code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/FeatureCollection/column_statistics_by_group.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a ... | github_jupyter |
# Making journal-quality figures using Matplotlib
```
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(font_scale=1.4)
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.patches import ConnectionPatch
import matplotlib.gridspec as gridspec
from matplotlib... | github_jupyter |
# <div align="center">BERT (Bidirectional Encoder Representations from Transformers) Explained: State of the art language model for NLP</div>
---------------------------------------------------------------------
<img src='asset/9_6/main.png'>
you can Find me on Github:
> ###### [ GitHub](https://github.com/lev1khacha... | github_jupyter |
# Customised Kernel of the ACBC (only trained with the score tensor generated from Integrated model.ipynb)
```
from sklearn.metrics import classification_report
class MyModel:
def __init__(self, w, lr):
self.w = w
self.size = w.shape[0]
self.lr = lr
def predict(self, x):
... | github_jupyter |
```
"""
Main application and routing logic
"""
# Standard imports
import os
# Database + Heroku + Postgres
from dotenv import load_dotenv
from flask import Flask, jsonify, request
from flask_sqlalchemy import SQLAlchemy
import psycopg2
from .models import DB, Strain
from flask_cors import CORS
# import model
#from n... | github_jupyter |
<a href="https://colab.research.google.com/github/mahfuz978/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling/blob/master/module1-join-and-reshape-data/Mahfuzur_Join_and_Reshape_Data_Assignment.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
_Lambd... | github_jupyter |
# Real World Example:
### AI, Machine Learning & Data Science
---
# What is the Value for your Business?
- By seeing acutal examples you'll be empowered to ask the right questions (and get fair help from consultants, startups, or data analytics companies)
- This will help you make the correct decisions for your b... | github_jupyter |
# CW10: Ordinary Differential Equations
Notes:
- solution to a differential equation is a function or set of functions
- Euler's Method serves as the basis for all others
- The names of each method gives insight to how the functions look/behave graphically
Solving a differential equation with initial condition:
$dy... | github_jupyter |
```
from sklearn.base import clone
from itertools import combinations
import numpy as np
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
class SBS():
def __init__(self, estimator, k_features, scoring=accuracy_score,
test_size=0.25, random_state=1):
... | github_jupyter |
## Importing Libraries
```
import numpy as np
import pandas as pd
import math
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
from matplotlib.offsetbox import (AnchoredOffsetbox, TextArea)
import statsmodels.formula.api as smf
import requests
import sklearn as skl
from sklearn import dat... | github_jupyter |
## Neural Networks in PyMC3 estimated with Variational Inference
(c) 2016 by Thomas Wiecki
## Current trends in Machine Learning
There are currently three big trends in machine learning: **Probabilistic Programming**, **Deep Learning** and "**Big Data**". Inside of PP, a lot of innovation is in making things scale us... | github_jupyter |
This notebook is broken into two sections, firstly an introduction to the NumPy package and secondly a breakdown of the NumPy random package.
# The NumPy Package
## What is it?
NumPy is a Python package used for scientific computing. Often cited as a fundamental package in this area, this is for good reason. It pro... | github_jupyter |
# 1.1 Getting started
## Prerequisites
### Installation
This tutorial requires **signac**, so make sure to install the package before starting.
The easiest way to do so is using conda:
```$ conda config --add channels conda-forge```
```$ conda install signac```
or pip:
```pip install signac --user```
Please re... | github_jupyter |
<a href="https://colab.research.google.com/github/DJCordhose/ml-workshop/blob/master/notebooks/tf-intro/2020-01-rnn-basics.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Sequences Basics
Example, some code and a lot of inspiration taken from: ht... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
!nvidia-smi
from argparse import Namespace
import sys
import os
home = os.environ['HOME']
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
print(os.environ['CUDA_VISIBLE_DEVICES'])
os.chdir(f'{home}/pycharm/automl')
# os.chdir(f'{home}/pycharm/automl/search_policies/rnn')
sys.path.appe... | github_jupyter |
SOP036 - Install kubectl command line interface
===============================================
Steps
-----
### Common functions
Define helper functions used in this notebook.
```
# Define `run` function for transient fault handling, suggestions on error, and scrolling updates on Windows
import sys
import os
impor... | github_jupyter |
<img align="left" src="https://lever-client-logos.s3.amazonaws.com/864372b1-534c-480e-acd5-9711f850815c-1524247202159.png" width=200>
<br></br>
<br></br>
## *Data Science Unit 4 Sprint 3 Assignment 1*
# Recurrent Neural Networks and Long Short Term Memory (LSTM)
![Monkey at a typewriter](https://upload.wikimedia.org... | github_jupyter |
```
"""
A randomly connected network learning a sequence
This example contains a reservoir network of 500 neurons.
400 neurons are excitatory and 100 neurons are inhibitory.
The weights are initialized randomly, based on a log-normal distribution.
The network activity is stimulated with three different inputs (A, B, ... | github_jupyter |
## shuffle
### 计算机如何生成素数
只简单介绍一种方法，**线性同余法**
$$N_{j+1} = (A*N_j + B) mod M $$
其中A,B,M是设定的常数。$N_0$被叫做随机种子，是计算机主板的计数器在内存中的记数值。
从公式中可以看出$N_{j+1}$ 与 $N_j$ 存在线性。
线性同余法生成的是均匀分布，如果需要生成其他分布则是据此进行采样。
### 那么如何随机打乱一个数组
1. 考虑一种方法：对于一个长度为n的数组，每次随机选取两个index，交换这两个index对应的数字。并将他们标记。循环直到数组中所有元素被处理
```
import random
def shuffl... | github_jupyter |
# Basics
```
print("Hello World!")
# This is comment!
bread=10
print(bread)
bread=input()
bread
43+5
'43'+5
```
**Find out the reason behind the above error.**
**Tip**: Copy the error and search in [Google](https://www.google.com/).
```
8
8+3
```
### Data Types
Integers: -2, -1, 0, 1, 2, 3, 4, 5
Floating-point ... | github_jupyter |
# Google form analysis visualizations
## Table of Contents
['Google form analysis' functions checks](#funcchecks)
['Google form analysis' functions tinkering](#functinkering)
```
%run "../Functions/1. Google form analysis.ipynb"
```
## 'Google form analysis' functions checks
<a id=funcchecks />
## 'Google form a... | github_jupyter |
```
from xml.etree import ElementTree
from xml.dom import minidom
from xml.etree.ElementTree import Element, SubElement, Comment, indent
def prettify(elem):
"""Return a pretty-printed XML string for the Element.
"""
rough_string = ElementTree.tostring(elem, encoding="ISO-8859-1")
reparsed = minidom.par... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W3D2_DynamicNetworks/W3D2_Tutorial1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Neuromatch Academy: Week 3, Day 2, Tutorial 1
# Neuronal N... | github_jupyter |
```
import xarray as xr
import pandas as pd
from joblib import load
import os
import math
from datetime import datetime,timedelta
from sklearn import preprocessing
def redondeo(coordenadas, base=1/12):
"""
Devuelve las coordenadas pasadas redondeadas
Parametros:
coordenadas -- lista de latitud y lo... | github_jupyter |
# `sourmash tax` submodule
### for integrating taxonomic information
The sourmash tax (alias `taxonomy`) commands integrate taxonomic information into the results of sourmash gather. tax commands require a properly formatted taxonomy csv file that corresponds to the database used for gather. For supported databases (... | github_jupyter |
```
def preprocess_string(s):
plaintext = ''
for i in s:
temp = ord(i)
if ((temp < 123 and temp > 96) or (temp < 91 and temp > 64)
or (temp < 58 and temp > 47) or temp == 32):
plaintext += i
return plaintext
def char_to_bits(s, char_size = 4):
if (char_size == 4):... | github_jupyter |
# Import Libraries
```
import sys
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.decomposition import PCA
from sklearn import random_projection
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import fbeta_score, roc_curve, auc
from sklearn import svm
impor... | github_jupyter |
# AMPL Model Colaboratory Template
[](https://github.com/ampl/amplcolab/blob/master/template/colab.ipynb) [](https://colab.research.google.com/github/ampl/amplcolab/blob... | github_jupyter |
# TV Script Generation
In this project, you'll generate your own [Simpsons](https://en.wikipedia.org/wiki/The_Simpsons) TV scripts using RNNs. You'll be using part of the [Simpsons dataset](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data) of scripts from 27 seasons. The Neural Network you'll build will gen... | github_jupyter |
# Random Signals and LTI-Systems
*This jupyter notebook is part of a [collection of notebooks](../index.ipynb) on various topics of Digital Signal Processing. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*
## Measurement of Acoustic Impulse Responses
Th... | github_jupyter |
```
import json
import os
import re
import tqdm
import random
from PIL import Image
%matplotlib inline
from pycocotools.coco import COCO
from pycocotools import mask as cocomask
import numpy as np
import skimage.io as io
import matplotlib.pyplot as plt
import pylab
import random
import os
pylab.rcParams['figure.figsiz... | github_jupyter |
# Lab 01 - Modelling and Systems Dynamics
```
import math, pandas
from matplotlib import pyplot
```
## Section 01 - Starting Jupyter Notebook
```
print(math.sin(4))
```
## Section 02 - Number and Arithmetic
```
print(5 / 8)
print(5 / 8.0) # or print(5 / float(8))
print(5 * 8)
print((1 / 2.0) ** 2)
```
### Review ... | github_jupyter |
# Visualize real data
Get to know the real data
```
import pandas as pd
import os
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
savefolder = 'figures_real_data'
folder = 'data'
df = pd.read_excel(os.path.join(folder, '20211128 - Full DART Data (Model & Test).xlsx'), header=2)
df.head()
impo... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
ppd_df = pd.read_csv("policedata/ppd_complaints.csv")
display(ppd_df.head())
# Add date of complaint
date_pattern = r"(\d{1,2}-\d{1,2}-\d{2,4})" #parentheses is to treat all characters as a "group", making extract work
complaint_str_series = ppd_df['summary'].str.... | github_jupyter |
```
# Dependencies and Setup
import pandas as pd
import numpy as np
# Load the file and set up the path for it
purchase_data = "Resources/purchase_data.csv"
# Read Purchasing File, store it in Panda frame, read the head of the file (first 5 items)
purchase_data_df = pd.read_csv(purchase_data)
purchase_data_df.head()
pu... | github_jupyter |
## TCLab Function Help
#### Connect/Disconnect
```lab = tclab.TCLab()``` Connect and create new lab object, ```lab.close()``` disconnects lab.
#### LED
```lab.LED()``` Percentage of output light for __Hot__ Light.
#### Heaters
```lab.Q1()``` and ```lab.Q2()``` Percenta... | github_jupyter |
# Face Generation
In this project, you'll use generative adversarial networks to generate new images of faces.
### Get the Data
You'll be using two datasets in this project:
- MNIST
- CelebA
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural netwo... | github_jupyter |
# Modeling and Simulation in Python
Chapter 21
Copyright 2017 Allen Downey
License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)
```
# Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an a... | github_jupyter |
**Important**: This notebook is different from the other as it directly calls **ImageJ Kappa plugin** using the [`scyjava` ImageJ brige](https://github.com/scijava/scyjava).
Since Kappa uses ImageJ1 features, you might not be able to run this notebook on an headless machine (need to be tested).
```
from pathlib impor... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content-dl/blob/main/tutorials/W1D1_BasicsAndPytorch/student/W1D1_Tutorial1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Tutorial 1: PyTorch
**Week 1, Day 1: Basics and ... | github_jupyter |
### 1 - e-posta baten histograma
Ahalik eta funtzio gehien sortuko ditugu, azpiproblema bakoitza funtzio beten bidez adierazteko. Adibidez:
* e-posta batetako testua, ilara zerrenda moduan (goiburua deskartatu)
* hitz batetako ertzetatik puntuazio zeinuak kendu
* hitz bat soilik karaktere alfabetikoez osotua ote dago... | github_jupyter |
# Classification metrics
Author: Geraldine Klarenberg
Based on the Google Machine Learning Crash Course
## Tresholds
In previous lessons, we have talked about using regression models to predict values. But sometimes we are interested in **classifying** things: "spam" vs "not spam", "bark" vs "not barking", etc.
Log... | github_jupyter |
```
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
from matplotlib import animation
from IPython.display import HTML
import numpy as np
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
%matplotlib inline
# Set up colors:
color_map = ListedColormap(['#1b9e77',... | github_jupyter |
```
# Name: example_analysis_data_creation.ipynb
# uthors: Stephan Meighen-Berger
# Example how to construct a data set to use for later analyses
# General imports
import numpy as np
import matplotlib.pyplot as plt
import sys
from tqdm import tqdm
import pickle
# Adding path to module
sys.path.append("../")
# picture p... | github_jupyter |
# Case: Impact of p_negative
Situation:
- 70% agent availability on weekdays and weekends
Task:
- Evaluate Shift Coverage over p_negative: [.001, .0025, .005, .0075, .01, .02, .03, .05, .1, .2]
- Evaluate Agent Satisfaction over p_negative: [.001, .0025, .005, .0075, .01, .02, .03, .05, .1, .2]
```
import abm_sched... | github_jupyter |
# First Attempt
batch size 256 lr 1e-3
### Import modules
```
%matplotlib inline
from __future__ import division
import sys
import os
os.environ['MKL_THREADING_LAYER']='GNU'
sys.path.append('../')
from Modules.Basics import *
from Modules.Class_Basics import *
```
## Options
```
classTrainFeatures = basic_features
... | github_jupyter |
# Programming Assignment
## Готовим LDA по рецептам
Как вы уже знаете, в тематическом моделировании делается предположение о том, что для определения тематики порядок слов в документе не важен; об этом гласит гипотеза <<мешка слов>>. Сегодня мы будем работать с несколько нестандартной для тематического моделирования к... | github_jupyter |
# Reading Notebooks
* https://nbformat.readthedocs.io/en/latest/api.html
## Read a .ipynb file
A notebook consists of metadata, format info, and a list of cells. Very simple.
```
import nbformat
from nbformat.v4.nbbase import new_code_cell, new_markdown_cell, new_notebook
# read notebook file
filename = "02.01-Tag... | github_jupyter |
# Paginación
El objetivo es crear una lista de páginas a mostrar. Se mostrará la página actual y las 3 siguientes y las 3 anteriores, y además la primera y la última. El algoritmo debe calcular qué numeros se muestran, y crear una lista con ellos. Además se pondrán espacios en blanco en el lugar donde irán los puntos ... | github_jupyter |
```
#Author: Eren Ali Aslangiray, Meryem Şahin
import pandas as pd
import os
import time
import sys
path1 = "/Users/erenmac/Desktop/NEW_DATA/Text/text_emotion.csv"
path2 = "/Users/erenmac/Desktop/NEW_DATA/Text/primary-plutchik-wheel-DFE.csv"
path3 = "/Users/erenmac/Desktop/NEW_DATA/Text/ssec-aggregated/train-combined-... | github_jupyter |
```
%matplotlib inline
import matplotlib
from matplotlib import pyplot as plt
import seaborn as sns
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('retina')
sns.set(rc={'figure.figsize':(11.7,8.27)})
sns.set_palette(sns.color_palette())
import pandas as pd
import pickle
from mcmcjoint.sampl... | github_jupyter |
```
import os, re
import pandas as pd
import numpy as np
import requests
from bs4 import BeautifulSoup
import time
import json
URL_LIST_BASE = "https://www.dogbreedslist.info/all-dog-breeds/list_1_{}.html" # {} in [1, 19]
def get_dog_list_page(n):
r = requests.get(URL_LIST_BASE.format(n))
soup = BeautifulSoup(... | github_jupyter |
## Dependencies
```
import json, glob
from tweet_utility_scripts import *
from tweet_utility_preprocess_roberta_scripts_aux import *
from transformers import TFRobertaModel, RobertaConfig
from tokenizers import ByteLevelBPETokenizer
from tensorflow.keras import layers
from tensorflow.keras.models import Model
```
# L... | github_jupyter |
```
%matplotlib inline
from refer import REFER
import numpy as np
import skimage.io as io
import matplotlib.pyplot as plt
```
# Load Refer Dataset
```
data_root = '../../data' # contains refclef, refcoco, refcoco+, refcocog and images
dataset = 'refcoco'
splitBy = 'unc'
refer = REFER(data_root, dataset, splitBy)
```... | github_jupyter |
# Binary trees
```
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from dtreeviz.trees import *
from lolviz import *
import numpy as np
import pandas as pd
%config InlineBackend.figure_format = 'retina'
```
## Setup
Make sure to install stuff:
```
pip install... | github_jupyter |
```
# reload packages
%load_ext autoreload
%autoreload 2
```
### Choose GPU (this may not be needed on your computer)
```
%env CUDA_DEVICE_ORDER=PCI_BUS_ID
%env CUDA_VISIBLE_DEVICES=''
```
### load packages
```
from tfumap.umap import tfUMAP
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt... | github_jupyter |
# Train and hyperparameter tune with RAPIDS
## Prerequisites
- Create an Azure ML Workspace and setup environmnet on local computer following the steps in [Azure README.md](https://gitlab-master.nvidia.com/drobison/aws-sagemaker-gtc-2020/tree/master/azure/README.md )
```
# verify installation and check Azure ML SDK ... | github_jupyter |
```
import sys
sys.executable
```
[Optional]: If you're using a Mac/Linux, you can check your environment with these commands:
```
!which pip3
!which python3
!ls -lah /usr/local/bin/python3
```
```
!pip3 install -U pip
!pip3 install torch==1.3.0
!pip3 install seaborn
import torch
torch.cuda.is_available()
# IPython ... | github_jupyter |
```
import pandas as pd
import numpy as np
from math import sqrt
from numpy import concatenate
from matplotlib import pyplot
from pandas import read_csv
from pandas import DataFrame
from pandas import concat
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.metri... | github_jupyter |
```
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import xarray as xr
import scipy
import os
from sklearn.decomposition import PCA
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import QuantileRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.me... | github_jupyter |
# DBSCAN without Libraries
```
import time
import warnings
import queue
import numpy as np
import pandas as pd
from sklearn import cluster, datasets, mixture
from sklearn.neighbors import kneighbors_graph
from sklearn import datasets
# from sklearn.datasets import make_blobs
from sklearn.preprocessing import Standar... | 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 |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Preamble" data-toc-modified-id="Preamble-1"><span class="toc-item-num">1 </span>Preamble</a></span><ul class="toc-item"><li><span><a href="#Some-general-parameters" data-toc-modified-id="Some-gen... | github_jupyter |
# BiDirectional LSTM classifier in keras
#### Load dependencies
```
import keras
from keras.datasets import imdb
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Embedding, SpatialDropout1D, Dense, Flatten, Dropout, LSTM
from keras.layers.wrappers imp... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from skimage.color import rgb2gray
from skimage.filters import gaussian
import scipy
import cv2
from scipy import ndimage
import Image_preperation as prep
import FitFunction as fit
import FileManager as fm
import Image_preperation as prep
def calc_mean(points)... | github_jupyter |
# Homework (15 pts) - Classification
```
# Questions are based on the mouse cortex protein expression level dataset used in lecture.
# The Data_Cortex_Nuclear.csv file is available in the same folder as this notebook
# or at https://www.kaggle.com/ruslankl/mice-protein-expression
import pandas as pd
import numpy as np... | github_jupyter |
# Assignment 2: Parts-of-Speech Tagging (POS)
Welcome to the second assignment of Course 2 in the Natural Language Processing specialization. This assignment will develop skills in part-of-speech (POS) tagging, the process of assigning a part-of-speech tag (Noun, Verb, Adjective...) to each word in an input text. Tag... | github_jupyter |
### Dependencies for this notebook:
* pip install spacy, pandas, matplotlib
* python -m spacy.en.download
```
from IPython.display import SVG, display
import spacy
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime
%matplotlib inline
#encode some text as uncode
text = u"I'm executing thi... | github_jupyter |
```
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
```
# Unpacking the paper - CATEGORICAL REPARAMETERIZATION WITH GUMBEL-SOFTMAX
* https://arxiv.org/pdf/1611.01144.pdf
## Introduction
* Not much to get here
## THE GUMBEL-SOFTMAX Distribution
... | github_jupyter |
# Sentiment Analysis
## Using XGBoost in SageMaker
_Deep Learning Nanodegree Program | Deployment_
---
As our first example of using Amazon's SageMaker service we will construct a random tree model to predict the sentiment of a movie review. You may have seen a version of this example in a pervious lesson although ... | github_jupyter |
```
__name__ = "k1lib.callbacks"
#export
from .callbacks import Callback, Callbacks, Cbs
import k1lib, os, torch
__all__ = ["Autosave", "DontTrainValid", "InspectLoss", "ModifyLoss", "Cpu", "Cuda",
"DType", "InspectBatch", "ModifyBatch", "InspectOutput", "ModifyOutput",
"Beep"]
#export
@k1lib.pat... | github_jupyter |
# DataFrame & Series
Di Pandas terdapat 2 kelas data baru yang digunakan sebagai struktur dari spreadsheet:
1. Series:
satu kolom bagian dari tabel dataframe yang merupakan 1 dimensional numpy array sebagai basis datanya, terdiri dari 1 tipe data (integer, string, float, dll).
2. DataFrame:
gabungan dari Series, be... | github_jupyter |
```
from aide_design.play import*
from aide_design import floc_model as floc
from aide_design import cdc_functions as cdc
from aide_design.unit_process_design.prefab import lfom_prefab_functional as lfom
from pytexit import py2tex
import math
```
# 1 L/s Plants in Parallel
# CHANCEUX
## Priya Aggarwal, Sung Min Kim, ... | github_jupyter |
```
import sqlite3
conn = sqlite3.connect('nominations.db')
#Checking Schema
schema = conn.execute("pragma table_info(nominations)")
schema.fetchall()
first_ten = conn.execute("select * from nominations limit 10").fetchall()
for i in first_ten:
print(i)
#Creating a ceremonies table that contains the list of tuples... | github_jupyter |
```
'''
# IoU_old
usage of loss func
criterion = nn.NLLLoss()
def step(x, y, is_train=True):
x = x.reshape(-1, 28 * 28)
y_pred = model(x)
loss = criterion(y_pred, y)
if is_train:
opt.zero_grad()
loss.backward()
opt.step()
return loss, y_pred
'''
torch.nn.Module
class IoU_old(n... | github_jupyter |
```
import h5py
import keras
import numpy as np
import json
import os
import uuid
import yaml
from attlayer import AttentionWeightedAverage
#from avglayer import MaskAverage
from copy import deepcopy
#from finetuning import (sampling_generator, finetuning_callbacks)
from operator import itemgetter
#from global_variabl... | github_jupyter |
**Import libraries**
```
import os
import logging
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Subset, DataLoader
from torch.backends import cudnn
import torch.utils.data
import torchvision
from torchvision import transforms
from torchvision.models import alexnet
from t... | github_jupyter |
# Reset Put Option
A reset put option is similar to a standard put option except that the exercise price is reset equal to the stock price on the pre-specified reset date if this stock price exceeds the original exercise price. Unlike the standard put option, a Reset put option has a stochastic strike price. On issue ... | github_jupyter |
```
from pysead import Truss_2D
import numpy as np
from random import random
import matplotlib.pyplot as plt
# initialize node dictionary
nodes = {}
# compute distances
distances_1 = np.arange(0,5*240,240)
distances_2 = np.arange(0,9*120,120)
# from node 1 to node 5
for i, distance in enumerate(distances_1):
node... | github_jupyter |
```
import os
import sys
import keras
import numpy as np
import tensorflow as tf
from keras import datasets
import matplotlib
import matplotlib.pyplot as plt
sys.path.append(os.getcwd() + "/../")
from bfcnn import BFCNN, collage, get_conv2d_weights
# setup environment
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
tf.compa... | github_jupyter |
### OkCupid DataSet: Classify using combination of text data and metadata
### Meeting 5, 03- 03- 2020
### Recap last meeting's decisions:
<ol>
<p>Meeting 4, 28- 01- 2020</p>
<li> Approach 1: </li>
<ul>
<li>Merge classs 1, 3 and 5</li>
<li>Under sample class 6 </li>
<li> Merge classes 6, 7, 8</li>... | github_jupyter |
# Task 4: Classification
_All credit for the code examples of this notebook goes to the book "Hands-On Machine Learning with Scikit-Learn & TensorFlow" by A. Geron. Modifications were made and text was added by K. Zoch in preparation for the hands-on sessions._
# Setup
First, import a few common modules, ensure Matp... | github_jupyter |
# "Build Your First Neural Network with PyTorch"
* article <https://curiousily.com/posts/build-your-first-neural-network-with-pytorch/>
* dataset <https://www.kaggle.com/jsphyg/weather-dataset-rattle-package>
requires `torch 1.4.0`
```
import os
from os.path import dirname
import numpy as np
import pandas as p... | github_jupyter |
# Scraping transfermarkt by html
```
from selenium.webdriver import (Chrome, Firefox)
import time
import requests
from bs4 import BeautifulSoup
from html_scraper import db
players = db['players']
player_urls = db['player_urls']
browser = Firefox()
url = 'https://www.transfermarkt.co.uk/primera-division/startseite/wet... | github_jupyter |
```
import itertools
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import NullFormatter
import pandas as pd
import numpy as np
import matplotlib.ticker as ticker
from sklearn import preprocessing
%matplotlib inline
# Load Data From CSV File
df = pd.read_csv('AnimeList.csv')
cols_id = list(df... | github_jupyter |
```
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout,Activation,BatchNormalization
from tensorflow.keras.callbacks import ModelCheckpoint
import matplotlib.pyplot as plt
%mat... | github_jupyter |
```
import collections
import json
import pprint
from datetime import datetime
import pandas as pd
# Notebook to generate attack tree Graphviz file and Emacs Org mode
# table for attack tree analysis configuration. The input is itemized
# list of attack tree nodes with mark modifiers.
# Should use Python3.4+
# Name ... | github_jupyter |
```
"Uni Face mask model"
#some important packages
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import model_from_json
from tensorflow.keras.models import load_model
from imutils.video import VideoStrea... | 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 |
# Convolutional Neural Networks: Application
Welcome to Course 4's second assignment! In this notebook, you will:
- Implement helper functions that you will use when implementing a TensorFlow model
- Implement a fully functioning ConvNet using TensorFlow
**After this assignment you will be able to:**
- Build and t... | github_jupyter |
```
%matplotlib inline
import pandas as pd
import xgboost as xgb
import numpy as np
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import graphviz
from sklearn.preprocessing import LabelEncoder
data = pd.read_csv("data/telco-churn.csv")
data.head()
data.shape
data.drop('customerID', axis = 1... | github_jupyter |
# Slow Stochastic
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
# yfinance is used to fetch data
import yfinance as yf
yf.pdr_override()
# input
symbol = 'AAPL'
start = '2018-08-01'
end = '2019-01-01'
# Read data
df = yf.download(symbo... | github_jupyter |
# ロジスティック写像
$$
f(x, a) = a x (1 - x)
$$
```
import numpy as np
import pathfollowing as pf
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set('poster', 'whitegrid', 'dark', rc={"lines.linewidth": 2, 'grid.linestyle': '-'})
def func(x, a):
return np.array([a[0] * x[0] * (1.0 - x[0])])
... | github_jupyter |
## Part 2: Introduction to Feed Forward Networks
### 1. What is a neural network?
#### 1.1 Neurons
A neuron is software that is roughly modeled after the neuons in your brain. In software, we model it with an _affine function_ and an _activation function_.
One type of neuron is the perceptron, which outputs a bina... | github_jupyter |
```
import gc
import locale
import pickle
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.colors import ListedColormap
from all_stand_var import conv_dict, lab_cols, used_cols
from all_own_funct import cnfl, value_filtering,y_modelling,x_modelling,... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Scale-heights-for-typical-atmospheric-soundings" data-toc-modified-id="Scale-heights-for-typical-atmospheric-soundings-1"><span class="toc-item-num">1 </span>Scale heights for typical atmospheric... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
sns.set_style('whitegrid')
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics impo... | github_jupyter |
```
import tensorflow as tf
print(tf.__version__)
import tensorflow_datasets as tfds
print(tfds.__version__)
```
# Get dataset
```
SPLIT_WEIGHTS = (8, 1, 1)
splits = tfds.Split.TRAIN.subsplit(weighted=SPLIT_WEIGHTS)
(raw_train, raw_validation, raw_test), metadata = tfds.load('cats_vs_dogs',
... | github_jupyter |
```
#default_exp tabular.core
#export
from fastai2.torch_basics import *
from fastai2.data.all import *
from nbdev.showdoc import *
#export
pd.set_option('mode.chained_assignment','raise')
```
# Tabular core
> Basic function to preprocess tabular data before assembling it in a `DataLoaders`.
## Initial preprocessing... | github_jupyter |
# Optimización
Author: Jesús Cid-Sueiro
Jerónimo Arenas-García
Versión: 0.1 (2019/09/13)
0.2 (2019/10/02): Solutions added
## Exercise: compute the minimum of a real-valued function
The goal of this exercise is to implement and test optimization algorithms for the minimization o... | github_jupyter |
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