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## Node Classification on Citation Network
As a start, we present a end to end example, demonstrating how GraphScope process node classification task on citation network by combining analytics, interactive and graph neural networks computation.
In this example, we use [ogbn-mag](https://ogb.stanford.edu/docs/nodeprop... | github_jupyter |
```
from IPython.core.debugger import set_trace
import gzip
import struct
import matplotlib as mpl
import matplotlib.pyplot as plt
# pre-requirement: MNIST data files stored in local directory under $folder/mnist/
# after downloaded from http://yann.lecun.com/exdb/mnist/
class MnistInput:
def __init__(self, da... | github_jupyter |
# Resample Data
## Pandas Resample
You've learned about bucketing to different periods of time like Months. Let's see how it's done. We'll start with an example series of days.
```
import numpy as np
import pandas as pd
dates = pd.date_range('10/10/2018', periods=11, freq='D')
close_prices = np.arange(len(dates))
cl... | github_jupyter |
# Average data on group level
We average each z-scored time series, weighted with the design (rest is inverted before averaging). We then average over all patients of one group. This excludes patients deemed inconclusive by the 2D-LI method performed in the previous step.
The results of this notebook will not be use... | github_jupyter |
# Epidemilogical analysis Lab Notebook
(to edit this notebook and the associated python files, `git checkout` the corresponding commit by its hash, e.g. `git checkout 422024d`)
```
from IPython.display import display, Markdown
from datetime import datetime
cur_datetime = datetime.now()
display(Markdown(f'# {cur_datet... | github_jupyter |
# MNIST Image Classification with TensorFlow on Cloud AI Platform
This notebook demonstrates how to implement different image models on MNIST using the [tf.keras API](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras).
## Learning objectives
1. Understand how to build a Dense Neural Network (DNN) for ... | github_jupyter |
# Calibration Procedure
* Compute center offset:
- Set $\lambda_{\rm center}$ to set of known spectral lines
- Measure pixel position of each:
- average each to determine central pixel $n_o$
| $\lambda_{\rm center}$ | Pixel |
| ----------------------: |:------:|
| 0 nm | 5.2 |
... | github_jupyter |
# Distribuição Normal
Gaussiana, curva de sino
* simétrica
* média = mediana = moda
* variáveis contínuas
Ex:
* altura e peso de uma população
* tamanho do crânio de recém nascidos
* pressão sanguínea
$$ p(x|\mu,\sigma) = \frac{1}{\sqrt{2\pi\sigma^2}}\exp\left[-\frac{(x-\mu)^2}{2\sigma^2}\right] $$
```
import matp... | github_jupyter |
```
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 16})
import numpy as np
import torch
import os
import networkx as nx
co_dir = "../results/2021-09-07_21-01_dist_mnist_complete"
cy_dir = "../results/2021-09-05_14-27_dist_mnist_v3"
r3_dir = "../results/2021-09-07_20-11_dist_mnist_random3"
r8_dir = ".... | github_jupyter |
```
%matplotlib inline
```
# Pyplot tutorial
An introduction to the pyplot interface.
Intro to pyplot
===============
:mod:`matplotlib.pyplot` is a collection of functions
that make matplotlib work like MATLAB.
Each ``pyplot`` function makes
some change to a figure: e.g., creates a figure, creates a plotting area... | github_jupyter |
# Prepare and Deploy a TensorFlow Model to AI Platform for Online Serving
This Notebook demonstrates how to prepare a TensorFlow 2.x model and deploy it for serving with AI Platform Prediction. This example uses the pretrained [ResNet V2 101](https://tfhub.dev/google/imagenet/resnet_v2_101/classification/4) image clas... | github_jupyter |
<a href="https://colab.research.google.com/github/SAK90/HousingPrices/blob/main/first_project.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import os
import tarfile
from six.moves import urllib
DOWNLOAD_ROOT = "https://raw.githubusercontent.co... | github_jupyter |
# Video Classification
Video classification is one of the many tasks in the field of _video understanding_, technologies that automatically extract information from video. You can read more about the great, wide world of video understanding in our blog post [An Introduction to Video Understanding: Capabilities and App... | github_jupyter |
## Import packages
```
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import savgol_filter
import cline_analysis as ca
import pandas as pd
import seaborn as sns
import datetime
import os
from scipy.signal import medfilt
import functools
from scipy.optimize import bisect
from scipy import stats
s... | github_jupyter |
```
import numpy as np
import tensorflow as tf
from tensorflow import keras
def split_sequence(sequence, n_steps):
X, y = list(), list()
for i in range(len(sequence)):
end_ix = i + n_steps
if end_ix > len(sequence) - 1:
break
seq_x, seq_y = sequence[i:end_ix], sequence[end_ix... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
```
#Configurable parameters for pure pursuit
+ How fast do you want the robot to move? It is fixed at $v_{max}$ in this exercise
+ When can we declare the goal has been reached?
+ What is the lookahead distance? Determines the next position on ... | github_jupyter |
```
import json
json.loads('{"coef0":0}')
#%%writefile ../../src/data/data_utils.py
# %load ../../src/data/data_utils.py
# %%writefile ../../src/data/data_utils.py
"""
Author: Jim Clauwaert
Created in the scope of my PhD
"""
import pandas as pd
import numpy as np
import itertools
def CreatePairwiseRankData(dfDatase... | github_jupyter |
```
from sklearn import datasets
import pandas as pd
# Load the boston house-prices dataset (regression).
boston = datasets.load_boston()
boston_target_name = 'MEDV'
boston_features_names = boston.feature_names
boston_df = pd.DataFrame(boston.data, columns=boston_features_names)
boston_df[boston_target_name] = boston.... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta, date
from mpl_toolkits.axes_grid1 import make_axes_locatable
# from matplotlib.ticker import FuncFormatter
import geopandas as gpd
ox_data_url = r'https://ocgptweb.azurewebsites.ne... | github_jupyter |
# Kernel-based Time-varying Regression - Part III
The tutorials **I** and **II** described the **KTR** model, it's fitting procedure, and diagnostics / validation methods (visualizations of the **KTR** regression). This tutorial covers more **KTR** configurations for advanced users. In particular, it describes how t... | github_jupyter |
## Markov switching autoregression models
This notebook provides an example of the use of Markov switching models in statsmodels to replicate a number of results presented in Kim and Nelson (1999). It applies the Hamilton (1989) filter the Kim (1994) smoother.
This is tested against the Markov-switching models from E... | github_jupyter |
```
# License: BSD
# Author: Sasank Chilamkurthy
from __future__ import print_function, division
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
import numpy as np
import torchvision
from torchvision import models, transforms
imp... | github_jupyter |
# Ejemplo paso a paso
Vamos a hacer un ejercicio con datos de verdad
### 1. Importamos las librerías de Pandas y Numpy
```
import pandas as pd
import numpy as np
```
### 2. Leemos el fichero
Si es un documento online, podremos leerlo directamente, si es un fichero, tendremos que guardarlo en esta misma carpeta o,... | github_jupyter |
# Reading SETI Hackathon Data
This tutorial will show you how to programmatically download the SETI code challenge data to your local file space and
start to analyze it.
Please see the [Step_1_Get_Data.ipynb](https://github.com/setiQuest/ML4SETI/blob/master/tutorials/Step_1_Get_Data.ipynb) notebook on information ab... | github_jupyter |
<a href="https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# HuggingFace `nlp` library - Quick overview
Models come and go (linear models, LSTM, Transformers, ...... | github_jupyter |
# Kwargs optimization wrapper
TAGS: Optimization and fitting
## This ia a method to implement optimization for functions taking keywords instead of a vector (python 3 only since 2 doesn't support multiple dictionnary unpacking simultaneously)
This was mostly implemented out of the need to optimize a ml algorythm's hy... | github_jupyter |
# Querying online data with astroquery and PyVO
There are two main general packages for accessing online data from Python in the Astropy ecosystem:
* The [astroquery](https://astroquery.readthedocs.io/en/latest/) coordinated package, which offers access to many services, including a number that are not VO compatible.... | github_jupyter |
```
%pylab inline
%load_ext autoreload
%autoreload 2
import jax
import jax.numpy as jnp
import numpy as onp
import haiku as hk
from jax.experimental import optix
from nsec.datasets.two_moons import get_two_moons
from nsec.utils import display_score_two_moons
from nsec.models.dae.ardae import ARDAE
from nsec.normaliz... | github_jupyter |
```
# ls -l| tail -10
# #G4
# from google.colab import drive
# drive.mount('/content/gdrive')
# cp fingerspelling5.tar.bz2 /media/datastorage/Phong/fingerspelling5.tar.bz2
# rm fingerspelling5.tar.bz2
cd /media/datastorage/Phong/
!tar xjf fingerspelling5.tar.bz2
cd dataset5
ls -l
mkdir surrey/B
mv dataset5/* surrey/B/
... | github_jupyter |
# Git and GitHub
## Lesson Goals
- Understand the purpose of version control systems
- Create a GitHub account
- Upload your first code to GitHub
## Prequisites
- None
## Code Management
Some new considerations come up as we build larger projects that eventually go into production...
- What happens if our computer... | github_jupyter |
# Curve Fitting and Interpolation
Teng-Jui Lin
Content adapted from UW AMATH 301, Beginning Scientific Computing, in Spring 2020.
- Curve fitting
- Curve fitting using error functions and [`scipy.optimize.fmin()`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin.html)
- Sum of squ... | github_jupyter |
<a href="https://colab.research.google.com/github/GoogleCloudPlatform/training-data-analyst/blob/master/courses/fast-and-lean-data-science/02_Dataset_playground.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Imports
```
import os, math
import n... | github_jupyter |
# Sparse Approximations
The `gp.MarginalSparse` class implements sparse, or inducing point, GP approximations. It works identically to `gp.Marginal`, except it additionally requires the locations of the inducing points (denoted `Xu`), and it accepts the argument `sigma` instead of `noise` because these sparse approx... | github_jupyter |
# Simple Linear Model - OCR
By Gaetano Bonofiglio, Veronica Iovinella
## Introduction
We'll start by developing a simple linear model for classification of handwritten digits (OCR) using MNIST data-set and then plot the results. This will later be compared with a Convolutional Neural Network for the same task.
## Imp... | github_jupyter |
## Working with RDDs (ch 3)
You control a Spark process by means of a Spark Context. In Python, the `Spark context` is a built-in variable, already bound to the Context. When using Java (or Scala), you need to do the binding yourself
```
sc
```
you will use `sc` to ask Spark to load the content of a file into memory... | github_jupyter |
# t-SNE for community composition
```
# Housekeeping
library(caret)
library(gridExtra)
library(reshape2)
library(Rtsne)
library(tidyr)
# Read in data
species_composition = read.table("../../../data/amplicon/species_composition_relative_abundance.txt",
sep = "\t",
... | github_jupyter |
# Using your own object detector for detection images
<table align="left"><td>
<a target="_blank" href="https://colab.research.google.com/github/TannerGilbert/Tutorials/blob/master/Tensorflow%20Object%20Detection/object_detection_with_own_model.ipynb">
<img src="https://www.tensorflow.org/images/colab_logo_32px... | 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 |
```
#default_exp api
```
# API
> High level functions for easy interaction
This module defines the building blocks for the CLI. These functions can be leveraged to define other custom workflows more easily.
```
#export
import importlib
import inspect
from collections import defaultdict
from pathlib import Path
from... | github_jupyter |
# Perform Bayesian optimization of CrabNet hyperparameters using Ax
###### Created January 8, 2022
# Description
We use [(my fork of) CrabNet](https://github.com/sgbaird/CrabNet) to adjust various hyperparameters for the experimental band gap matbench task (`matbench_expt_gap`). We chose this task because `CrabNet` ... | github_jupyter |
# Team 6a - Final Project - Phase 3
## Title :
US and Japan YouTube trending videos
## Problem:
Our goal is to analyze US and Japan YouTube trending videos and present how the video categories and country culture correlate with the video’s popularity in 2018 and 2020.
We will analyze the characteristics, inc... | github_jupyter |
<a href="https://colab.research.google.com/github/ProfessorPatrickSlatraigh/CST2312/blob/main/CST2312_Class08.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# **CST2312 Class 08 - Dictionaries and Tuples**
updated 27-Feb-2022 by Professor Patrick ... | github_jupyter |
## Airline Passenger Volume
This model predicts the volume of airline passengers using historical data from January 1949 to December 1960, with a total of 144 observations. Even with this modest data set, surprisingly accurate predictions are possible.
This is time series data, which is well suited to Long-Short Term... | github_jupyter |
```
# Word2vec basics using tensorflow
import numpy as np
import tensorflow as tf
corpus_raw = 'He is the king . The King is royal . She is the royal queen '
corpus_raw = corpus_raw.lower()
corpus_raw
words = []
for word in corpus_raw.split():
# we can't treat '.' as a word
if word != '.':
words.append... | github_jupyter |
```
import shap
import pandas as pd
import numpy as np
import tensorflow as tf
import tensorflow.keras.backend as K
import matplotlib.pyplot as plt
plt.style.use('ggplot')
from PIL import Image
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.image import ImageDataGenerator
fro... | github_jupyter |
```
#In this tutorial, you will further explore
#the NASA Exoplanet Archive and practice making simple plots.
#To guide you, here is the tutorial from in class:
import matplotlib.pyplot as plot #import the matplotlib.pyplot module (library) as 'plot'
import pandas as pd #import the 'pandas' module (library) as pd
dat... | github_jupyter |
# Train WaveGlow Model with custom training step
## Boilerplate Import
```
import tensorflow as tf
from tensorflow.python.eager import profiler
print("GPU Available: ", tf.test.is_gpu_available())
tf.keras.backend.clear_session()
import os, sys
root_dir, _ = os.path.split(os.getcwd())
script_dir = os.path.join(root_d... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
from pandas_profiling import ProfileReport
# Set style and settings
plt.style.use('ggplot')
pd.set_option('display.max_columns', 50)
pd.set_option('display.max_rows', 15)
# Load data and set Datetime column
collisions = pd.read_csv('../data/external/Collisions.cs... | github_jupyter |
# Simple symbol plotting with text on Cartesian projection
Symbol plotting in Magics is the plotting of different types of symbols at selected locations. A symbol in this context is a number (the value at the location), a text string (given by the user) or a Magics marker.
List of all **msymbol** parameters you can... | github_jupyter |
**Parameters to reproduce the paper's results**:
* change the optimizer from SGD to Adam in `lib/models.py`,
* change the size of the vocabulary from 1000 to 10000 in `train.keep_top_words()` below.
```
%load_ext autoreload
%autoreload 2
import sys, os
sys.path.insert(0, '..')
from lib import models, graph, coarsenin... | github_jupyter |
# The $N$-body problem. Maximum: 80 pts + 25 bonus pts
## Problem 0 (Problem statement) 5 pts
Consider the $N$-body problem
$$
V({\bf y}_j) = \sum_{i=1}^N G({\bf x}_i, {\bf y}_j) q_i, \quad j=1,\dots,N,
$$
where ${\bf x}_i$ is the location of source charges and ${\bf y}_j$ is the location of receivers where the p... | github_jupyter |
<img src="images/utfsm.png" alt="" width="100px" align="right"/>
# USM Numérica
# Licencia y configuración del laboratorio
Ejecutar la siguiente celda mediante *`Ctr-S`*.
```
"""
IPython Notebook v4.0 para python 3.0
Librerías adicionales:
Contenido bajo licencia CC-BY 4.0. Código bajo licencia MIT.
(c) Sebastian F... | github_jupyter |
### This notebook trains a N2V network in the first step and then trains a 3-class U-Net for segmentation using the denoised images as input.
```
# We import all our dependencies.
import warnings
warnings.filterwarnings('ignore')
import sys
sys.path.append('../../')
from voidseg.models import Seg, SegConfig
from n2v.m... | github_jupyter |
# Mask R-CNN - Train FCN using MRCNN in Predict Mode
```
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:95% !important; }</style>"))
%matplotlib inline
%load_ext autoreload
%autoreload 2
import sys,os, pprint
pp = pprint.PrettyPrinter(indent=2, width=100)
print('Current working ... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%aimport utils_1_1
import pandas as pd
import numpy as np
import altair as alt
from altair_saver import save
import datetime
import dateutil.parser
from os.path import join
from constants_1_1 import SITE_FILE_TYPES
from utils_1_1 import (
get_site_file_paths,
get_site_fi... | github_jupyter |
```
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
```
## МНК для линейной регрессии
$\mathbf{w}=(A^TA)^{-1}(A^T\mathbf{y})$
Загрузите файл food_trucks.txt. В нём два столбца значений — количество жителей в городе и доход гру... | github_jupyter |
[](https://www.repostatus.org/#active)
[](https://badge.fury.io/py/geoshapes)
[. Here, the goal is to solve a set of simultaneous linear equations.
Jacob Albrecht, 2019
# Problem Setup
A distillation t... | github_jupyter |
# Sci-Hub coverage of referenced (cited) articles
Based on [OpenCitations](http://opencitations.net/).
```
import json
import pathlib
import pandas
with open('00.configuration.json') as read_file:
config = json.load(read_file)
path = pathlib.Path('data/doi.tsv.xz')
doi_df = pandas.read_table(path, compression='x... | github_jupyter |
<a href="https://colab.research.google.com/github/TangJiahui/6.036_Machine_Learning/blob/main/MIT_6_036_HW08_CNN.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#MIT 6.036 Fall 2020: Homework 8#
This colab notebook provides code and a framework for... | github_jupyter |
```
from pitas import power, flipper_tools
from orphics import maps as omaps
from pixell import enplot, enmap, curvedsky
import numpy as np
from cosmikyu import stats, mpi, datasets, config, utils, gan, transforms
from cosmikyu import nn as cnn
import os
from itertools import product
import healpy as hp
import matplotl... | github_jupyter |
```
from sklearn import linear_model
import glob
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append("../../")
from mfilter.types import FrequencySamples, TimeSeries, FrequencySeries, TimesSamples
# functions
# read file
def read_file():
# folder MLensing... | github_jupyter |
# Multiclass Example
This example show shows how to use `tsfresh` to extract and select useful features from timeseries in a multiclass classification example.
The underlying control of the false discovery rate (FDR) has been introduced by [Tang et al. (2020, Sec. 3.2)](https://doi.org/10.1140/epjds/s13688-020-00244-... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '2'
import sys
sys.path.insert(0, "/home/husein/parsing/self-attentive-parser/src")
sys.path.append("/home/husein/parsing/self-attentive-parser")
import tensorflow as tf
from transformers import AlbertTokenizer
from transformers import AlbertTokenizer
tokenizer = Alber... | github_jupyter |
### Setup
```
# IMPORTS & OTHER SETTINGS
%run 'settings.py'
%matplotlib inline
!pwd
from scipy.spatial import distance
from sklearn.preprocessing import QuantileTransformer
# File paths
data_path = '../data/'
pickle_path = os.path.join(data_path, 'pickles')
if not os.path.exists(data_path):
raise Exception('Hold ... | github_jupyter |
```
import pandas as pd
pd.set_option("display.max_rows", None)
pd.set_option("display.max_columns", None)
import os
import shutil
shutil.rmtree("lending-club-data")
os.mkdir("lending-club-data")
os.mkdir("lending-club-data/risk-engine")
```
# Original data
```
df = pd.read_csv("source-data/loan.csv", dtype=str, par... | github_jupyter |
##### Copyright 2021 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 |
```
from fit.datamodules.super_res import MNIST_SResFITDM, CelebA_SResFITDM
from fit.utils import convert2DFT, pol2cart
from fit.utils.tomo_utils import get_polar_rfft_coords_2D
from fit.transformers.PositionalEncoding2D import PositionalEncoding2D
from matplotlib import pyplot as plt
import torch
import numpy as np... | github_jupyter |
```
import os
import shutil
# We do not need a dataset so we load the fake input
from meta_learning.backend.tensorflow.dataset import noop as _dataset_noop
# We load the optimizers we care about.
from meta_learning.backend.tensorflow import meta_optimizer as _meta_optimizer
# The memory types we want to use.
from me... | github_jupyter |
```
import sys
import pandas
# local imports
sys.path.insert(0, '../')
import utils
```
## Read DO Slim
```
commit = '72614ade9f1cc5a5317b8f6836e1e464b31d5587'
url = utils.rawgit('dhimmel', 'disease-ontology', commit, 'data/slim-terms.tsv')
disease_df = pandas.read_table(url)
disease_df = disease_df.rename(columns=... | github_jupyter |
```
from data import load_data
clinical, _, genes, treatments, outcome = load_data()
clinical.head()
treatments.columns = [c.replace('therapy_first_line_Non-therapy',
'therapy_first_line_Non-treatment') for c in treatments.columns]
treatments.head()
```
# THERAPY SENSITIVITY MODELLI... | github_jupyter |
This workbook is a Python coding example utilizing the SAS QKB for data quality, enrichment, and entity resolution
```
# import swat (SAS Scripting Language for Analytics Transfer),
# and pandas
import swat
import pandas as pd
# Create a connection to CAS, specifying host name or url
# the SAS Viya server... | github_jupyter |
# Lecture 33: AlexNet
```
%matplotlib inline
import tqdm
import copy
import time
import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import matplotlib.pyplot as plt
import torchvision
from torchvision import transforms,datasets, models
print(torch.__versio... | github_jupyter |
# Advanced Ray Tutorial - Exercise Solutions
© 2019-2020, Anyscale. All Rights Reserved
First, import everything we'll need and start Ray:
```
import ray, time, sys
import numpy as np
sys.path.append("../..")
from util.printing import pd... | github_jupyter |
# Applying Deep Batch Active Learning to your own learning task
In this notebook, we show how our implemented batch mode deep active learning (BMDAL) methods can be applied to a custom NN. We will first change the working directory from the examples subfolder to the main folder, which is required for the imports to wo... | github_jupyter |
# Recommendation System
Student Name: Dacheng Wen (dachengw)
## Introduction
This tutorial will introduce a approach to build a simple recommendation system.
Accroding to the definition from Wikipedia, recommendation system is a subclass of information filtering system that seek to predict the "rating" or "preference... | github_jupyter |
<!-- dom:TITLE: Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis -->
# Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis
<!-- dom:AUTHOR: Morten Hjorth-Jensen at Department of Physics, University of Oslo & Department of Physics and Ast... | github_jupyter |
##FlightPredict Package management
Run these cells only when you need to install the package or update it. Otherwise go directly the next section
1. !pip show flightPredict: provides information about the flightPredict package
2. !pip uninstall --yes flightPredict: uninstall the flight predict package. Run before insta... | github_jupyter |
```
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import pandas as pd
from Bio import Entrez
from Rules_Class import Rules
import functions as fn
from sklearn.metrics import confusion_matrix
import os
import sys
start=0
end=10
input_directory=os.path.realpath('../Data')
result_directory=os.path.realpath('../Data')
lis... | github_jupyter |
# Counting Objects: Part III
In our [last notebook](https://github.com/JoshVarty/ImageClassification/blob/master/3_CountingAgain.ipynb) we saw that with enough data and sensible transforms we can train convolutional neural networks to classify pictures according to the number of cirlces in them. Even after adding circ... | github_jupyter |
<table width = "100%">
<tr style="background-color:white;">
<!-- QWorld Logo -->
<td style="text-align:left; padding:0px; width:200px;">
<img src="images/QWorld.png"> </td>
<!-- Padding -->
<td width="*">     </td>
<td style="padding:0px;wi... | github_jupyter |
# Latent Semantic Indexing
Here, we apply the technique *Latent Semantic Indexing* to capture the similarity of words. We are given a list of words and their frequencies in 9 documents, found on [GitHub](https://github.com/ppham27/MLaPP-solutions/blob/master/chap12/lsiDocuments.pdf).
```
%matplotlib inline
import nu... | github_jupyter |
# Gluon example with DALI
## Overview
This is a modified [DCGAN example](https://gluon.mxnet.io/chapter14_generative-adversarial-networks/dcgan.html), which uses DALI for reading and augmenting images.
## Sample
```
import os.path
import matplotlib as mpl
import tarfile
import matplotlib.image as mpimg
from matplot... | github_jupyter |
**Chapter 7 – Ensemble Learning and Random Forests**
_This notebook contains all the sample code and solutions to the exercises in chapter 7._
<table align="left">
<td>
<a target="_blank" href="https://colab.research.google.com/github/ageron/handson-ml2/blob/master/07_ensemble_learning_and_random_forests.ipynb"... | github_jupyter |
```
Copyright 2020 The IREE Authors
Licensed under the Apache License v2.0 with LLVM Exceptions.
See https://llvm.org/LICENSE.txt for license information.
SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
```
# Training and Executing an MNIST Model with IREE
## Overview
This notebook covers installing IREE an... | github_jupyter |
# `pretty_midi` tutorial
This tutorial goes over the functionality of [pretty_midi](http://github.com/craffel/pretty_midi), a Python library for creating, manipulating, and extracting information from MIDI files. For more information, check [the docs](http://craffel.github.io/pretty-midi/).
```
import pretty_midi
im... | github_jupyter |
# Multivariate Joint Use Case (Single DataFrameCase)
In this vignette a use case of the Multivariate Channel Entropy Triangle is presented. We are going to evaluate the effectiveness of feature transformation using PCA in entropic terms.
### Importing Libraries
We import the package entropytriangle, which will impor... | github_jupyter |
# Reconstruction de synonymes - énoncé
Ce notebook est plus un jeu. On récupère d'abord des synonymes via la base [WOLF](http://alpage.inria.fr/~sagot/wolf-en.html). On ne garde que les synonymes composé d'un seul mot. On prend ensuite un texte quelconque qu'on découpe en phrase. Pour chaque phrase qu'on rencontre, on... | github_jupyter |
```
# 파이썬은 요렇게 샾으로 주석을 만듬
print("Hello Python")
print('Hello Python')
# 보는 바와 같이 쌍따옴표나 홑따옴표 구별이 없음
num = 1
Num = 10
print(num, Num)
# Shift + Enter: 실행
# dd: 셀 삭제
# b: 아래쪽 셀 생성
z = 3 - 4j
print(type(z))
print(z.imag)
print(z.real)
print(z.conjugate())
# / 는 일반적인 나누기
# // 는 나머지를 버림 (몫만 취함)
num1 = 3 // 7
num2 = 3333 // ... | github_jupyter |
# 1. Python performance
<br><br><br><br><br>
## Python is now mainstream
Below is an analysis of GitHub repos created by CMS physicists (i.e. "everyone who forked cms-sw/cmssw").
GitHub labels these repos as C/C++, Python, or Jupyter: the Python and Jupyter categories are now the most common.
<img src="img/lhlhc-g... | github_jupyter |
```
! conda install geopandas -qy
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import geopandas as gpd
from shapely.geometry import Point, Polygon
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection im... | github_jupyter |
# Self-Driving Car Engineer Nanodegree
## Deep Learning
## Project: Build a Traffic Sign Recognition Classifier
In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included i... | github_jupyter |
# Pairs Trading with Machine Learning
Jonathan Larkin
August, 2017
In developing a Pairs Trading strategy, finding valid, eligible pairs which exhibit unconditional mean-reverting behavior is of critical importance. This notebook walks through an example implementation of finding eligible pairs. We show how popular a... | github_jupyter |
# Equilibrium Properties and Partial Ordering (Al-Fe and Al-Ni)
```
# Only needed in a Jupyter Notebook
%matplotlib inline
# Optional plot styling
import matplotlib
matplotlib.style.use('bmh')
import matplotlib.pyplot as plt
from pycalphad import equilibrium
from pycalphad import Database, Model
import pycalphad.varia... | github_jupyter |
```
%matplotlib notebook
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import csv
import sklearn.feature_extraction.text
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
import itertools
from sklearn import preprocessing
im... | github_jupyter |
# `Практикум по программированию на языке Python`
<br>
## `Занятие 9: Web-разработка на Python`
<br><br>
### `Роман Ищенко (roman.ischenko@gmail.com)`
#### `Москва, 2021`
```
import warnings
warnings.filterwarnings('ignore')
```
### `HTTP`
HTTP (HyperText Transfer Protocol) — это протокол, позволяющий получать ра... | github_jupyter |
# HEART DISEASE PREDICTION
# OVERVIEW :
## PREDICTING HEART DISESES BASED ON TARGET VARIABLE
# IMPORTING THE DATASET AND REQ LIB
```
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import rcParams
from matplotlib.cm import rainbow
%matplotlib inline
import warnings
warnings... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
import numpy as np
import pandas as pd
import torch
import torchvision
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from matp... | github_jupyter |
```
import networkx as nx
import numpy as np
import pandas as pd
import random
import scipy.stats
import matplotlib.pyplot as plt
from sklearn.neural_network import MLPClassifier
from sklearn import svm
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, roc_curve, auc
from sklearn.model_select... | github_jupyter |
```
from __future__ import print_function
from imp import reload
```
## UAT for NbAgg backend.
The first line simply reloads matplotlib, uses the nbagg backend and then reloads the backend, just to ensure we have the latest modification to the backend code. Note: The underlying JavaScript will not be updated by this ... | github_jupyter |
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