a>
# JWST Pipeline Validation Notebook: calwebb_detector1, dark_current unit tests
<span style="color:red"> **Instruments Affected**</span>: NIRCam, NIRISS, NIRSpec, MIRI, FGS
### Table of Contents
<div style="text-align: left">
<br> [Introduction](#intro)
<br> [JWST Unit Tests](#unit)
<br> ... | github_jupyter |
## Dự án 01: Xây dựng Raspberry PI thành máy tính cho Data Scientist (PIDS)
## Bài 01. Cài đặt TensorFlow và các thư viện cần thiết
##### Người soạn: Dương Trần Hà Phương
##### Website: [Mechasolution Việt Nam](https://mechasolution.vn)
##### Email: mechasolutionvietnam@gmail.com
---
## 1. Mở đầu
Nếu bạn muốn chạy mộ... | github_jupyter |
# Transform points from pitch to texture and vice-versa
### Matrix P - Projection
The P matrix is a 3x4 matrix that given a 3D point in the world reference frame, it is projected into the 2D image in **texture coordinate** reference frame, i.e:
\begin{align}
\mathbf{pt_{world}} = (x, y, z, 1) \\
\mathbf{pt_{pitch}} = ... | github_jupyter |
# House Price Prediction With TensorFlow
[![open_in_colab][colab_badge]][colab_notebook_link]
[![open_in_binder][binder_badge]][binder_notebook_link]
[colab_badge]: https://colab.research.google.com/assets/colab-badge.svg
[colab_notebook_link]: https://colab.research.google.com/github/UnfoldedInc/examples/blob/master... | github_jupyter |
Lambda School Data Science
*Unit 2, Sprint 2, Module 3*
---
<p style="padding: 10px; border: 2px solid red;">
<b>Before you start:</b> Today is the day you should submit the dataset for your Unit 2 Build Week project. You can review the guidelines and make your submission in the Build Week course for your cohort ... | github_jupyter |
# Semantic Image Clustering
**Author:** [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/)<br>
**Date created:** 2021/02/28<br>
**Last modified:** 2021/02/28<br>
**Description:** Semantic Clustering by Adopting Nearest neighbors (SCAN) algorithm.
## Introduction
This example demonstrates how to app... | github_jupyter |
```
import json
import numpy as np
from sklearn.model_selection import train_test_split
import tensorflow.keras as keras
import matplotlib.pyplot as plt
import random
import librosa
import math
# path to json
data_path = "C:\\Users\\Saad\\Desktop\\Project\\MGC\\Data\\data.json"
def load_data(data_path):
with ope... | github_jupyter |
# Notebook 3 - Advanced Data Structures
So far, we have seen numbers, strings, and lists. In this notebook, we will learn three more data structures, which allow us to organize data. The data structures are `tuple`, `set`, and `dict` (dictionary).
## Tuples
A tuple is like a list, but is immutable, meaning that it ca... | github_jupyter |
### Mount Google Drive (Works only on Google Colab)
```
from google.colab import drive
drive.mount('/content/gdrive')
```
# Import Packages
```
import os
import numpy as np
import pandas as pd
from zipfile import ZipFile
from PIL import Image
from tqdm.autonotebook import tqdm
from IPython.display import display
fr... | github_jupyter |
<a id='pd'></a>
<div id="qe-notebook-header" align="right" style="text-align:right;">
<a href="https://quantecon.org/" title="quantecon.org">
<img style="width:250px;display:inline;" width="250px" src="https://assets.quantecon.org/img/qe-menubar-logo.svg" alt="QuantEcon">
</a>
</div>
#... | github_jupyter |
```
import numpy
import scipy
import scipy.sparse
import sklearn.metrics.pairwise
from sklearn import datasets
from sklearn.metrics.pairwise import pairwise_distances
#%%
descriptions = []
with open('descriptions.txt', encoding = "utf8") as f:
for line in f:
text = line.low... | github_jupyter |
# Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is based off of Andrej Karpathy's [post on RNNs](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) and [i... | github_jupyter |
```
print('Kazuma Shachou')
nome_do_filme = "Bakamon"
print(nome_do_filme)
nome_do_filme
import pandas as pd
filmes = pd.read_csv("https://raw.githubusercontent.com/alura-cursos/introducao-a-data-science/master/aula0/ml-latest-small/movies.csv")
filmes.columns = ["filmeid", "titulo", "generos"]
filmes.head()
# Lendo a... | github_jupyter |
```
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | github_jupyter |
# Import Dependencies
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
# scaling and dataset split
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
# OLS, Ridge
from sklearn.linear_model import LinearRegression, Ridge
#... | github_jupyter |
# Regression on Decison Trees and Random Forest
```
#importing important libraries
#libraries for reading dataset
import numpy as np
import pandas as pd
#libraries for data visualisation
import matplotlib.pyplot as plt
import seaborn as sns
#libraries for model building and understanding
import sklearn
from sklearn... | github_jupyter |
# In this note book the following steps are taken:
1. Remove highly correlated attributes
2. Find the best hyper parameters for estimator
3. Find the most important features by tunned random forest
4. Find f1 score of the tunned full model
5. Find best hyper parameter of model with selected features
6. Find f1 score of... | github_jupyter |
# Anailís ghramadaí trí [deplacy](https://koichiyasuoka.github.io/deplacy/)
## le [Stanza](https://stanfordnlp.github.io/stanza)
```
!pip install deplacy stanza
import stanza
stanza.download("ga")
nlp=stanza.Pipeline("ga")
doc=nlp("Táimid faoi dhraíocht ag ceol na farraige.")
import deplacy
deplacy.render(doc)
deplac... | github_jupyter |
<a href="https://colab.research.google.com/github/open-mmlab/mmclassification/blob/master/docs/tutorials/MMClassification_python.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# MMClassification Python API tutorial on Colab
In this tutorial, we wi... | github_jupyter |
<a href="https://colab.research.google.com/github/EnzoGolfetti/imersaoalura_dados_3/blob/main/aula05_imersao_alura_dados.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#Análise de Drugs Discovery - Imersão Dados Alura
Desafio 1: Investigar por que ... | github_jupyter |
<div class="alert alert-block alert-info">
<b><h1>ENGR 1330 Computational Thinking with Data Science </h1></b>
</div>
Copyright © 2021 Theodore G. Cleveland and Farhang Forghanparast
Last GitHub Commit Date:
# 14: Visual display of data
This lesson is a prelude to the `matplotlib` external module package... | github_jupyter |
```
from os import listdir
from numpy import array
from keras.preprocessing.text import Tokenizer, one_hot
from keras.preprocessing.sequence import pad_sequences
from keras.models import Model, Sequential, model_from_json
from keras.utils import to_categorical
from keras.layers.core import Dense, Dropout, Flatten
from ... | github_jupyter |
```
```
# **Deep Convolutional Generative Adversarial Network (DC-GAN):**
DC-GAN is a foundational adversarial framework developed in 2015.
It had a major contribution in streamlining the process of designing adversarial frameworks and visualizing intermediate representations, thus, making GANs more accessible to b... | github_jupyter |
## Code Equivalence
```
import ast
import astpretty
import showast
import sys
import re
sys.path.insert(0, '../preprocess/')
sys.path.insert(0, '../../coarse2fine.git/src')
from sketch_generation import Sketch
from tree import SketchRepresentation
import table
SKP_WORD = '<sk>'
RIG_WORD = '<]>'
LFT_WORD = '<[>'
def... | github_jupyter |
# Import Scikit Learn, Pandas and Numpy
```
import sklearn
import numpy as np
import pandas as pd
```
# 1. Read the Dataset using Pandas
```
data = pd.read_csv("data/amazon_baby.csv")
data
```
# 2. Exploratory Data Analysis
```
data.head()
data.info()
```
### The first observation is that we have cells with null ... | github_jupyter |
```
%autosave 0
```
# MCPC rehearsal problem Oct 25 2017 at UCSY
## Problem E: Stacking Plates
### Input format
- 1st Line: 1 integer, Number of Test Case, each Test Case has following data
+ 1 Line: 1 integer, **n**(Number of Stacks)
+ **n** Lines: first integer: **h** (Number of Plates), and **h** integers ... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
from functools import reduce
import seaborn as sns; sns.set(rc={'figure.figsize':(15,15)})
import numpy as np
import pandas as pd
from sqlalchemy import create_engine
from sklearn.preprocessing import MinMaxScaler
engine = create_engine('postgresql://postgres:mimi... | github_jupyter |
[View in Colaboratory](https://colab.research.google.com/github/tjh48/DCGAN/blob/master/dcgan.ipynb)
First we'll import all the tools we need and set some initial parameters
```
import numpy as np
from sklearn.utils import shuffle
import matplotlib.pyplot as plt
import os
from keras.models import *
from keras.layers... | github_jupyter |
## Preprocessing
```
# Import our dependencies
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import pandas as pd
import tensorflow as tf
# Import and read the charity_data.csv.
import pandas as pd
application_df = pd.read_csv("Resources/charity_data.csv")
appl... | github_jupyter |
# Puts ALL WISE Astrometry reference catalogues into GAIA reference frame
<img src=https://avatars1.githubusercontent.com/u/7880370?s=200&v=4>
The WISE catalogues were produced by ../dmu16_allwise/make_wise_samples_for_stacking.csh
In the catalogue, we keep:
- The position;
- The chi^2
This astrometric correction ... | github_jupyter |
```
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file... | github_jupyter |
```
from pykat import finesse
from pykat.commands import *
import numpy as np
import matplotlib.pyplot as plt
import scipy
from IPython import display
pykat.init_pykat_plotting(dpi=200)
base1 = """
l L0 10 0 n0 #input laser
... | github_jupyter |
-> Associated lecture videos:
in Neural Networks/Lesson 4 - Deep Learning with PyTorch: video 4, video 5, video 6, video 7
# Introduction to Deep Learning with PyTorch
In this notebook, you'll get introduced to [PyTorch](http://pytorch.org/), a framework for building and training neural networks. PyTorch in a lot o... | github_jupyter |
# Chapter 7 - Iterations
-------------------------------
Computers do not get bored. If you want the computer to repeat a certain task hundreds of thousands of times, it does not protest. Humans hate too much repetition. Therefore, repetitious tasks should be performed by computers. All programming languages support ... | github_jupyter |
# 2D Advection-Diffusion equation
in this notebook we provide a simple example of the DeepMoD algorithm and apply it on the 2D advection-diffusion equation.
```
# General imports
import numpy as np
import torch
import matplotlib.pylab as plt
# DeepMoD functions
from deepymod import DeepMoD
from deepymod.model.func_... | github_jupyter |
# Web crawling exercise
```
from selenium import webdriver
```
## Quiz 1
- 아래 URL의 NBA 데이터를 크롤링하여 판다스 데이터 프레임으로 나타내세요.
- http://stats.nba.com/teams/traditional/?sort=GP&dir=-1
### 1.1 webdriver를 실행하고 사이트에 접속하기
```
driver = webdriver.Chrome()
url = "http://stats.nba.com/teams/traditional/?sort=GP&dir=-1"
driver.get(... | github_jupyter |
### Functions
```
# syntax
# Declaring a function
def function_name():
codes
codes
# Calling a function
function_name()
def generate_full_name ():
first_name = 'Ayush'
last_name = 'Jindal'
space = ' '
full_name = first_name + space + last_name
print(full_name)
generate_full_name () # c... | github_jupyter |
# Custom Building Recurrent Neural Network
**Notation**:
- Superscript $[l]$ denotes an object associated with the $l^{th}$ layer.
- Superscript $(i)$ denotes an object associated with the $i^{th}$ example.
- Superscript $\langle t \rangle$ denotes an object at the $t^{th}$ time-step.
- **Sub**script $i$ den... | github_jupyter |
# Data description:
I'm going to solve the International Airline Passengers prediction problem. This is a problem where given a year and a month, the task is to predict the number of international airline passengers in units of 1,000. The data ranges from January 1949 to December 1960 or 12 years, with 144 observation... | github_jupyter |
<font color ='0c6142' size=6)> Chemistry </font> <font color ='708090' size=3)> for Technology Students at GGC </font>
<font color ='708090' size=3)> Measurement Uncertainty, Accuracy, and Precision </font>
[Textbook: Chapter 1, section 5](https://openstax.org/books/chemistry-2e/pages/1-5-measurement-uncertainty-a... | github_jupyter |
```
import sys
sys.path.insert(1, '/home/maria/Documents/EnsemblePursuit')
from EnsemblePursuit.EnsemblePursuit import EnsemblePursuit
import numpy as np
from scipy.stats import zscore
import matplotlib.pyplot as plt
from scipy.ndimage import gaussian_filter, gaussian_filter1d
data_path='/home/maria/Documents/data_for_... | github_jupyter |
# Comparing soundings from NCEP Reanalysis and various models
We are going to plot the global, annual mean sounding (vertical temperature profile) from observations.
Read in the necessary NCEP reanalysis data from the online server.
The catalog is here: <https://psl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanaly... | github_jupyter |
<a href="https://colab.research.google.com/github/boopathiviky/Eulers-Project/blob/repo/euler's.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#1.multiples of 3 and 5
```
sum1=0
x=int(input())
for i in range(1,x):
if(i%3==0 or i%5==0):
... | github_jupyter |
# `numpy`
မင်္ဂလာပါ၊ welcome to the week 07 of Data Science Using Python.
We will go into details of `numpy` this week (as well as do some linear algebra stuffs).
## `numpy` အကြောင်း သိပြီးသမျှ
* `numpy` ဟာ array library ဖြစ်တယ်၊
* efficient ဖြစ်တယ်၊
* vector နဲ့ matrix တွေကို လွယ်လွယ်ကူကူ ကိုင်တွယ်နိုင်တယ... | github_jupyter |
# Building your Deep Neural Network: Step by Step
Welcome to your week 4 assignment (part 1 of 2)! Previously you trained a 2-layer Neural Network with a single hidden layer. This week, you will build a deep neural network with as many layers as you want!
- In this notebook, you'll implement all the functions require... | github_jupyter |
```
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | github_jupyter |
```
import pandas as pd
import datetime
from finquant.portfolio import build_portfolio
from finquant.moving_average import compute_ma, ema
from finquant.moving_average import plot_bollinger_band
from finquant.efficient_frontier import EfficientFrontier
### DOES OUR OPTIMIZATION ACTUALLY WORK?
# COMPARING AN OPTIMIZED ... | github_jupyter |
# Isolation Forest (IF) outlier detector deployment
Wrap a scikit-learn Isolation Forest python model for use as a prediction microservice in seldon-core and deploy on seldon-core running on minikube or a Kubernetes cluster using GCP.
## Dependencies
- [helm](https://github.com/helm/helm)
- [minikube](https://github... | github_jupyter |
```
import numpy as np
import pandas as pd
import os
import spacy
import en_core_web_sm
from spacy.lang.en import English
from spacy.lang.en.stop_words import STOP_WORDS
import string
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
fro... | github_jupyter |
# Implementing logistic regression from scratch
The goal of this notebook is to implement your own logistic regression classifier. We will:
* Extract features from Amazon product reviews.
* Convert an SFrame into a NumPy array.
* Implement the link function for logistic regression.
* Write a function to compute t... | github_jupyter |
## Demo: MultiContainer feeder example
The basic steps to set up an OpenCLSim simulation are:
* Import libraries
* Initialise simpy environment
* Define object classes
* Create objects
* Create sites
* Create vessels
* Create activities
* Register processes and run simpy
----
#### 0. Import libraries
```
impor... | github_jupyter |
```
from IPython.display import Latex
# Latex(r"""\begin{eqnarray} \large
# Z_{n+1} = Z_{n}^(-e^(Z_{n}^p)^(e^(Z_{n}^p)^(-e^(Z_{n}^p)^(e^(Z_{n}^p)^(-e^(Z_{n}^p))))))
# \end{eqnarray}""")
```
# Parameterized machine learning algo:
## tanh(Z) = (a exp(Z) - b exp(-Z)) / (c exp(Z) + d exp(-Z))
### with parameters a,b,c,... | github_jupyter |
# Hashtags
```
from nltk.tokenize import TweetTokenizer
import os
import pandas as pd
import re
import sys
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation
from IPython.display import clear_output
def squeal(text=None):
clear_output(wait=True)
... | github_jupyter |
```
from IPython.core.display import HTML
HTML("<style>.container { width:95% !important; }</style>")
```
# Lecture 11, Solution methods for multiobjective optimization
## Reminder:
### Mathematical formulation of multiobjective optimization problems
Multiobjective optimization problems are often formulated as
$$
\... | github_jupyter |
```
import pandas as pd
import os, sys
from sklearn.gaussian_process import GaussianProcessRegressor
import shapefile
from functools import partial
import pyproj
from shapely.geometry import shape, Point, mapping
from shapely.ops import transform
processeddir = "../data/processed/"
rawdir = "../data/raw/"
df = pd.read_... | github_jupyter |
# Graph
> in progress
- toc: true
- badges: true
- comments: true
- categories: [self-taught]
- image: images/bone.jpeg
- hide: true
https://towardsdatascience.com/using-graph-convolutional-neural-networks-on-structured-documents-for-information-extraction-c1088dcd2b8f
CNNs effectively capture patterns in data in Eu... | github_jupyter |
# All those moments will be los(s)t in time, like tears in rain.
> I am completely operational, and all my circuits are functioning perfectly.
- toc: true
- badges: true
- comments: true
- categories: [jupyter]
- image: images/posts/2020-12-10-Tears-In-Rain/Tears-In-Rain.jpg
```
#hide
!pip install -Uqq fastbook
impo... | github_jupyter |
# 08 - Common problems & bad data situations
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons Licence" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" title='This work is licensed under a Creative Commons Attribution 4.0 International License.... | github_jupyter |
## Define the Convolutional Neural Network
After you've looked at the data you're working with and, in this case, know the shapes of the images and of the keypoints, you are ready to define a convolutional neural network that can *learn* from this data.
In this notebook and in `models.py`, you will:
1. Define a CNN w... | github_jupyter |
```
import tensorflow as tf
import tensorflow as tf
from tensorflow.python.keras.applications.vgg19 import VGG19
model=VGG19(
include_top=False,
weights='imagenet'
)
model.trainable=False
model.summary()
from tensorflow.python.keras.preprocessing.image import load_img, img_to_array
from tensorflow.python.keras.... | github_jupyter |
```
from sklearn import datasets
wine = datasets.load_wine()
print(wine.DESCR)
print('Features: ', wine.feature_names)
print('Labels: ', wine.target_names)
#wine=wine.sample(frac=1)
data = wine.data
target = wine.target
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_s... | github_jupyter |
```
%reload_ext watermark
%matplotlib inline
from os.path import exists
from metapool.metapool import *
from metapool import (validate_plate_metadata, assign_emp_index, make_sample_sheet, KLSampleSheet, parse_prep, validate_and_scrub_sample_sheet, generate_qiita_prep_file)
%watermark -i -v -iv -m -h -p metapool,sample... | github_jupyter |
<p align="center">
<h1 align="center">Machine Learning and Statistics Tasks 2020</h1>
<h1 align="center"> Task 1: Python function sqrt2</h1>
<h2 align="center"> Author: Ezekiel Onaloye</h2>
<h2 align="center"> Created: November 2020 </h2>
</p>
### Task 1
Write a Python fu... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from tqdm.autonotebook import tqdm
from joblib import Parallel, delayed
import umap
import pandas as pd
from avgn.utils.paths import DATA_DIR, most_recent_subdirectory, ensure_dir
DATASET_ID = 'swamp_sparrow'
fr... | github_jupyter |
<img src="../../images/banners/python-basics.png" width="600"/>
# <img src="../../images/logos/python.png" width="23"/> Conda Environments
## <img src="../../images/logos/toc.png" width="20"/> Table of Contents
* [Understanding Conda Environments](#understanding_conda_environments)
* [Understanding Basic Package Man... | github_jupyter |
# Configuraciones para el Grupo de Estudio
<img src="./img/f_mail.png" style="width: 700px;"/>
## Contenidos
- ¿Por qué jupyter notebooks?
- Bash
- ¿Que es un *kernel*?
- Instalación
- Deberes
## Python y proyecto Jupyter
<img src="./img/py.jpg" style="width: 500px;"/>
<img src="./img/jp.png" style="width: 100px;"/... | github_jupyter |
# Generación de observaciones aleatorias a partir de una distribución de probabilidad
La primera etapa de la simulación es la **generación de números aleatorios**. Los números aleatorios sirven como el bloque de construcción de la simulación. La segunda etapa de la simulación es la **generación de variables aleatorias... | github_jupyter |
# MHKiT Quality Control Module
The following example runs a simple quality control analysis on wave elevation data using the [MHKiT QC module](https://mhkit-software.github.io/MHKiT/mhkit-python/api.qc.html). The data file used in this example is stored in the [\\\\MHKiT\\\\examples\\\\data](https://github.com/MHKiT-S... | github_jupyter |
```
import numpy as np
import pandas as pd
import scipy as sp
from scipy.stats import mode
from sklearn import linear_model
import matplotlib
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn import preprocessing
import sklearn as sk
import sklearn.discriminant_analysis as da
import skl... | github_jupyter |
```
import random
import copy
import os
import time
import numpy as np
import matplotlib.pyplot as plt
import warnings
# warnings.filterwarnings("ignore")
%matplotlib inline
class Dataset:
def __init__(self,X,y,proportion=0.8,shuffle=True, mini_batch=0):
"""
Dataset class provide tools to manage dat... | github_jupyter |
```
import tushare as ts
import sina_data
import numpy as np
import pandas as pd
from pandas import DataFrame, Series
from datetime import datetime, timedelta
from dateutil.parser import parse
import time
import common_util
import os
def get_time(date=False, utc=False, msl=3):
if date:
time_fmt = "%Y-%m-%d ... | github_jupyter |
```
%matplotlib inline
%pylab inline
pylab.rcParams['figure.figsize'] = (10, 6)
import numpy as np
from numpy.lib import stride_tricks
import cv2
from matplotlib.colors import hsv_to_rgb
import matplotlib.pyplot as plt
import numpy as np
np.set_printoptions(precision=3)
class PatchMatch(object):
def __init__(self, ... | github_jupyter |
```
# -*- coding: utf-8 -*-
# เรียกใช้งานโมดูล
file_name="data"
import codecs
from tqdm import tqdm
from pythainlp.tokenize import word_tokenize
#import deepcut
from pythainlp.tag import pos_tag
from nltk.tokenize import RegexpTokenizer
import glob
import nltk
import re
# thai cut
thaicut="newmm"
from sklearn_crfsuite ... | github_jupyter |
# Introduction
This notebook can be used to both train a discriminator on the AG news dataset and steering text generation in the direction of each of the four classes of this dataset, namely world, sports, business and sci/tech.
My code uses and builds on a text generation plug-and-play model developed by the Ube... | github_jupyter |
```
%run ../Python_files/util_data_storage_and_load.py
%run ../Python_files/load_dicts.py
%run ../Python_files/util.py
import numpy as np
from numpy.linalg import inv
# load link flow data
import json
with open('../temp_files/link_day_minute_Apr_dict_JSON_adjusted.json', 'r') as json_file:
link_day_minute_Apr_dic... | github_jupyter |
<a href="https://colab.research.google.com/github/sapinspys/DS-Unit-2-Regression-Classification/blob/master/DS7_Sprint_Challenge_5_Regression_Classification.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_
# Reg... | github_jupyter |
```
import cv2
import numpy as np
import matplotlib.pyplot as plt
import glob
import pandas as pd
import os
def imshow(img):
img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
plt.imshow(img)
def get_lane_mask(sample,lane_idx):
points_lane = []
h_max = np.max(data['h_samples'][sample])
h_min = np.min(data['h... | github_jupyter |
# Navigation
---
In this notebook, you will learn how to use the Unity ML-Agents environment for the first project of the [Deep Reinforcement Learning Nanodegree](https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893).
### 1. Start the Environment
We begin by importing some necessary packages... | github_jupyter |
# Data Download - *read description before running*
Term project for ESS 490/590
Grad: Erik Fredrickson
Undergrad: Ashika Capirala
*This notebook demonstrates how the open access datasets can be downloaded, but these data are provided at significantly higher temporal resolution than needed for the purposes of our s... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, ReLU, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.losses import categorical_cro... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
# Character level language model - Dinosaurus Island
Welcome to Dinosaurus Island! 65 million years ago, dinosaurs existed, and in this assignment they are back. You are in charge of a special task. Leading biology researchers are creating new breeds of dinosaurs and bringing them to life on earth, and your job is to ... | github_jupyter |
```
import cv2 as cv
import matplotlib.pyplot as plt
import numpy as np
import argparse
net = cv.dnn.readNetFromTensorflow("graph_opt.pb")
inWidth = 368
inHeight = 368
thr = 0.2
BODY_PARTS = { "Nose": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
"LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip"... | github_jupyter |
## Moodle Database: Educational Data Log Analysis
The Moodle LMS is a free and open-source learning management system written in PHP and distributed under the GNU General Public License. It is used for blended learning, distance education, flipped classroom and other e-learning projects in schools, universities, work... | github_jupyter |
# Format DataFrame
```
import pandas as pd
from sklearn.datasets import make_regression
data = make_regression(n_samples=600, n_features=50, noise=0.1, random_state=42)
train_df = pd.DataFrame(data[0], columns=["x_{}".format(_) for _ in range(data[0].shape[1])])
train_df["target"] = data[1]
print(train_df.shape)
tra... | github_jupyter |
# Durables vs Non Durables At Low And High Frequencies
```
!pip install numpy
!pip install matplotlib
!pip install pandas
!pip install pandas_datareader
!pip install datetime
!pip install seaborn
# Some initial setup
from matplotlib import pyplot as plt
import numpy as np
plt.style.use('seaborn-darkgrid')
import pan... | github_jupyter |
[source](../../api/alibi_detect.od.isolationforest.rst)
# Isolation Forest
## Overview
[Isolation forests](https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf) (IF) are tree based models specifically used for outlier detection. The IF isolates observations by randomly selecting a feature and then rando... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
import warnings
warnings.filterwarnings('ignore')
# !pip install tensorflow_text
!pip install transformers emoji
# !pip install ktrain
from transformers import AutoTokenizer
import pandas as pd
dataset = pd.read_excel("/content/drive/MyDrive/English Cate... | github_jupyter |
# Self-Driving Car Engineer Nanodegree
## Project: **Finding Lane Lines on the Road**
***
In this project, you will use the tools you learned about in the lesson to identify lane lines on the road. You can develop your pipeline on a series of individual images, and later apply the result to a video stream (really j... | github_jupyter |
# Classification models using python and scikit-learn
There are many users of online trading platforms and these companies would like to run analytics on and predict churn based on user activity on the platform. Keeping customers happy so they do not move their investments elsewhere is key to maintaining profitability... | github_jupyter |
```
from bs4 import BeautifulSoup as bs
from splinter import Browser
import pandas as pd
with Browser("chrome") as browser:
# Visit URL
url = "https://mars.nasa.gov/news/"
browser.visit(url)
browser.fill('search', 'splinter - python acceptance testing for web applications')
# Find and click the 'sea... | github_jupyter |
<small><small><i>
Introduction to Python - available from https://gitlab.erc.monash.edu.au/andrease/Python4Maths.git
The original version was written by Rajath Kumar and is available at https://github.com/rajathkumarmp/Python-Lectures.
The notes have been updated for Python 3 and amended for use in Monash University m... | github_jupyter |
# Overfitting demo
## Create a dataset based on a true sinusoidal relationship
Let's look at a synthetic dataset consisting of 30 points drawn from the sinusoid $y = \sin(4x)$:
```
import graphlab
import math
import random
import numpy
from matplotlib import pyplot as plt
%matplotlib inline
```
Create random values ... | github_jupyter |
# Gridworld
The Gridworld environment (inspired from Sutton and Barto, Reinforcement Learning: an Introduction) is represented in figure. The environment is a finite MDP in which states are represented by grid cells. The available actions are 4: left, right, up, down. Actions m... | github_jupyter |
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