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```
import sys
import numpy as np
import scipy.io as sio
import keras
import numpy as np
import os
import matplotlib
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from keras.optimizers import SGD
from keras.optimizers import Adam, Adadelta
from keras.callbacks import ModelCheckp... | github_jupyter |
# CycleGAN, Image-to-Image Translation
In this notebook, we're going to define and train a CycleGAN to read in an image from a set $X$ and transform it so that it looks as if it belongs in set $Y$. Specifically, we'll look at a set of images of [Yosemite national park](https://en.wikipedia.org/wiki/Yosemite_National_P... | github_jupyter |
# TensorFlow
In this notebook, we'll learn the basics of [TensorFlow + Keras](https://tensorflow.org), which is a machine learning library used to build dynamic neural networks. We'll learn about the basics, like creating and using Tensors.
# Set seeds
```
%tensorflow_version 2.x
import numpy as np
import tensorflow... | github_jupyter |
```
%load_ext watermark
import numpy as np
import pandas as pd
```
## Load the JupyterHub logs
```
columns = ['user', 'machine', 'session_start', 'session_end', 'session_length', 'log_file']
df_all = pd.read_csv("../data/jhub_logs.csv.gz", parse_dates=["session_start", "session_end"])
df_all["session_length"] = (df_a... | github_jupyter |
```
%serialconnect
# below is the esp8266 version
# RST | GPIO1 TX
# A0 | GPIO3 RX
# D0 GPIO16 | GPIO5 D1 SCL
# SCK D5 GPIO14 | GPIO4 D2 SDA
# MISO D6 GPIO12 | GPIO0 D3
# MOSI D7 GPIO13 | GPIO2 D4 LED
# SS D8 GPIO15 | GND
# 3V3 ... | github_jupyter |
# MAPEM de Pierro algorithm for the Bowsher prior
One of the more popular methods for guiding a reconstruction based on a high quality image was suggested by Bowsher. This notebook explores this prior.
We highly recommend you look at the [PET/MAPEM](../PET/MAPEM.ipynb) notebook first. This example extends upon the qua... | github_jupyter |
```
%matplotlib inline
```
*************************
Text rendering With LaTeX
*************************
Rendering text with LaTeX in Matplotlib.
Matplotlib has the option to use LaTeX to manage all text layout. This
option is available with the following backends:
* Agg
* PS
* PDF
The LaTeX option is activated ... | github_jupyter |
```
#all_slow
```
# Tutorial - Migrating from Lightning
> Incrementally adding fastai goodness to your Lightning training
We're going to use the MNIST training code from Lightning's 'Quick Start' (as at August 2020), converted to a module. See `migrating_lightning.py` for the Lightning code we are importing here.
`... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
```
# LDLS Demo
This notebook demonstrates how to use LDLS to perform instance segmentation of a LiDAR point cloud. This demo uses Frame 571 from the KITTI object detection dataset.
## Setup
Import LiDAR segmentation modules:
```
import numpy as np
from pathlib import Path
im... | github_jupyter |
# Background Computation with "ofilter"
This notebook illustrate background calculations using `ofilter` algorithm adapted from IRAF.
```
import matplotlib.pyplot as plt
import numpy as np
import scipy
import ofiltsky
%matplotlib inline
```
### Generate Data
```
# Set the seed for reproducibility
np.random.seed(0... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W3D2_DynamicNetworks/student/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
# Ne... | github_jupyter |
# Creating city models and objects
In this tutorial we explore how to create new city models with using `cjio`'s
API.
```
from pathlib import Path
from cjio import cityjson
from cjio.models import CityObject, Geometry
```
Set up paths for the tutorial.
```
package_dir = Path(__name__).resolve().parent.parent.pare... | github_jupyter |
# Image classification of simulated AT-TPC events
Welcome to this project in applied machine learning. In this project we will tackle a simple classification problem of two different classes. The classes are simulated reaction types for the Ar(p, p') experiment conducted at MSU, in this task we'll focus on the classi... | github_jupyter |
### Import Package
```
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np
import traceback
import contextlib
import pathlib
```
### Load Dataset
```
mnist = tf.keras.datasets.fashion_mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
print("Train... | github_jupyter |
<a href="https://colab.research.google.com/github/probml/probml-notebooks/blob/main/notebooks/bnn_hmc_gaussian.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# (SG)HMC for inferring params of a 2d Gaussian
Based on
https://github.com/google-rese... | github_jupyter |
# CIFAR10 전이학습 기반 분류기
이 노트북은 사전 훈련된 심층-CNN 중에서 VGG16으로 전이학습의 개념을 확용한 분류기를 구축하는 단계를 개략적으로 설명한다.
```
%matplotlib inline
# Pandas and Numpy for data structures and util fucntions
import scipy as sp
import numpy as np
import pandas as pd
from numpy.random import rand
pd.options.display.max_colwidth = 600
# Scikit 임포트
f... | github_jupyter |
# Table of Contents
<p><div class="lev1 toc-item"><a href="#Setting-up-your-machine-Learning-Application" data-toc-modified-id="Setting-up-your-machine-Learning-Application-1"><span class="toc-item-num">1 </span>Setting up your machine Learning Application</a></div><div class="lev2 toc-item"><a href="#Train... | github_jupyter |
[source](../api/alibi_detect.ad.model_distillation.rst)
# Model distillation
## Overview
[Model distillation](https://arxiv.org/abs/1503.02531) is a technique that is used to transfer knowledge from a large network to a smaller network. Typically, it consists of training a second model with a simplified architecture... | github_jupyter |
## 각 형태소 분석기 비교 (KKMA, KOMORAN, MECAB, TWITTER)
##### 긍정리뷰 3개 vs. 부정리뷰 3개를 기준으로
------------
#### 긍정리뷰
"3달정도 사용해오고 있는데 가성비부터 최고예요. 운동도 하고 교통비도 아끼고 대만족입니다."
"QR코드 이용해서 대여하니 빠르고 편해요. 처음 이용해봤는데 좋았네요^^"
"너무너무 좋아요. 결제도 쉽고 대여소 찾는 것도 쉽고 정기권 끊어서 타고있어요. 이거 덕분에 자전거 많이 타고있어요!! 감사합니다~"
#### 부정리뷰
"소셜로그인은 ... | github_jupyter |
<a href="https://colab.research.google.com/github/liscolme/EscapeEarth/blob/main/Interns/Elise/BLS_Function_Test.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
from google.colab import drive
drive.mount('/content/gdrive')
######################... | github_jupyter |
```
import math
import numpy as np
from sympy import *
t, s, k = symbols('t, s, k')
# Defino los métodos de suma de rectángulos - izquierda
def _Riemman_izq(Func, limA, limB, numI):
""" Método de la Bisección para encontrar raíces en un intervalo.
## Parámetros:
Func (function) : función que de... | github_jupyter |
```
file_1 = """Stock Close Beta Cap
Apple 188.72 0.2 895.667B
Tesla 278.62 0.5 48.338B"""
file_2 = """Employee Wage Hired Promotion
Linda 3000 2017 Yes
Bob 2000 2016 No
Joshua 800 2019 Yes"""
```
### My solution
Other approaches are possible
```
def parser(stringa):
"""
Parse string and returns dict of list... | github_jupyter |
```
import numpy as np
import pandas as pd
import xarray as xr
from glob import glob
import pymongo
import pdb
from datetime import datetime, timedelta
from sqlalchemy import create_engine
import time
import psycopg2
import os
from io import StringIO
from scipy import sparse
from scipy.sparse.linalg import svds
impo... | github_jupyter |
<a href="https://colab.research.google.com/github/satyajitghana/PadhAI-Course/blob/master/11_VectorizedGDAlgorithms.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors
impor... | github_jupyter |
<a href="https://colab.research.google.com/github/mottaquikarim/PYTH2/blob/master/src/Topics/nb/basic_data_types.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Basic Data Types
Let's discuss data types, variables, and naming.
A data type is a u... | github_jupyter |
# CT-LTI: Multiple Sample Performance Evaluation Table
This table is found in the appendix section A.4. and summarizes the performance comparison between NODEC and OC in relative terms of error and energy. Without extensive hyperparameter optimization we see that NODEC is competitive to OC for all graphs and intial-tar... | github_jupyter |
## PLINK GWAS Regression Tutorial
These commands walk through running the GWAS regressions from Marees et al. 2018 using PLINK.
As in all PLINK tutorials, the comments and code from the original tutorial are included with R steps commented out (and replaced by python where necessary) and to disambiguate between comme... | github_jupyter |
### quero usar matplotlib para ilustrar permutações
A primeira coisa é fazer circulos numerados
```
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
circle1=plt.Circle((0,0),.1,color='r', alpha=0.2, clip_on=False)
plt.axes(aspect="equal")
fig = plt.gcf()
fig.gca().add_artist(circle1)
plt.axis("o... | github_jupyter |
# Fitting to existing data
```
# Base Data Science snippet
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import time
from tqdm import tqdm_notebook
%matplotlib inline
%load_ext autoreload
%autoreload 2
```
Inspiration - https://www.lewuathe.com/covid-19-dynamics-with-sir-model.html... | github_jupyter |
<img src="https://raw.githubusercontent.com/brazil-data-cube/code-gallery/master/img/logo-bdc.png" align="right" width="64"/>
# <span style="color:#336699">Introduction to the SpatioTemporal Asset Catalog (STAC)</span>
<hr style="border:2px solid #0077b9;">
<div style="text-align: left;">
<a href="https://nbviewe... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import plotly.offline as py
import plotly.graph_objs as go
import plotly.tools as tls
import seaborn as sns
import io
from google.colab import files
uploaded = files.upload()
data = uploaded['LAND TEMPERATURES FILE FROM KAGGLE... | github_jupyter |
```
import bare
```
#### Plot detected interest points over images
```
image_file_name = 'image.tif'
ip_file_name = 'image.vwip'
ip_csv_file_name = bare.core.write_ip_to_csv(ip_file_name)
bare.plot.ip_plot(image_file_name, ip_csv_file_name)
```
!['example_plot_IP'](example_plots/interest_points/v2_sub8_interest_poi... | github_jupyter |
```
%matplotlib inline
import numpy as np
import pandas as pd
import scipy
import sklearn
import spacy
import matplotlib.pyplot as plt
import seaborn as sns
import re
from nltk.corpus import state_union, stopwords
from collections import Counter
from sklearn.model_selection import train_test_split
import warnings
warni... | github_jupyter |
<a href="https://colab.research.google.com/github/GivanTsai/Bert-cookbook/blob/main/BERT_Fine_Tuning_Sentence_Classification_v4.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# BERT Fine-Tuning Tutorial with PyTorch
By Chris McCormick and Nick Rya... | github_jupyter |
# Example: Polynomial Cureve Fitting
Observse a real-valued input variable $x$ $\rightarrow$ predict a real-valued target variable $t$
* $\textbf{x} \equiv (x_1, \cdots, x_i, \cdots, x_N)^T, \quad x_i \in [0, 1]$
* $\textbf{t} \equiv (t_1, \cdots, t_i, \cdots, t_N)^T, \quad t_i = \sin(2\pi x_i) + N(\mu, \sigma^2)$
... | github_jupyter |
## Initialization
```
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import math
import scipy.io
from scipy.special import expit
from math import *
from scipy import optimize
sns.set_style('whitegrid')
%matplotlib inline
```
## Loading Data
```
mat = scipy.io.loadmat('ex... | github_jupyter |
# Analyse wavefields
This notebook checks the velocity models and FD simulations output by `generate_velocity_models.py` and `generate_forward_simulations.py` are sensible.
```
import glob
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import numpy as np
import scipy as sp
import sys
sys.pat... | github_jupyter |
# Week 3: Transfer Learning
Welcome to this assignment! This week, you are going to use a technique called `Transfer Learning` in which you utilize an already trained network to help you solve a similar problem to the one it was originally trained to solve.
Let's get started!
```
import os
import zipfile
import matp... | github_jupyter |
# Introduction to Taxi ETL Job
This is the Taxi ETL job to generate the input datasets for the Taxi XGBoost job.
## Prerequirement
### 1. Download data
All data could be found at https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page
### 2. Download needed jars
* [cudf-21.12.2-cuda11.jar](https://repo1.maven.o... | github_jupyter |
```
# default_exp cli
#hide
from nbdev.showdoc import *
#export
from dash_oop_components.core import *
#export
import os
import webbrowser
from pathlib import Path
import click
```
# dashapp CLI
> a simple way of launching dashboards directly from the commandline
With `dash_oop_components` you can easily dump the co... | github_jupyter |
# Bag of Tricks Experiment
Analyze the effects of our different "tricks".
1. Sample matches off mask
2. Scale by hard negatives
3. L2 pixel loss on matches
We will compare standard network, networks missing one trick only, and a network without any tricks (i.e same as Tanner Schmidt)
```
import dense_correspondence... | github_jupyter |
# Calculate Shapley values
Shapley values as used in coalition game theory were introduced by William Shapley in 1953.
[Scott Lundberg](http://scottlundberg.com/) applied Shapley values for calculating feature importance in [2017](http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.... | github_jupyter |
# Allosteric pathways with current flow analysis on protein-cofactor networks
*This tutorial shows how to build and analyze networks that include protein residues and cofactors (e.g. lipids or small molecules).*
***Note***: To build and analyze a residue interaction network of the isolated protein only, just skip the... | github_jupyter |
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-59152712-8"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-59152712-8');
</script>
# Start-to-Finish Example: Validating Shifted Kerr-Schild i... | github_jupyter |
# Aerospace and Defense Portfolio Risk and Returns
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import math
import warnings
warnings.filterwarnings("ignore")
# fix_yahoo_finance is used to fetch data
import yfinance as yf
yf.pdr_override()
# input
# Aerospace and ... | github_jupyter |
```
import numpy as np
import pandas as pd
import scipy
print(f"SciPy version: {scipy.__version__}")
from collections import OrderedDict
import scipy.sparse as sp
import time
import random
from constants import (DATA_OCT, DATA_NOV, EXPORT_DIR, UX_CONSTANTS, SEED, NEW_USER_ID, NEW_PRODUCT_ID, T, USECOLS,
... | github_jupyter |
# **Working memory training**: Module allegiance matrix calculation
**Last edited:** 04-10-2018
Step 0: Loading libraries
--------------------------------
```
import sys
sys.path.append("..")
import os
%matplotlib inline
import scipy.io as sio
import numpy as np
from nilearn import plotting
import pandas as pd
i... | github_jupyter |
---
_You are currently looking at **version 1.0** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-machine-learning/resources/bANLa) course resource._
---
## Applied Machine... | github_jupyter |
## Dependencies
```
import json, warnings, shutil, glob
from jigsaw_utility_scripts import *
from scripts_step_lr_schedulers import *
from transformers import TFXLMRobertaModel, XLMRobertaConfig
from tensorflow.keras.models import Model
from tensorflow.keras import optimizers, metrics, losses, layers
SEED = 0
seed_ev... | github_jupyter |
# Data pre-processing steps
1. Remove columns that contain "Call" data
2. Transpose the dataframe so that each row is a patient and each column is a gene
3. Remove gene description and set the gene accession numbers as the column headers
4. Merge the data (expression values) with the class labels (patient numbers)
``... | github_jupyter |
# 1A.e - Correction de l'interrogation écrite du 14 novembre 2014
coût algorithmique, calcul de séries mathématiques
```
from jyquickhelper import add_notebook_menu
add_notebook_menu()
```
## Enoncé 1
### Q1
Le code suivant produit une erreur. Corrigez le programme.
```
nbs = [ 1, 5, 4, 7 ] #
for n in nbs: ... | github_jupyter |
```
import time
import glob
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_t... | github_jupyter |
# Spark JDBC to Databases
- [Overview](#spark-jdbc-overview)
- [Setup](#spark-jdbc-setup)
- [Define Environment Variables](#spark-jdbc-define-envir-vars)
- [Initiate a Spark JDBC Session](#spark-jdbc-init-session)
- [Load Driver Packages Dynamically](#spark-jdbc-init-dynamic-pkg-load)
- [Load Driver Packag... | github_jupyter |
```
#experiment name and snapshot folder (used for model persistence)
from __future__ import print_function
experiment_setup_name = "tutorial.wikicat.advanced"
snapshot_path = "./agentnet_snapshots/"
!mkdir ./agentnet_snapshots
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
#theano imports... | github_jupyter |
# Math Part 1
```
from __future__ import print_function
import tensorflow as tf
import numpy as np
from datetime import date
date.today()
author = "kyubyong. https://github.com/Kyubyong/tensorflow-exercises"
tf.__version__
np.__version__
sess = tf.InteractiveSession()
```
NOTE on notation
* _x, _y, _z, ...: NumPy 0-d... | github_jupyter |
```
import matplotlib as mpl
import matplotlib.pyplot as plt
age_x = [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]
dev_x = [38496, 42000, 46752, 49320, 53200,
56000, 62316, 64928, 67317, 68748, 73752]
ax = plt.bar(age_x, dev_x)
for index, value in zip(age_x, dev_x):
plt.text(index, value+5000, f'{... | github_jupyter |
# Dask pipeline
## Example: Tracking the International Space Station with Dask
In this notebook we will be using two APIs:
1. [Google Maps Geocoder](https://developers.google.com/maps/documentation/geocoding/overview)
2. [Open Notify API for ISS location](http://api.open-notify.org/)
We will use them to keep track ... | github_jupyter |
# Multi-Timescale Prediction
This notebook showcases some ways to use the **MTS-LSTM** from our recent publication to generate predictions at multiple timescales: [**"Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network"**](https://arxiv.org/abs/2010.07921).
Let's assume we h... | github_jupyter |
# Helium Hydride (Tapered HeH+) Exemplar
## Step 0: Import various libraries
```
# Imports for QSCOUT
import jaqalpaq
from jaqalpaq.core import circuitbuilder
from jaqalpaq.core.circuit import normalize_native_gates
from jaqalpaq import pygsti
from qscout.v1 import native_gates
# Imports for basic mathematical functi... | github_jupyter |
# Regularization
Welcome to the second assignment of this week. Deep Learning models have so much flexibility and capacity that **overfitting can be a serious problem**, if the training dataset is not big enough. Sure it does well on the training set, but the learned network **doesn't generalize to new examples** that... | github_jupyter |
### Global and Local Scopes
In Python the **global** scope refers to the **module** scope.
The scope of a variable is normally defined by **where** it is (lexically) defined in the code.
```
a = 10
```
In this case, **a** is defined inside the main module, so it is a global variable.
```
def my_func(n):
c = n ... | github_jupyter |
# Ungraded Lab: Walkthrough of ML Metadata
Keeping records at each stage of the project is an important aspect of machine learning pipelines. Especially in production models which involve many iterations of datasets and re-training, having these records will help in maintaining or debugging the deployed system. [ML Me... | github_jupyter |
## Exercise: Pricing a European Call Option under Risk Neutrality
#### John Stachurski
Let's price a European option under the assumption of risk neutrality (for simplicity).
Suppose that the current time is $t=0$ and the expiry date is $n$.
We need to evaluate
$$ P_0 = \beta^n \mathbb E_0 \max\{ S_n - K, 0 \} $$
... | github_jupyter |
# Weight Sampling Tutorial
If you want to fine-tune one of the trained original SSD models on your own dataset, chances are that your dataset doesn't have the same number of classes as the trained model you're trying to fine-tune.
This notebook explains a few options for how to deal with this situation. In particular... | github_jupyter |
## Build an MTH5 and Operate the Aurora Pipeline
Outlines the process of making an MTH5 file, generating a processing config, and running the aurora processor
```
# Required imports for theh program.
from pathlib import Path
import sys
import pandas as pd
from mth5.clients.make_mth5 import MakeMTH5
from mth5 import ... | github_jupyter |
```
import wandb
wandb.init(project="test")
from wandb.integration.sb3 import WandbCallback
'''
A large part of the code in this file was sourced from the rl-baselines-zoo library on GitHub.
In particular, the library provides a great parameter optimization set for the PPO2 algorithm,
as well as a great example impleme... | github_jupyter |
Objective
------------------------
Try out different hypothesis to investigate the effect of lockdown measures on mobility
- Assume that mobility is affected by weather, lockdown and miscellanous
- Consider misc. info to be one such as week info (if it is a holisday week etc...)
- Assume mobility follows a weekly pat... | github_jupyter |
# Studying avoided crossing for a 1 cavity-2 qubit system, <mark>with and without thermal losses</mark>
1. **Introduction**
2. **Problem parameters**
3. **Setting up operators, Hamiltonian's, and the initial state**
4. **Demonstrating avoided crossing**
* Plotting the ramp pulse generated
* Solving the Master... | github_jupyter |
# seaborn.jointplot
---
Seaborn's `jointplot` displays a relationship between 2 variables (bivariate) as well as 1D profiles (univariate) in the margins. This plot is a convenience class that wraps [JointGrid](http://seaborn.pydata.org/generated/seaborn.JointGrid.html#seaborn.JointGrid).
```
%matplotlib inline
import ... | github_jupyter |
# Think Bayes: Chapter 9
This notebook presents code and exercises from Think Bayes, second edition.
Copyright 2016 Allen B. Downey
MIT License: https://opensource.org/licenses/MIT
```
from __future__ import print_function, division
% matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import math... | github_jupyter |
**Documentation for getting started with ipyleaflet:**
https://ipyleaflet.readthedocs.io
**Video tutorial for this:**
https://www.youtube.com/watch?v=VW1gYD5eB6E
## Create default interactive map
```
# import the package
import ipyleaflet
# define m as a default map
m = ipyleaflet.Map()
# display map
m
```
## Cu... | github_jupyter |
```
import seaborn as sns
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
#load dataset into the notebook
data = pd.read_csv('titanic.csv')
data.head()
#get all coumns in small caps
data.columns.str.lower()
#lets look the mean of survival using gender
data.groupby('Sex')[['Survived']].mean()... | github_jupyter |
```
import numpy as np
import pandas as pd
import scipy as sp
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
sns.set()
from IPython.core.pylabtools import figsize
import statsmodels.api as sm
from patsy import dmatrix
df = pd.read_csv('~/src/properties_2016.csv')
df.tail()
train_2016_d... | github_jupyter |
# Read datasets
```
import pandas as pd
countries_of_the_world = pd.read_csv('../datasets/countries-of-the-world.csv')
countries_of_the_world.head()
mpg = pd.read_csv('../datasets/mpg.csv')
mpg.head()
student_data = pd.read_csv('../datasets/student-alcohol-consumption.csv')
student_data.head()
young_people_survey_dat... | github_jupyter |
# Preprocessing
To begin the training process, the raw images first had to be preprocessed. For the most part, this meant removing the banners that contained image metadata while retaining as much useful image data as possible. To remove the banners, I used a technique called "reflective padding" which meant I remove ... | github_jupyter |
<a href="https://colab.research.google.com/github/GavinHacker/recsys_model/blob/master/7_recbaserecall.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# 使用基于电影相似度进行推荐的方法进行召回
### install library
```
!pip install pymysql
from google.colab import dri... | github_jupyter |
# 03 - Registering a Model in your Workspace
Now that we have trained a set of models and identified the run containing the best model, we want to deploy the model for inferencing.
```
import environs
e_vars = environs.Env()
e_vars.read_env('../workshop.env')
USER_NAME = e_vars.str("USER_NAME")
EXPERIMENT_NAME = e_... | github_jupyter |
```
from datascience import *
import seaborn as sns
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import statsmodels.formula.api as smf
from matplotlib.lines import Line2D
plt.style.use('seaborn')
#Data clarification
#Rank - Current World ranking based on 4 last competitions
... | github_jupyter |
```
CLR = {
'blue': ['#e0f3ff', '#aadeff', '#2bb1ff', '#15587f', '#0b2c40'],
'gold': ['#fff3dc', '#ffebc7', '#ffddab', '#b59d79', '#5C4938'],
'red': ['#ffd8e8', '#ff9db6', '#ff3e72', '#6B404C', '#521424'],
'gray': ['#eeeeee', '#bbbbbb', '#999999', '#666666', '#333333'],
}
import pathlib
import matplot... | github_jupyter |
## Data Visualization
- Pie Chart: Compare Percentages
- Bar Chart: Compare Scores across groups
- Histogram: Show frequency of values/value range
- Line Chart: Show trend of Scores
- Scatter Plot: Show Relationship between a pair of Scores
- Map: Show Geo Distribution of data
|Type|Variable Y|Variable X|
|:--:|:--:|... | github_jupyter |
# Quantum Kernel Alignment with Qiskit Runtime
<br>
**Classification with Support Vector Machines**<br>
Classification problems are widespread in machine learning applications. Examples include credit card risk, handwriting recognition, and medical diagnosis. One approach to tackling classification problems is the su... | github_jupyter |
```
import networkx
import collections
%load_ext autoreload
%autoreload 2
from pymedphys._experimental import tree, graphviz
module_dependencies = tree.get_module_dependencies()
internal_modules = set(module_dependencies.keys())
root = 'pymedphys'
top_level_api = [item for item in module_dependencies[root] if not item... | github_jupyter |
# Evaluation metrics for classification models
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
pd.options.mode.chained_assignment = None
%matplotlib inline
```
### Back with the credit card default dataset
```
# Loading the dataset
DATA_DIR = '../data'
FILE... | github_jupyter |
```
from __future__ import print_function
from textwrap import dedent
import pytablewriter
table_name = "example_table"
headers = ["int", "float", "str", "bool", "mix", "time"]
data = [
[0, 0.1, "hoge", True, 0, "2017-01-01 03:04:05+0900"],
[2, "-2.23", "foo", False, None, "2017-12-23 12... | github_jupyter |
```
import pickle
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
with open('cdrk_lastepisode_heat.pickle', 'rb') as f:
last_heat = pickle.load(f)
with open('cdrk_heat_unique0.pickle', 'rb') as f:
heat_uniq0 = pickle.load(f)
with open('cdrk_heat_freq0.pickle', 'rb') as f:
heat_fr... | github_jupyter |
# Deterministic Inputs, Noisy “And” gate model (DINA)
This notebook will show you how to train and use the DINA.
First, we will show how to get the data (here we use Math1 from math2015 as the dataset).
Then we will show how to train a DINA and perform the parameters persistence.
At last, we will show how to load the ... | github_jupyter |
## Ejercicio 1
Dada la siguiente lista:
> ```ejer_1 = [1,2,3,4,5]```
Inviertela par que quede de la siguiente manera
> ```ejer_1 = [5,4,3,2,1]```
## Ejercicio 2
Eleva todos los elementos de la lista al cuadrado
> ```ejer_2 = [1,2,3,4,5]```
## Ejercicio 3
Crea una lista nueva con todas las combinaciones de las siguie... | github_jupyter |
# Building ERDDAP Datasets
This notebook documents the process of creating XML fragments
for nowcast system run results files
for inclusion in `/results/erddap-datasets/datasets.xml`
which is symlinked to `/opt/tomcat/content/erddap/datasets.xml`
on the `skookum` ERDDAP server instance.
The contents are a combination... | github_jupyter |
<a href="https://colab.research.google.com/github/iotanalytics/IoTTutorial/blob/main/code/detection_and_segmentation/Anomaly_Detection_with_Autoencoder_.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# **Anomaly Detection with Autoencoder**
Autoenc... | github_jupyter |
# Robot Class
In this project, we'll be localizing a robot in a 2D grid world. The basis for simultaneous localization and mapping (SLAM) is to gather information from a robot's sensors and motions over time, and then use information about measurements and motion to re-construct a map of the world.
### Uncertainty
A... | github_jupyter |
### Model features
- augmentation (6 image generated)
- 2 dropout layer
- adam optimizer with learning rate decay
```
NAME = '2dropout-augmentation'
LOAD = True
import sys
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import cv2
import random
from tqdm impor... | github_jupyter |
# 09 - Decision Trees
by [Alejandro Correa Bahnsen](albahnsen.com/)
version 0.2, May 2016
## Part of the class [Machine Learning for Risk Management](https://github.com/albahnsen/ML_RiskManagement)
This notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License](http://creativecommo... | github_jupyter |
# Location Set Covering Problem (LSCP)
*Authors:* [Germano Barcelos](https://github.com/gegen07), [James Gaboardi](https://github.com/jGaboardi), [Levi J. Wolf](https://github.com/ljwolf), [Qunshan Zhao](https://github.com/qszhao)
Location Set Covering is a problem realized by Toregas, et al. (1971). He figured out t... | github_jupyter |
```
!pip3 install tqdm
from post_processing import *
from const import ROOT
from const import *
```
vert_path = os.path.join(".", 'vertical_hamming')
vert_path1 = os.path.join(".", 'vertical_hamming_res')
vert_file_list = glob.glob(os.path.join(vert_path, '*.png'))+ glob.glob(os.path.join(vert_path1, '*.png'))
df = pd... | github_jupyter |
# Character Sequence to Sequence
In this notebook, we'll build a model that takes in a sequence of letters, and outputs a sorted version of that sequence. We'll do that using what we've learned so far about Sequence to Sequence models. This notebook was updated to work with TensorFlow 1.1 and builds on the work of Dav... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Create Image
In this notebook, we show the following steps for deploying a web service using AzureML:
- Create an image
- Test image locally
```
import pandas as pd
from utilities import text_to_json, get_auth
from azureml.co... | github_jupyter |
# Weight Initialization
In this lesson, you'll learn how to find good initial weights for a neural network. Having good initial weights can place the neural network close to the optimal solution. This allows the neural network to come to the best solution quicker.
## Testing Weights
### Dataset
To see how different w... | github_jupyter |
```
#import urllib, urllib3
#from bs4 import BeautifulSoup
#import requests
#import time
#import io
#import numpy as np
##import nltk
##from nltk.corpus import wordnet as wn
#import pandas as pd
#from sklearn.feature_extraction.text import CountVectorizer
#import gensim
#from gensim.models.ldamodel import LdaModel
```
... | github_jupyter |
# Interpretable forecasting with N-Beats
```
import os
import warnings
warnings.filterwarnings("ignore")
os.chdir("../../..")
import pandas as pd
import torch
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_forecasting import TimeSeriesDataSet, NBeats, Baseline
from... | github_jupyter |
# Create a QComponent - Advanced
```
from qiskit_metal import draw, Dict
from qiskit_metal.toolbox_metal import math_and_overrides
from qiskit_metal.qlibrary.core import QComponent
import qiskit_metal as metal
design = metal.designs.DesignPlanar()
```
## Qubits and Junctions
The vast majority of junction management ... | github_jupyter |
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