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# Lightweight python components
Lightweight python components do not require you to build a new container image for every code change. They're intended to use for fast iteration in notebook environment.
**Building a lightweight python component**
To build a component just define a stand-alone python function and then... | github_jupyter |
# Predicting Student Admissions with Neural Networks
In this notebook, we predict student admissions to graduate school at UCLA based on three pieces of data:
- GRE Scores (Test)
- GPA Scores (Grades)
- Class rank (1-4)
The dataset originally came from here: http://www.ats.ucla.edu/
## Loading the data
To load the da... | github_jupyter |
```
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.layers import AveragePooling2D
from tensorflow.keras.layers import Dropout
from tensorflow.keras.la... | github_jupyter |
```
import matplotlib.pyplot as plt
from matplotlib import cm, colors, rcParams
import numpy as np
import bayesmark.constants as cc
from bayesmark.path_util import abspath
from bayesmark.serialize import XRSerializer
from bayesmark.constants import ITER, METHOD, TEST_CASE, OBJECTIVE, VISIBLE_TO_OPT
# User settings, m... | github_jupyter |
```
import sys
# sys.path.append('GVGAI_GYM')
import gym
import gym_gvgai
import numpy as np
import random
from IPython.display import clear_output
from collections import deque
import progressbar
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import Model, Sequential
from tensorflow.k... | github_jupyter |
```
from sklearn.neighbors import NearestNeighbors
import numpy as np
from matplotlib import pyplot as plt
#import corner
import urllib
import os
import sys
#import GCRCatalogsF
from astropy.io import fits
#from demo_funcs_local import *
from sklearn.model_selection import train_test_split
import pandas as pd
from astr... | github_jupyter |
```
import pandas as pd
root = "C:\\Users\\user\\SadafPythonCode\\MLHackathon\\ML-for-Good-Hackathon\\Data\\"
df = pd.read_csv(root + 'CrisisLogger\\crisislogger.csv')
a = df.iloc[28,1]
a
import spacy
nlp = spacy.load('en_core_web_sm',disable=['ner','textcat'])
# function for rule 2
def rule2(text):
doc = nlp... | github_jupyter |
# Elementare Datentypen
*Erinnerung:* Beim Deklarieren einer Variable muss man deren Datentyp angeben oder er muss eindeutig erkennbar sein.
Die beiden folgenden Anweisungen erzeugen beide eine Variable vom Typ `int`:
var a int
b := 42
Bisher haben wir nur einen Datentyp benutzt: `int`. Dieser Typ steht für ... | github_jupyter |
# CPO Datascience
This program is intended for use by the Portland State University Campus Planning Office (CPO).
```
#Import required packages
import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
import datetime
import seaborn as sns
import matplotlib.pyplot as pl... | github_jupyter |
# Trabalhando com o Jupyter
Ferramenta que permite criação de código, visualização de resultados e documentação no mesmo documento (.ipynb)
**Modo de comando:** `esc` para ativar, o cursor fica inativo
**Modo de edição:** `enter` para ativar, modo de inserção
### Atalhos do teclado (MUITO úteis)
Para usar os atalhos... | github_jupyter |
# Uniview module for LIGO event GW170817
*Aaron Geller, 2018*
### Imports and function definitions
```
#This directory contains all the data needed for the module. It should be in the same directory as the notebook
dataFolder = "data"
import sys, os, shutil, errno, string, urllib
sys.path.append(( os.path.abspath... | github_jupyter |
# Experiment Analysis
In this notebook we will evaluate the results form the experiments executed. For each experiment, one parameter is changed and all others were kept constant as to determine the effect of one variable.
**The goals of this analysis are:**
1. Determine the relationship of the number of parameters i... | github_jupyter |
```
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from s... | github_jupyter |
# Hyper-parameter tuning
**Learning Objectives**
1. Learn how to use `cloudml-hypertune` to report the results for Cloud hyperparameter tuning trial runs
2. Learn how to configure the `.yaml` file for submitting a Cloud hyperparameter tuning job
3. Submit a hyperparameter tuning job to Cloud AI Platform
## Introducti... | github_jupyter |
```
import pandas as pd
import os
import geopy as geo
import numpy as np
from folium.plugins import FastMarkerCluster
import folium
from geopy.geocoders import Nominatim
import matplotlib.pyplot as plt
%matplotlib inline
```
## Importando a base e gerando um sample
```
os.getcwd()
df = pd.read_csv('/home/rsa/Documen... | github_jupyter |
# Layers and Blocks
:label:`sec_model_construction`
When we first introduced neural networks,
we focused on linear models with a single output.
Here, the entire model consists of just a single neuron.
Note that a single neuron
(i) takes some set of inputs;
(ii) generates a corresponding scalar output;
and (iii) has a ... | github_jupyter |
**Note.** *The following notebook contains code in addition to text and figures. By default, the code has been hidden. You can click the icon that looks like an eye in the toolbar above to show the code. To run the code, click the cell menu, then "run all".*
```
# Import packages, set preferences, etc.
%matplotlib inl... | github_jupyter |
# Project 2: Digit Recognition
## Statistical Machine Learning (COMP90051), Semester 2, 2017
*Copyright the University of Melbourne, 2017*
### Submitted by: Yitong Chen
### Student number: 879326
### Kaggle-in-class username: *YitongChen*
In this project, you will be applying machine learning for recognising digit... | github_jupyter |
# Machine Learning Engineer Nanodegree
## Supervised Learning
## Project 2: Building a Student Intervention System
Welcome to the second project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional funct... | github_jupyter |
<a href="https://colab.research.google.com/github/JoanesMiranda/Machine-learning/blob/master/Autoenconder.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
### Importando as bibliotecas necessárias
```
import numpy as np
import matplotlib.pyplot as p... | github_jupyter |
## Peer-graded Assignment: Segmenting and Clustering Neighborhoods in Toronto
# Part 3
```
import pandas as pd
import numpy as np
import json
!conda install -c conda-forge geopy --yes
from geopy.geocoders import Nominatim
import requests
from pandas.io.json import json_normalize
import matplotlib.cm as cm
import m... | github_jupyter |
<a href="https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_05_3_keras_l1_l2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# T81-558: Applications of Deep Neural Networks
**Module 5: Regularization ... | github_jupyter |
# Saving a web page to scrape later
For many scraping jobs, it makes sense to first save a copy of the web page (or pages) that you want to scrape and then operate on the local files you've saved. This is a good practice for a couple of reasons: You won't be bombarding your target server with requests every time you f... | github_jupyter |
```
import pandas as pd
nhl_games= pd.read_csv("/Users/joejohns/data_bootcamp/GitHub/final_project_nhl_prediction/Data/Kaggle_Data_Ellis/game.csv")
nhl_games.columns
nhl_20162017 = nhl_games.loc[(nhl_games['season'] == 20162017)&(nhl_games['type'] == 'R') , ['game_id', 'season', 'type', 'date_time_GMT', 'away_team_id'... | github_jupyter |
# Handle shapefiles using Geopandas
```
###############################################################################################
###############################################################################################
# Part 1: work with shapefiles
# I am using a "shapefile" which consists of at least fo... | github_jupyter |
# Task 1: Getting started with Numpy
Let's spend a few minutes just learning some of the fundamentals of Numpy. (pronounced as num-pie **not num-pee**)
### what is numpy
Numpy is a Python library that support large, multi-dimensional arrays and matrices.
Let's look at an example. Suppose we start with a little tab... | github_jupyter |
# Intake / Pangeo Catalog: Making It Easier To Consume Earth’s Climate and Weather Data
Anderson Banihirwe (abanihi@ucar.edu), Charles Blackmon-Luca (blackmon@ldeo.columbia.edu), Ryan Abernathey (rpa@ldeo.columbia.edu), Joseph Hamman (jhamman@ucar.edu)
- NCAR, Boulder, CO, USA
- Columbia University, Palisades, NY, US... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
import pyart
import scipy
radar = pyart.io.read('/home/zsherman/cmac_test_radar.nc')
radar.fields.keys()
max_lat = 37
min_lat = 36
min_lon = -98.3
max_lon = -97
lal = np.arange(min_lat, max_lat, .2)
lol = np.arange(min_lon, max_lon, .2)
display = pyart.graph.Radar... | github_jupyter |
# 第2回 ベクトル空間モデル
この演習ページでは，ベクトル空間モデルに基づく情報検索モデルについて説明します．具体的には，文書から特徴ベクトルへの変換方法，TF-IDFの計算方法，コサイン類似度による文書ランキングについて，その実装例を説明します．第2回演習の最終目的は，ある与えられた文書コーパスに対して，TF-IDFで重み付けされた特徴ベクトルによる文書ランキングが実装できるようになることです．
## ライブラリ
この回の演習では，以下のライブラリを使用します．
- [numpy, scipy](http://www.numpy.org/)
+ Pythonで科学技術計算を行うための基礎的なライブラリ．
- [gens... | github_jupyter |
```
!pip install roboschool==1.0.48 gym==0.15.4
import tensorflow as tf
import numpy as np
import gym
import roboschool
class TD3PG:
def __init__(self,env,memory):
self.env=env
self.state_dimension=env.observation_space.shape
self.action_dimension=env.action_space.shape[0]
self.min_action=env.action_... | github_jupyter |
# Exploring Text Data (2)
## PyConUK talk abstract
Data set of abstracts for the PyConUK 2016 talks (retrieved 14th Sept 2016 from https://github.com/PyconUK/2016.pyconuk.org)
The data can be found in `../data/pyconuk2016/{keynotes,workshops,talks}/*`
There are 101 abstracts
## Load the data
Firstly, we load all ... | github_jupyter |
# Explicit Feedback Neural Recommender Systems
Goals:
- Understand recommender data
- Build different models architectures using Keras
- Retrieve Embeddings and visualize them
- Add metadata information as input to the model
```
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import os.path as o... | github_jupyter |
```
import sys
sys.path.insert(0, '/cndd/fangming/CEMBA/snmcseq_dev')
from __init__ import *
from snmcseq_utils import cd
from snmcseq_utils import create_logger
# from CEMBA_update_mysql import connect_sql
log = create_logger()
log.info("Hello")
#
input_f = '/cndd2/fangming/projects/scf_enhancers/data/bulk/round2/mc... | github_jupyter |
# Loading Libraries
```
# Importing the core libraies
import numpy as np
import pandas as pd
from IPython.display import Markdown
from datetime import timedelta
from datetime import datetime
import plotly.express as px
import plotly.graph_objs as go
!pip install pycountry
import pycountry
from plotly.offline import... | github_jupyter |
Author: Maxime Marin
@: mff.marin@gmail.com
# Accessing IMOS data case studies: Walk-through and interactive session - Analysis
In this notebook, we will provide a receipe for further analysis to be done on the same dataset we selected earlier. In the future, a similar notebook can be tailored to a particular datas... | github_jupyter |
```
"""The purpose of this tutorial is to introduce you to:
(1) how gradient-based optimization of neural networks
operates in concrete practice, and
(2) how different forms of learning rules lead to more or less
efficient learning as a function of the shape of the optimization
landscape
... | github_jupyter |
## Problem Statement
An experimental drug was tested on 2100 individual in a clinical trial. The ages of participants ranged from thirteen to hundred. Half of the participants were under the age of 65 years old, the other half were 65 years or older.
Ninety five percent patients that were 65 years or older exper... | github_jupyter |
# Riemannian Optimisation with Pymanopt for Inference in MoG models
The Mixture of Gaussians (MoG) model assumes that datapoints $\mathbf{x}_i\in\mathbb{R}^d$ follow a distribution described by the following probability density function:
$p(\mathbf{x}) = \sum_{m=1}^M \pi_m p_\mathcal{N}(\mathbf{x};\mathbf{\mu}_m,\mat... | github_jupyter |
# 样式迁移
如果你是一位摄影爱好者，你也许接触过滤镜。它能改变照片的颜色样式，从而使风景照更加锐利或者令人像更加美白。但一个滤镜通常只能改变照片的某个方面。如果要照片达到理想中的样式，你可能需要尝试大量不同的组合。这个过程的复杂程度不亚于模型调参。
在本节中，我们将介绍如何使用卷积神经网络，自动将一个图像中的样式应用在另一图像之上，即*样式迁移*（style transfer） :cite:`Gatys.Ecker.Bethge.2016`。
这里我们需要两张输入图像：一张是*内容图像*，另一张是*样式图像*。
我们将使用神经网络修改内容图像，使其在样式上接近样式图像。
例如， :numref:`fig_style_tran... | github_jupyter |
```
!pip install -U -q pyDrive
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth... | github_jupyter |
```
import os
import csv
import platform
import pandas as pd
import networkx as nx
from graph_partitioning import GraphPartitioning, utils
run_metrics = True
cols = ["WASTE", "CUT RATIO", "EDGES CUT", "TOTAL COMM VOLUME", "Qds", "CONDUCTANCE", "MAXPERM", "NMI", "FSCORE", "FSCORE RELABEL IMPROVEMENT", "LONELINESS"]
p... | github_jupyter |
```
%%capture
!pip install wikidataintegrator
from rdflib import Graph, URIRef
from wikidataintegrator import wdi_core, wdi_login
from datetime import datetime
import copy
import pandas as pd
import getpass
print("username:")
username = input()
print("password:")
password = getpass.getpass()
login = wdi_login.WDLogin(u... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
import pandas as pd
import seaborn as sns
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn import neighbors, datasets
from sklearn.model_selection import cross_val_score
fr... | github_jupyter |
```
import sys
sys.executable
import argparse
import math
import h5py
import numpy as np
import tensorflow as tf
import socket
import glob
import os
import sys
import h5py
import provider
import tf_util
from model import *
from plyfile import PlyData, PlyElement
print("success")
BATCH_SIZE = 1
BATCH_SIZE_EVAL = 1
NUM... | github_jupyter |
# Thực hiện học trên model
```
# import
import random
import math
import time
import pandas as pd
import numpy as np
import torch
import torch.utils.data as data
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
# Thiết định các giá trị ban đầu
torch.manual_seed(1234)
np.random.seed(123... | 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 |
<hr style="margin-bottom: 40px;">
<img src="https://user-images.githubusercontent.com/7065401/58563302-42466a80-8201-11e9-9948-b3e9f88a5662.jpg"
style="width:400px; float: right; margin: 0 40px 40px 40px;"... | github_jupyter |
```
# Dataset from here
# https://archive.ics.uci.edu/ml/datasets/Adult
import great_expectations as ge
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
%matplotlib inline
"""
age: continuous.
workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never... | github_jupyter |
# Nothing But NumPy: A 3-layer Binary Classification Neural Network on Iris Flowers
Part of the blog ["Nothing but NumPy: Understanding & Creating Binary Classification Neural Networks with Computational Graphs from Scratch"](https://medium.com/@rafayak/nothing-but-numpy-understanding-creating-binary-classification-ne... | github_jupyter |
```
import san
from src_end2end import statistical_features
import lsa_features
import pickle
import numpy as np
from tqdm import tqdm
import pandas as pd
import os
import skopt
from skopt import gp_minimize
from sklearn import preprocessing
from skopt.space import Real, Integer, Categorical
from skopt.utils import use... | github_jupyter |
```
# default_exp qlearning.dqn_target
#export
import torch.nn.utils as nn_utils
from fastai.torch_basics import *
from fastai.data.all import *
from fastai.basics import *
from dataclasses import field,asdict
from typing import List,Any,Dict,Callable
from collections import deque
import gym
import torch.multiprocessin... | github_jupyter |
# 1. Import Libraries
```
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from jcopml.pipeline import num_pipe, cat_pipe
from jcopml.utils import save_model, load_model
from jcopml.plot imp... | github_jupyter |
# Programming_Assingment17
```
Question1.
Create a function that takes three arguments a, b, c and returns the sum of the
numbers that are evenly divided by c from the range a, b inclusive.
Examples
evenly_divisible(1, 10, 20) ➞ 0
# No number between 1 and 10 can be evenly divided by 20.
evenly_divisible(1, 10, 2) ➞ ... | github_jupyter |
## Importing necessary packages
```
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# preprocessing/decomposition
from sklearn.preprocessing import LabelEncoder, StandardScaler, OneHotEncoder
from sklearn.decomposition import PCA, FastICA, FactorAnalysis, Ker... | github_jupyter |
# Simulating a Predator and Prey Relationship
Without a predator, rabbits will reproduce until they reach the carrying capacity of the land. When coyotes show up, they will eat the rabbits and reproduce until they can't find enough rabbits. We will explore the fluctuations in the two populations over time.
# Using Lo... | github_jupyter |
```
import csv
import pickle
import pandas as pd
import numpy as np
import requests
import json
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
import sklearn.preprocessing
from sklearn import preprocessing
data=pd.read_csv("temp1-costamesa.csv")
data
data.columns
data=data.drop_duplica... | github_jupyter |
# Late contributions Received and Made
## Setup
```
%load_ext sql
from django.conf import settings
connection_string = 'postgresql+psycopg2://{USER}:{PASSWORD}@{HOST}:{PORT}/{NAME}'.format(
**settings.DATABASES['default']
)
%sql $connection_string
```
## Unique Composite Key
The documentation says that the reco... | github_jupyter |
# Basics
Let's first take a look at what's inside the ``ib_insync`` package:
```
import ib_insync
print(ib_insync.__all__)
```
### Importing
The following two lines are used at the top of all notebooks. The first line imports everything and the second
starts an event loop to keep the notebook live updated:
```
from... | github_jupyter |
# Practical 2 - Loops and conditional statements
In today's practical we are going to continue practicing working with loops whilst also moving on to the use of conditional statements.
<div class="alert alert-block alert-success">
<b>Objectives:</b> The objectives of todays practical are:
- 1) [Loops: FOR loops con... | github_jupyter |
## Imports
```
from __future__ import print_function, division
import pandas as pd
import numpy as np
import statsmodels.api as sm
import statsmodels.formula.api as smf
import patsy
import seaborn as sns
import matplotlib.pyplot as plt
import scipy.stats as stats
%matplotlib inline
from sklearn.linear_model import L... | github_jupyter |
```
%tensorflow_version 1.x
#Suppress warnings which keep poping up
import warnings
warnings.filterwarnings("ignore")
from keras import backend as K
import time
import matplotlib.pyplot as plt
import numpy as np
% matplotlib inline
np.random.seed(2017)
from keras.models import Sequential
from keras.layers.convoluti... | github_jupyter |
```
from sympy import pi, cos, sin, symbols
from sympy.utilities.lambdify import implemented_function
import pytest
from sympde.calculus import grad, dot
from sympde.calculus import laplace
from sympde.topology import ScalarFunctionSpace
from sympde.topology import element_of
from sympde.topology import NormalVector
f... | github_jupyter |
```
import re
```
The re module uses a backtracking regular expression engine
Regular expressions match text patterns
Use case examples:
- Check if an email or phone number was written correctly.
- Split text by some mark (comma, dot, newline) which may be useful to parse data.
- Get content from HTML tags.
- Impr... | github_jupyter |
<table> <tr>
<td style="background-color:#ffffff;">
<a href="http://qworld.lu.lv" target="_blank"><img src="..\images\qworld.jpg" width="25%" align="left"> </a></td>
<td style="background-color:#ffffff;vertical-align:bottom;text-align:right;">
prepared by <a href="http://abu.lu.... | github_jupyter |
# Document classifier
Praktisch wenn eine Menge Dokumente sortiert werden muss
## Daten
- Wir brauchen zuerst daten um unser Modell zu trainieren
```
#!pip3 install -U textblob
from textblob.classifiers import NaiveBayesClassifier
train = [
('I love this sandwich.', 'pos'),
('this is an amazing place!', 'po... | github_jupyter |
# A simple DNN model built in Keras.
Let's start off with the Python imports that we need.
```
import os, json, math
import numpy as np
import shutil
import tensorflow as tf
print(tf.__version__)
```
## Locating the CSV files
We will start with the CSV files that we wrote out in the [first notebook](../01_explore/t... | github_jupyter |
```
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="whitegrid")
sns.set_context("paper")
my_data = pd.read_csv("success_rate_RFC_bm5_normal_docking_2.csv")
my_data
my_data.set_index("Scoring",inplace=True)
# sns.barplot(data=my_data[my_data["Method"]=="Pydock"])
# sns.ba... | github_jupyter |
```
import numpy as np
import torch
import pandas as pd
import json
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder as LE
import bisect
import torch
from datetime import datetime
from sklearn.model_selection import train_test_split
!cp -r drive/My\ Drive/T11 ./T11
np... | github_jupyter |
# CaptureFile - Transactional record logging library
## Overview
Capture files are compressed transactional record logs and by convention use the
extension ".capture". Records can be appended but not modified and are
explicitly committed to the file.
Any records that are added but not committed will not be visible t... | github_jupyter |
### Code to implement Graphs
```
class DiGraphAsAdjacencyMatrix:
def __init__(self):
#would be better a set, but I need an index
self.__nodes = list()
self.__matrix = list()
def __len__(self):
"""gets the number of nodes"""
return len(self.__nodes)
... | github_jupyter |
# NMF Analysis
Performs a simple tf-idf of the question pairs and NMF dimension reduction to calculate cosine similarity of each question pair. The goal of the analysis is to see if the pairs labeled as duplicates have a distinctly different cosine similarity compared to those pairs marked as not duplicates.
```
# da... | github_jupyter |
```
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import statsmodels.api as sm
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from marshmallow import Schema, fields, post_load
# pip install marshmallow
# SQLite
import s... | 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 |
# Multivariate Dependencies Beyond Shannon Information
This is a companion Jupyter notebook to the work *Multivariate Dependencies Beyond Shannon Information* by Ryan G. James and James P. Crutchfield. This worksheet was written by Ryan G. James. It primarily makes use of the ``dit`` package for information theory cal... | github_jupyter |
```
%run technical_trading.py
#%%
data = pd.read_csv('../../data/hs300.csv',index_col = 'date',parse_dates = 'date')
data.vol = data.vol.astype(float)
#start = pd.Timestamp('2005-09-01')
#end = pd.Timestamp('2012-03-15')
#data = data[start:end]
#%%
chaikin = CHAIKINAD(data, m = 14, n = 16)
kdj = KDJ(data)
adx = ADX(dat... | github_jupyter |
<img src=https://a-static.projektn.sk/2020/11/Startup.jpg>
# Startup Profit Prediction
# 1. Reading and Understanding the Data
```
#basic libraries and visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
#statmodels
import statsmodels.api as sm
from statsmodels.... | github_jupyter |
```
import os
import requests
from bs4 import BeautifulSoup
import pandas as pd
from splinter import Browser
import splinter
import numpy as np
# create variables to store scraped info
movie_titles = []
opening_amts = []
total_gross = []
per_of_total = []
num_of_theaters = []
open_date = []
# loop through pages to get ... | github_jupyter |
# DAT257x: Reinforcement Learning Explained
## Lab 2: Bandits
### Exercise 2.3: UCB
```
import numpy as np
import sys
if "../" not in sys.path:
sys.path.append("../")
from lib.envs.bandit import BanditEnv
from lib.simulation import Experiment
#Policy interface
class Policy:
#num_actions: (int) Number of a... | github_jupyter |
<p><img src="https://oceanprotocol.com/static/media/banner-ocean-03@2x.b7272597.png" alt="drawing" width="800" align="center"/>
<h1><center>Ocean Protocol - Manta Ray project</center></h1>
<h3><center>Decentralized Data Science and Engineering, powered by Ocean Protocol</center></h3>
<p>Version 0.5.3 - beta</p>
<p>P... | github_jupyter |
# Exercise 1
Add the specified code for each code cell, running the cells _in order_.
Write a **`while`** loop that prints out every 5th number (multiples of 5) from 0 to 100 (inclusive).
- _Tip:_ use an **`end=','`** keyword argument to the `print()` function to print all the numbers on the same line.
```
nums = 0
w... | github_jupyter |
```
import numpy as np
import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
from nn_interpretability.interpretation.lrp.lrp_0 import LRP0
from nn_interpretability.interpretation.lrp.lrp_eps import LRPEpsilon
from nn_interpretability.interpretation.lrp.lrp_gamma import LRPGamma
from nn_inte... | github_jupyter |
<img src="images/bannerugentdwengo.png" alt="Banner" width="400"/>
<div>
<font color=#690027 markdown="1">
<h1>BESLISSINGSBOOM</h1>
</font>
</div>
<div class="alert alert-box alert-success">
In deze notebook laat je Python een beslissingsboom genereren op basis van een tabel met gelabelde voorbeelden... | github_jupyter |
# Project 1: Linear Regression Model
This is the first project of our data science fundamentals. This project is designed to solidify your understanding of the concepts we have learned in Regression and to test your knowledge on regression modelling. There are four main objectives of this project.
1\. Build Linear Re... | github_jupyter |
[SCEC BP3-QD](https://strike.scec.org/cvws/seas/download/SEAS_BP3.pdf) document is here.
# [DRAFT] Quasidynamic thrust fault earthquake cycles (plane strain)
## Summary
* Most of the code here follows almost exactly from [the previous section on strike-slip/antiplane earthquake cycles](c1qbx/part6_qd).
* Since the f... | github_jupyter |
# 머신 러닝 교과서 3판
# 9장 - 웹 애플리케이션에 머신 러닝 모델 내장하기
**아래 링크를 통해 이 노트북을 주피터 노트북 뷰어(nbviewer.jupyter.org)로 보거나 구글 코랩(colab.research.google.com)에서 실행할 수 있습니다.**
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://nbviewer.org/github/rickiepark/python-machine-learning-book-3rd-edition... | github_jupyter |
```
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('Data/20200403-WHO.csv')
df
df = df[df['Country/Territory'] != 'conveyance (Diamond']
death_rate = df['Total Deaths']/df['Total Confirmed']*100
df['Death Rate'] = death_rate
df
countries_infected = len(df)... | github_jupyter |
#Gaussian bayes classifier
In this assignment we will use a Gaussian bayes classfier to classify our data points.
# Import packages
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import multivariate_normal
from sklearn.metrics import classification_report
from matplotlib ... | github_jupyter |
# BLU02 - Learning Notebook - Data wrangling workflows - Part 2 of 3
```
import matplotlib.pyplot as plt
import pandas as pd
import os
```
# 2 Combining dataframes in Pandas
## 2.1 How many programs are there per season?
How many different programs does the NYP typically present per season?
Programs are under `/d... | github_jupyter |
```
%matplotlib inline
```
# Faces dataset decompositions
This example applies to `olivetti_faces` different unsupervised
matrix decomposition (dimension reduction) methods from the module
:py:mod:`sklearn.decomposition` (see the documentation chapter
`decompositions`) .
```
print(__doc__)
# Authors: Vlad Niculae... | github_jupyter |
# In this step, we'll process graduation data from the federal files
## In most cases, this is a straight "pull" from the data, but there are a few possible modifications:
- If the sample is too small from the most recent year, use 3 years of data
- For HBCUs, boost by 15%
- For a handful of schools, adjust down to re... | github_jupyter |
# Straighten an image using the Hough transform
We'll write our own Hough transform to compute the Hough transform and use it to straighten a wonky image.
## Package inclusion for Python
```
import copy
import math
import numpy as np
import cv2
```
## Read the image from a file on the disk and return a new matrix
... | github_jupyter |
```
# 초기 설정
from IPython.core.display import display, HTML
display(HTML("<style>.container { width: 100% !important; }</style>"))
pd.set_option("display.max_columns", 40)
import missingno as msno
%matplotlib inline
import pprint
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as ... | github_jupyter |
# Demonstration notebook to search, list product and download a band
This notebook is defined in four different sections:
1. Import requirements and definition of the parameters
2. Search for cached products
3. Listing the bands of one product
4. Downloading a band
## Import requirements and definition of the paramet... | github_jupyter |
Wayne H Nixalo - 09 Aug 2017
This JNB is an attempt to do the neural artistic style transfer and super-resolution examples done in class, on a GPU using PyTorch for speed.
Lesson NB: [neural-style-pytorch](https://github.com/fastai/courses/blob/master/deeplearning2/neural-style-pytorch.ipynb)
## Neural Style Transfe... | github_jupyter |
# In-Class Coding Lab: Conditionals
The goals of this lab are to help you to understand:
- Relational and Logical Operators
- Boolean Expressions
- The if statement
- Try / Except statement
- How to create a program from a complex idea.
# Understanding Conditionals
Conditional statements permit the non-linear exec... | github_jupyter |
<a href="https://colab.research.google.com/github/gabilodeau/INF6804/blob/master/FeatureVectorsComp.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
INF6804 Vision par ordinateur
Polytechnique Montréal
Distances entre histogrammes (L1, L2, MDPA, Bh... | github_jupyter |
# The IPython widgets, now in IHaskell !!
It is highly recommended that users new to jupyter/ipython take the *User Interface Tour* from the toolbar above (Help -> User Interface Tour).
> This notebook introduces the [IPython widgets](https://github.com/ipython/ipywidgets), as implemented in [IHaskell](https://github... | github_jupyter |
# VARLiNGAM
## Import and settings
In this example, we need to import `numpy`, `pandas`, and `graphviz` in addition to `lingam`.
```
import os
os.environ["PATH"] += os.pathsep + '/Users/elena/opt/anaconda3/lib/python3.7/site-packages/graphviz'
from sklearn.preprocessing import StandardScaler
import numpy as np
import... | github_jupyter |
# Contanimate DNS Data
```
"""
Make dataset pipeline
"""
import pandas as pd
import numpy as np
import os
from collections import Counter
import math
import torch
from torch.utils.data import DataLoader
from torch.nn.utils.rnn import pad_sequence
from dga.models.dga_classifier import DGAClassifier
from dga.datasets.do... | github_jupyter |
# Eliminating Outliers
Eliminating outliers is a big topic. There are many different ways to eliminate outliers. A data engineer's job isn't necessarily to decide what counts as an outlier and what does not. A data scientist would determine that. The data engineer would code the algorithms that eliminate outliers from... | github_jupyter |
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