code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
# Cordex preprocessing
```
%load_ext autoreload
%autoreload 2
import xclim
xclim.__version__
import os
import intake
import xarray as xr
import numpy as np
from tqdm.notebook import tqdm
os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
xr.set_options(keep_attrs=True)
print(np.__version__)
print(xr.__version__)
import ... | github_jupyter |
# Synthetic seismic: wedge
We're going to make the famous wedge model, which interpreters can use to visualize the tuning effect. Then we can extend the idea to other kinds of model.
## Make a wedge earth model
```
import matplotlib.pyplot as plt
import numpy as np
length = 80 # x range
depth = 200 # z range
```
... | github_jupyter |
```
#@title Environment Setup
import glob
BASE_DIR = "gs://download.magenta.tensorflow.org/models/music_vae/colab2"
print('Installing dependencies...')
!apt-get update -qq && apt-get install -qq libfluidsynth1 fluid-soundfont-gm build-essential libasound2-dev libjack-dev
!pip install -q pyfluidsynth
!pip install -qU... | github_jupyter |
### Counter
The `Counter` dictionary is one that specializes for helping with, you guessed it, counters!
Actually we used a `defaultdict` earlier to do something similar:
```
from collections import defaultdict, Counter
```
Let's say we want to count the frequency of each character in a string:
```
sentence = 'the... | github_jupyter |
# Download Data
This notebook downloads the necessary data to replicate the results of our paper on Gender Inequalities on Wikipedia.
Note that we use a file named `dbpedia_config.py` where we set which language editions we will we study, as well as where to save and load data files.
By [Eduardo Graells-Garrido](htt... | github_jupyter |
# Quantum Cryptography: Quantum Key Distribution
***
### Contributors:
A.J. Rasmusson, Richard Barney
Have you ever wanted to send a super secret message to a friend? Then you need a key to encrypt your message, and your friend needs the same key to decrypt your message. But, how do you send a super secret key to your... | 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 |
_ELMED219-2021_. Alexander S. Lundervold, 10.01.2021.
# Natural language processing and machine learning: a small case-study
This is a quick example of some techniques and ideas from natural language processing (NLP) and some modern approaches to NLP based on _deep learning_.
> Note: we'll take a close look at what ... | github_jupyter |
<a href="https://colab.research.google.com/github/kalz2q/mycolabnotebooks/blob/master/affinitydesigner.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# メモ
affinity designer を学ぶ
```
%%html
<svg width="300" viewBox="0 0 348 316" xmlns="http://www.w3... | github_jupyter |
# Pocessing GWO hourly meteorological data
**Author: Jun Sasaki Coded on February 13, 2022 Updated on February 14, 2022.**<br>
Extract and plot GWO (Ground Weather Observation) hourly data.
```
from metdata import gwo
from datetime import datetime
import matplotlib.pyplot as plt
from matplotlib.ticker import Multipl... | github_jupyter |
#### Measures of central tendencies
```
from typing import List
daily_minutes = [1,68.77,51.25,52.08,38.36,44.54,57.13,51.4,41.42,31.22,34.76,54.01,38.79,47.59,49.1,27.66,41.03,36.73,48.65,28.12,46.62,35.57,32.98,35,26.07,23.77,39.73,40.57,31.65,31.21,36.32,20.45,21.93,26.02,27.34,23.49,46.94,30.5,33.8,24.23,21.4,27.9... | github_jupyter |
```
pip install jupyter-dash
pip install dash_daq
pip install --ignore-installed --upgrade plotly==4.5.0
```
At this point, restart the runtime environment for Colab
```
import pandas as pd
import numpy as np
import datetime
import matplotlib.pyplot as plt
import random
import scipy.stats
import plotly.express as px
... | github_jupyter |
## Problem 1
___
In this problem, we will build a Multilayer Perceptron (MLP) and train it on the MNIST hand-written digit dataset.
### Summary:
___
[Question 1](#1.1)
[Question 2](#1.2)
[Question 3](#1.3)
[Question 4](#1.4)
___
### 1. Building the Model <a id='1.1'></a>
__________
**1.1) Build an MLP and... | github_jupyter |
This Notebook is a short example of how to use the Ising solver implemented using the QAOA algorithm. We start by declaring the import of the ising function.
```
from grove.ising.ising_qaoa import ising
from mock import patch
```
This code finds the global minima of an Ising model with external fields of the form
$... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import ListedColormap
from ml.data import create_lineal_data
from ml.visualization import decision_boundary
%matplotlib inline
```
# Función de coste y gradiente
## Generación de datos
### Entrenamiento
```
np.random.seed(0) # Para hac... | github_jupyter |
**Math - Linear Algebra**
*Linear Algebra is the branch of mathematics that studies [vector spaces](https://en.wikipedia.org/wiki/Vector_space) and linear transformations between vector spaces, such as rotating a shape, scaling it up or down, translating it (ie. moving it), etc.*
*Machine Learning relies heavily on L... | github_jupyter |
```
import pandas as pd
import os
from tqdm import tqdm
from utils import avg, evidence_to_mask, text_len_scatter
def to_data_df(df, data_dir):
data_df = []
columns = ['text', 'classification', 'rationale' ,'query']
for i in tqdm(range(len(df))):
df_row = df.loc[i]
doc_id = df_row[... | github_jupyter |
# Notebook served by Voilà
#### Notebook copied from https://github.com/ChakriCherukuri/mlviz
<h2>Gradient Descent</h2>
* Given a the multi-variable function $\large {F(x)}$ differentiable in a neighborhood of a point $\large a$
* $\large F(x)$ decreases fastest if one goes from $\large a$ in the direction of the neg... | github_jupyter |
# Wrangle & Analyze Data
### (WeRateDogs Twitter Archive)
<ul>
<li><a href="#intro">Introduction</a></li>
<li><a href="#wrangle">Data Wrangling</a></li>
<ul>
<li><a href="#gather">Gathering Data</a></li>
<li><a href="#assess">Assessing Data</a></li>
<li><a href="#clean">Cle... | github_jupyter |
# Determine derivative of Jacobian from angular velocity to exponential rates
Peter Corke 2021
SymPy code to deterine the time derivative of the mapping from angular velocity to exponential coordinate rates.
```
from sympy import *
```
A rotation matrix can be expressed in terms of exponential coordinates (also cal... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/GetStarted/02_adding_data_to_qgis.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_bl... | github_jupyter |
# Starbucks Capstone Challenge
## Project Overview
This data set contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. Once every few days, Starbucks sends out an offer to users of the mobile app. An offer can be merely an advertisement for a drink or an actual offer such as a dis... | github_jupyter |
<table>
<tr>
<td style="background-color:#ffffff;"><a href="https://qsoftware.lu.lv/index.php/qworld/" target="_blank"><img src="..\images\qworld.jpg" width="70%" align="left"></a></td>
<td style="background-color:#ffffff;" width="*"></td>
<td style="background-color:#ffffff;vertical-align... | github_jupyter |
```
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
import pickle
from tqdm.notebook import tqdm
from tqdm import trange
%matplotlib inline
def read_list_of_arrays(filename):
A = pickle.load(open(filename, 'rb'))
if len(A) == 3:
print(A[1][0], A[2][0]... | github_jupyter |
```
import torch
import argparse
import csv
import datetime
import math
import torch.nn as nn
from torch.nn.functional import leaky_relu, softmax
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader
from collections import Counter
from GANutils import *
from utils import *
... | github_jupyter |
# SP via class imbalance
Example [test scores](https://www.brookings.edu/blog/social-mobility-memos/2015/07/29/when-average-isnt-good-enough-simpsons-paradox-in-education-and-earnings/)
SImpson's paradox can also occur due to a class imbalance, where for example, over time the value of several differnt subgroups all ... | github_jupyter |
```
#写函数，求n个随机整数均值的平方根，整数范围在m与k之间
import random,math
def pingfanggeng():
m = int(input('请输入一个大于0的整数，作为随机整数的下界，回车结束。'))
k = int(input('请输入一个大于0的整数，作为随机整数的上界，回车结束。'))
n = int(input('请输入随机整数的个数，回车结束。'))
i=0
total=0
while i<n:
total=total+random.randint(m,k)
i=i+1
average=total/... | github_jupyter |
# `pandas` Part 2: this notebook is a 2nd lesson on `pandas`
## The main objective of this tutorial is to slice up some DataFrames using `pandas`
>- Reading data into DataFrames is step 1
>- But most of the time we will want to select specific pieces of data from our datasets
# Learning Objectives
## By the end of th... | github_jupyter |
# Exp 101 analysis
See `./informercial/Makefile` for experimental
details.
```
import os
import numpy as np
from IPython.display import Image
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import seaborn as sns
sns.set_style('ticks')
matplotlib.... | github_jupyter |
# Creating a Linear Cellular Automaton
Let's start by creating a linear cellular automaton
)
from learntools.python.ex7 import *
print('Setup complete.')
```
# E... | github_jupyter |
# Exercise 4
Hi everyone, today we are going to have an introduction to Machine Learning and Deep Learning, as well as we will work with the Linear/Logistic regression and Correlation.
# Part 1: Curve Fitting:
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
```
Sometimes we are going to fi... | github_jupyter |
# Figures for comparison of arrival direction and joint models
Here use the output from the `arrival_vs_joint` notebook to plot the figures shown in the paper.
<br>
<br>
*This code is used to produce Figures 6, 7 and 8 (left panel) in Capel & Mortlock (2019).*
```
import numpy as np
import h5py
import matplotlib as m... | github_jupyter |
# Setting up the Data Science Environment
One of the largest hurdles beginners face is setting up an environment that they can quickly get up and running and analyzing data.
### Objectives
1. Understand the difference between interactive computing and executing a file
1. Ensure that Anaconda is installed properly wi... | github_jupyter |
```
import os, sys, time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import scipy.sparse
import sklearn
from sklearn.pipeline import Pipeline
from sklearn.model_selection import PredefinedSplit
from sklearn.linear_model import LinearRegression, Elas... | github_jupyter |
#### stdin and stdout ** skipped**
piping data at the command line if you run Python scripts through it.
```
import sys, re
sys.argv[0]
# sys.argv is the list of command-line arguments
# sys.argv[0] is the name of the program itself
# sys.argv[1] will be the regex specified at the command line
regex = sys.argv[1]
fo... | github_jupyter |
# **Spotify Data Analysis**
---
I want to accomplish 2 things with this project. First, I want to learn how to use the Spotify API. Learning how to use this API serves as a great gateway into the API Universe.
The documentation is amazing, the API calls you can make to Spotify, per day, is more than enough for almost ... | github_jupyter |
```
from molmap import model as molmodel
import molmap
import matplotlib.pyplot as plt
import pandas as pd
from tqdm import tqdm
from joblib import load, dump
tqdm.pandas(ascii=True)
import numpy as np
import tensorflow as tf
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
np.random.seed(123)
tf.compat.v1.set_rando... | github_jupyter |
```
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import geopy.distance as gd
from mpl_toolkits.basemap import Basemap
from datetime import datetime, timedelta
pd.options.mode.chained_assignment = None
# from pandarallel import pandarallel
vessel_information = pd.read_c... | github_jupyter |
# The Discrete-Time Fourier Transform
*This Jupyter notebook is part of a [collection of notebooks](../index.ipynb) in the bachelors module Signals and Systems, Comunications Engineering, Universität Rostock. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*... | github_jupyter |
# KakaoBrunch12M
KakaoBrunch12M은 [카카오 아레나에서 공개한 데이터](https://arena.kakao.com/datasets?id=2)로 [브런치 서비스](https://brunch.co.kr) 사용자를 통해 수집한 데이터입니다.
이 예제에서는 브런치 데이터에서 ALS를 활용해 특정 글과 유사한 글을 추천하는 예제와 개인화 추천 예제 두 가지를 살펴보겠습니다.
```
import buffalo.data
from buffalo.algo.als import ALS
from buffalo.algo.options import ALSOption... | github_jupyter |
# Interactive Map - Confirmed Cases in the US by State
> Interactive Visualizations of The Count and Growth of COVID-19 in the US.
- comments: true
- author: Asif Imran
- categories: [growth, usa, altair, interactive]
- image: images/us-growth-state-map.png
- permalink: /growth-map-us-states/
```
#hide
import request... | github_jupyter |
# 列表List
- 一个列表可以储存任意大小的数据集合，你可以理解为他是一个容器
```
a = [1,2,3,'a',1.0,True,[1,2]]
a
```
## 先来一个例子爽一爽
## 创建一个列表
- a = [1,2,3,4,5]
```
import numpy as np
a = [[1,2],[3,4]]
np.array(a)
```
## 列表的一般操作
```
a = 'Kim'
b = 'im'
b in a
a = [1,2,'Kim',1.0]
b = 'im'
b in a
a = [1]#列表只能... | github_jupyter |
```
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from pathlib import Path
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import Stan... | github_jupyter |
> **Tip**: Welcome to the Investigate a Dataset project! You will find tips in quoted sections like this to help organize your approach to your investigation. Before submitting your project, it will be a good idea to go back through your report and remove these sections to make the presentation of your work as tidy as ... | github_jupyter |
# Análisis de la Movilidad en Bogotá
¿Cuáles son las rutas más críticas de movilidad y sus características en la ciudad de Bogotá?
Se toman los datos de la plataforma:
https://datos.movilidadbogota.gov.co
```
import pandas as pd
import os
os.chdir('../data_raw')
data_file_list = !ls
data_file_list
data_file_list[len... | github_jupyter |
**Chapter 11 – Training Deep Neural Networks**
_This notebook contains all the sample code and solutions to the exercises in chapter 11._
<table align="left">
<td>
<a target="_blank" href="https://colab.research.google.com/github/ageron/handson-ml2/blob/master/11_training_deep_neural_networks.ipynb"><img src="h... | github_jupyter |
## Imports
```
from bayes_opt import BayesianOptimization
import pandas as pd
import numpy as np
from datetime import timedelta
from tqdm import tqdm_notebook as tqdm
from sklearn import metrics
from sklearn.model_selection import StratifiedKFold
import lightgbm as lgb
from matplotlib import pyplot as plt
#import seab... | github_jupyter |
___
<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
___
# NLP (Natural Language Processing) with Python
This is the notebook that goes along with the NLP video lecture!
In this lecture we will discuss a higher level overview of the basics of Natural Language Processing, which basical... | github_jupyter |
# "The Evolution of Music Industry Sales"
> "Deep dive analysis into music industry sales over the past 40 years."
- toc:true
- branch: master
- badges: true
- comments: true
- author: Karinn Murdock
- categories: [fastpages, jupyter]
Karinn Murdock
03/14/2022
# The Evolution of Music Industry Sales
## Introductio... | github_jupyter |
# Developing an AI application
Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall appli... | github_jupyter |
```
import pandas as pd
import seaborn as sns
desc = pd.read_csv('data/VariableDefinitions.csv')
train = pd.read_csv('data/Train.csv')
test = pd.read_csv('data/Test.csv')
sub = pd.read_csv('data/Samplesubmission.csv')
df.loc[1].values
desc.drop(['Unnamed: 1'], axis=1, inplace=True)
desc.iloc[2:, ].values
train.head(2)
... | github_jupyter |
```
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import matplotlib
from matplotlib import rcParams
rcParams['font.family'] = 'monospace'
#rcParams['font.sans-serif'] = ['Tahoma']
import numpy as np
import math
import datetime
import networkx as nx
import os
def TodaysDate():
Today = datetime.da... | github_jupyter |
# Developing an AI application
Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall appli... | github_jupyter |
```
%%html
<style>
.jp-Cell {max-width: 1024px !important; margin: auto}
.jp-Cell-inputWrapper {max-width: 1024px !important; margin: auto}
.jp-MarkdownOutput p {text-align: justify;}
.jupyter-matplotlib-figure {margin: auto;}
</style>
%matplotlib widget
from pyvisco import inter
GUI = inter.Control()
`... | github_jupyter |
# Numpy (✗)
> Makine Öğrenmesi ve Derin Öğrenme için gerekli Numpy konuları.
- toc: true
- badges: true
- comments: true
- categories: [jupyter]
- image: images/chart-preview.png
# Set up
```
import numpy as np
# Set seed for reproducibility
np.random.seed(seed=1234)
```
# Basics
Let's take a took at how to creat... | github_jupyter |
<img src="images/usm.jpg" width="480" height="240" align="left"/>
# MAT281 - Introducción a Pandas
## Objetivos de la clase
* Aprender conceptos básicos de la librería pandas.
## Contenidos
* [Pandas](#c1)
<a id='c1'></a>
## Pandas
<img src="images/pandas.jpeg" width="360" height="240" align="center"/>
[Pand... | github_jupyter |
# CODE TO PERFORM SIMPLE LINEAR REGRESSION ON FUEL CONSUMPTION DATASET
# Dr. Ryan @STEMplicity
# PROBLEM STATEMENT
- You have been hired as a consultant to a major Automotive Manufacturer and you have been tasked to develop a model to predict the impact of increasing the vehicle ho... | github_jupyter |
```
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set_style("whitegrid", {'axes.grid' : False})
import joblib
import catboost
import xgboost as xgb
import lightgbm as lgb
from category_encoders import BinaryEncoder
from sklearn.metric... | github_jupyter |
## Tutorial on QAOA Compiler
This tutorial shows how to use the QAOA compiler for QAOA circuit compilation and optimization. (https://github.com/mahabubul-alam/QAOA-Compiler).
### Inputs to the QAOA Compiler
The compiler takes three json files as the inputs. The files hold the following information:
* ZZ-interactions... | github_jupyter |
# Session 7: The Errata Review No. 1
This session is a review of the prior six sessions and covering those pieces that were left off. Not necessarily errors, but missing pieces to complete the picture from the series. These topics answer some questions and will help complete the picture of the C# language features d... | github_jupyter |
```
"""
Made on July 10th, 2019
@author: Theodore Pena
@contact: theodore.pena@tufts.edu
"""
line_color = 'purple' # Color for the 10-panel plots
x_Delta = np.log10(54) # In our time units, the time between SDSS and HSC
default_Delta_value = -0.0843431604042636
data_path = '/home/tpena01/AGN_variability_project/Simula... | github_jupyter |
<a href="https://colab.research.google.com/github/jonkrohn/ML-foundations/blob/master/notebooks/2-linear-algebra-ii.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Linear Algebra II: Matrix Operations
This topic, *Linear Algebra II: Matrix Operat... | github_jupyter |
## Classification
Classification - predicting the discrete class ($y$) of an object from a vector of input features ($\vec x$).
Models used in this notebook include: Logistic Regression, Support Vector Machines, KNN
**Author List**: Kevin Li
**Original Sources**: http://sci... | github_jupyter |
<a href="https://colab.research.google.com/github/davidedomini/stroke_predictions/blob/main/StrokePrediction.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Stroke Prediction
**Davide Domini** <br>
davide.domini@studio.unibo.it<br> <br>
Programm... | github_jupyter |
# Inference
## Imports & Args
```
import argparse
import json
import logging
import os
import random
from io import open
import numpy as np
import math
import _pickle as cPickle
from scipy.stats import spearmanr
from tensorboardX import SummaryWriter
from tqdm import tqdm
from bisect import bisect
import yaml
from e... | github_jupyter |
```
import datetime
import time
import json
import os
import string
import requests
import sys
import traceback
import azure.cosmos.cosmos_client as cosmos_client
from helpers import keys
from helpers import nlp_helper
from gremlin_python.driver import client, serializer
config = {
'ENDPOINT': keys.cosmos_uri,
... | github_jupyter |
# Day 9 - Finding the sum, again, with a running series
* https://adventofcode.com/2020/day/9
This looks to be a variant of the [day 1, part 1 puzzle](./Day%2001.ipynb); finding the sum of two numbers in a set. Only now, we have to make sure we know what number to remove as we progres! This calls for a _sliding windo... | github_jupyter |
# Intro to matplotlib - plotting in Python
Most of this material is reproduced or adapted from
https://github.com/jakevdp/PythonDataScienceHandbook
The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https:... | github_jupyter |
# Campus SEIR Modeling
## Campus infection data
The following data consists of new infections reported since August 3, 2020, from diagnostic testing administered by the Wellness Center and University Health Services at the University of Notre Dame. The data is publically available on the [Notre Dame Covid-19 Dashboar... | github_jupyter |
```
# dependencies
import pandas as pd
from sqlalchemy import create_engine, inspect
# read raw data csv
csv_file = "NYC_Dog_Licensing_Dataset.csv"
all_dog_data = pd.read_csv(csv_file)
all_dog_data.head(10)
# trim data frame to necessary columns
dog_data_df = all_dog_data[['AnimalName','AnimalGender','BreedName','Bo... | github_jupyter |
# What's this PyTorch business?
You've written a lot of code in this assignment to provide a whole host of neural network functionality. Dropout, Batch Norm, and 2D convolutions are some of the workhorses of deep learning in computer vision. You've also worked hard to make your code efficient and vectorized.
For the ... | github_jupyter |
<a href="https://colab.research.google.com/github/tmbern/DS-Unit-2-Kaggle-Challenge/blob/master/module4-classification-metrics/Unit_2_Sprint_2_Module_4_CLASS-lecture-notebook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Lambda School Data Science... | github_jupyter |
# test note
* jupyterはコンテナ起動すること
* テストベッド一式起動済みであること
```
!pip install --upgrade pip
!pip install --force-reinstall ../lib/ait_sdk-0.1.7-py3-none-any.whl
from pathlib import Path
import pprint
from ait_sdk.test.hepler import Helper
import json
# settings cell
# mounted dir
root_dir = Path('/workdir/root/ait')
ait_n... | github_jupyter |
# HM2: Numerical Optimization for Logistic Regression.
### Name: [Your-Name?]
## 0. You will do the following:
1. Read the lecture note: [click here](https://github.com/wangshusen/DeepLearning/blob/master/LectureNotes/Logistic/paper/logistic.pdf)
2. Read, complete, and run my code.
3. **Implement mini-batch SGD** ... | github_jupyter |
```
%matplotlib inline
import math
import numpy
import pandas
import seaborn
import matplotlib.pyplot as plt
import plot
def fmt_money(number):
return "${:,.0f}".format(number)
def run_pmt(market, pmt_rate):
portfolio = 1_000_000
age = 65
max_age = 100
df = pandas.DataFrame(index=range(age, max_age... | github_jupyter |
```
import os
import json
import boto3
import sagemaker
import numpy as np
from source.config import Config
config = Config(filename="config/config.yaml")
sage_session = sagemaker.session.Session()
s3_bucket = config.S3_BUCKET
s3_output_path = 's3://{}/'.format(s3_bucket)
print("S3 bucket path: {}".format(s3_output_p... | github_jupyter |
<!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/Pytho... | github_jupyter |
### Note
* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think through the steps.
```
# Dependencies and Setup
import pandas as pd
# File to Load
school_data_to_load = "Resources/schools_complete.csv"
student_data_to_load = "Resources/stud... | github_jupyter |
# TV Script Generation
In this project, you'll generate your own [Seinfeld](https://en.wikipedia.org/wiki/Seinfeld) TV scripts using RNNs. You'll be using part of the [Seinfeld dataset](https://www.kaggle.com/thec03u5/seinfeld-chronicles#scripts.csv) of scripts from 9 seasons. The Neural Network you'll build will ge... | github_jupyter |
```
import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn... | github_jupyter |
**[CDS-01]** 必要なモジュールをインポートして、乱数のシードを設定します。
```
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(20160704)
tf.set_random_seed(20160704)
```
**[CDS-02]** CIFAR-10 のデータセットをダウンロードします。ダウンロード完了まで少し時間がかかります。
```
%%bash
mkdir -p /tmp/cifar10_data
cd /tmp/cifar10_data
curl -OL http:... | github_jupyter |
```
# - Decide which map to plot
# in main notebook code
#mapvarnow = 'skj' # choose: skj, bet
# - Define constant plot params
stipsizenow = 10; stipmarknow = 'o'
stipfacecolnow = 'none'
stipedgeltcolnow = 'whitesmoke'
stipewnow = 0.8 # marker edge width
eezfcnow = 'none'; eezlcnow = 'lightgray' #'silver'
eezlsnow = '... | github_jupyter |
# Ejemplo de simulación numérica
```
import numpy as np
from scipy.integrate import odeint
from matplotlib import rc
import matplotlib.pyplot as plt
%matplotlib inline
rc("text", usetex=True)
rc("font", size=18)
rc("figure", figsize=(6,4))
rc("axes", grid=True)
```
## Problema físico
# Chapter 8: Basic Data Wrangling With Pandas
<h2>Chapter Outline<span class="tocSkip"></span></h2>
<hr>
<div class="toc"><ul class="toc-item"><li><span><a href="#1.-DataFrame-Characteristics" data-toc-modified-id="1.-DataFrame-Characteristics-2">1. DataFrame Characteristics</a></span></li><li... | github_jupyter |
```
import sys,os
sys.path.append('../')
from deep_rl import *
import matplotlib.pyplot as plt
import torch
from tqdm.notebook import trange, tqdm
import random
import numpy as np
import time
%load_ext autoreload
%reload_ext autoreload
%autoreload 2
select_device(0)
def dqn_feature(hu=676,**kwargs):
generate_tag(kw... | github_jupyter |
```
import time
import pandas as pd
import numpy as np
city_con = {
1: ["chicago.csv","Chicago"],
2: ["new_york_city.csv","New York City"],
3: ["washington.csv","Washington"],
4:[0,"Exit"],
"NS":[0,"Not selected"]
}
fltr_choice = {
1:"Month",
2:"Day",
3:"Show all data",
4:"Exit",
... | github_jupyter |
```
# installing required module
!pip install fuzzy-c-means
import cv2
import numpy as np
import math
import bisect
from google.colab.patches import cv2_imshow
from skimage import morphology
from sklearn.cluster import KMeans
from fcmeans import FCM
def imadjust(src, tol=1, vin=[0,255], vout=(0,255)):
# src : inp... | github_jupyter |
```
import pandas as pd
pd.set_option('max_colwidth', 400)
df_glove_parag = pd.read_csv("../results/final_results_08_03/test_results_glove_preprocessed_reports_08_03.csv")
df_glove_no_back_parag = pd.read_csv("../results/final_results_08_03/test_results_glove_no_back_preprocessed_reports_08_03.csv")
df_ft_parag = pd.r... | github_jupyter |
# Modeling and fitting
## Prerequisites
- Knowledge of spectral analysis to produce 1D On-Off datasets, [see the following tutorial](spectrum_analysis.ipynb)
- Reading of pre-computed datasets [see the MWL tutorial](analysis_mwl.ipynb)
- General knowledge on statistics and optimization methods
## Proposed approach
... | github_jupyter |
```
#hide
from qbism import *
```
# Tutorial
> "Chauncey Wright, a nearly forgotten philosopher of real merit, taught me when young that I must not say necessary about the universe, that we don’t know whether anything is necessary or not. So I describe myself as a bettabilitarian. I believe that we can bet on the beh... | github_jupyter |
```
%run ../Python_files/load_dicts.py
%run ../Python_files/util.py
from util import *
import numpy as np
from numpy.linalg import inv, matrix_rank
import json
# # load logit_route_choice_probability_matrix
# P = zload('../temp_files/logit_route_choice_probability_matrix_Sioux.pkz')
# P = np.matrix(P)
# print('rank of... | github_jupyter |
```
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import datetime
import pytz
import urllib as ur
import json
```
## Funções para auxiliar a manipulação do tempo
```
def convert_datetime_timezone(dt, tz1, tz2):
"""
Converte uma hora no fuso UTC ou São Paulo para um provável fuso de Va... | github_jupyter |
# 5장
```
import matplotlib
matplotlib.rc('font', family="NanumBarunGothicOTF")
%matplotlib inline
```
# 5.2 아이리스 데이터셋
```
import pandas as pd
from matplotlib import pyplot as plt
import sklearn.datasets
def get_iris_df():
ds = sklearn.datasets.load_iris()
df = pd.DataFrame(ds['data'], columns=ds['feature... | github_jupyter |
In this notebook you can define your own configuration and run the model based on your custom configuration.
## Dataset
`dataset_name` is the name of the dataset which will be used in the model. In case of using KITTI, `dataset_path` shows the path to `data_paths` directory that contains every image and its pair path... | github_jupyter |
# Case 1. Heart Disease Classification
Joona Klemetti
4.2.2018
Cognitive Systems for Health Technology Applications
Helsinki Metropolia University of Applied Science
# 1. Objectives
The aim of this case is learn to manipulate and read data from externals sources using panda’s functions and use keras de... | github_jupyter |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.