text stringlengths 2.5k 6.39M | kind stringclasses 3
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|---|---|
```
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rcParams
import seaborn as sns
%matplotlib inline
rcParams['figure.figsize'] = 10, 8
sns.set_style('whitegrid')
num = 50
xv = np.linspace(-500,400,num)
yv = np.linspace(-500,400,num)
X,Y = np.meshgrid(xv,yv)
# frist X,Y
a = 8.2
intervalo... | github_jupyter |
```
from egocom import audio
from egocom.multi_array_alignment import gaussian_kernel
from egocom.transcription import async_srt_format_timestamp
from scipy.io import wavfile
import os
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score
from egocom.transcription import write_subtitles
def ... | github_jupyter |
# Running and Plotting Coeval Cubes
The aim of this tutorial is to introduce you to how `21cmFAST` does the most basic operations: producing single coeval cubes, and visually verifying them. It is a great place to get started with `21cmFAST`.
```
%matplotlib inline
import matplotlib.pyplot as plt
import os
# We chang... | github_jupyter |
# Determinant Quantum Monte Carlo
## 1 Hubbard model
The Hubbard model is defined as
\begin{align}
\label{eq:ham} \tag{1}
H &= -\sum_{ij\sigma} t_{ij} \left( \hat{c}_{i\sigma}^\dagger \hat{c}_{j\sigma} + hc \right)
+ \sum_{i\sigma} (\varepsilon_i - \mu) \hat{n}_{i\sigma}
+ U \sum_{i} \left( \hat{n}... | github_jupyter |
```
import os.path
from collections import Counter
from glob import glob
import inspect
import os
import pickle
import sys
from cltk.corpus.latin.phi5_index import PHI5_INDEX
from cltk.corpus.readers import get_corpus_reader
from cltk.stem.latin.j_v import JVReplacer
from cltk.stem.lemma import LemmaReplacer
from cltk... | github_jupyter |
```
from __future__ import print_function
import numpy as np
np.random.seed(1337) # for reproducibility
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils i... | github_jupyter |
# Compute forcing for 1%CO2 data
```
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
filedir1 = '/Users/hege-beatefredriksen/OneDrive - UiT Office 365/Data/CMIP5_globalaverages/Forcingpaperdata'
storedata = False # store anomalies in file?
storeforcingdata = False
createnewfile = Fals... | github_jupyter |
# Bayesian Parametric Regression
Notebook version: 1.5 (Sep 24, 2019)
Author: Jerónimo Arenas García (jarenas@tsc.uc3m.es)
Jesús Cid-Sueiro (jesus.cid@uc3m.es)
Changes: v.1.0 - First version
v.1.1 - ML Model selection included
v.1.2 - Some typos corrected
... | github_jupyter |
# Goals
### 1. Learn to implement Resnet V2 Block (Type - 1) using monk
- Monk's Keras
- Monk's Pytorch
- Monk's Mxnet
### 2. Use network Monk's debugger to create complex blocks
### 3. Understand how syntactically different it is to implement the same using
- Traditional Keras
- Traditiona... | github_jupyter |
```
from misc import HP
import argparse
import random
import time
import pickle
import copy
import SYCLOP_env as syc
from misc import *
import sys
import os
import cv2
import argparse
import tensorflow.keras as keras
from keras_networks import rnn_model_102, rnn_model_multicore_201, rnn_model_multicore_202
from curric... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
```
# 1.
## a)
```
def simetrica(A):
"Verifică dacă matricea A este simetrică"
return np.all(A == A.T)
def pozitiv_definita(A):
"Verifică dacă matricea A este pozitiv definită"
for i in range(1, len(A) + 1):
d_minor = np.linalg.det(A[:i... | github_jupyter |
# Input data representation as 2D array of 3D blocks
> An easy way to represent input data to neural networks or any other machine learning algorithm in the form of 2D array of 3D-blocks
- toc: false
- branch: master
- badges: true
- comments: true
- categories: [machine learning, jupyter, graphviz]
- image: images/ar... | github_jupyter |
# Visualize Counts for the three classes
The number of volume-wise predictions for each of the three classes can be visualized in a 2D-space (with two classes as the axes and the remained or class1-class2 as the value of the third class). Also, the percentage of volume-wise predictions can be shown in a modified pie... | github_jupyter |
# Soft Computing
## Vežba 1 - Digitalna slika, computer vision, OpenCV
### OpenCV
Open source biblioteka namenjena oblasti računarske vizije (eng. computer vision). Dokumentacija dostupna <a href="https://opencv.org/">ovde</a>.
### matplotlib
Plotting biblioteka za programski jezik Python i njegov numerički paket ... | github_jupyter |
## <center>Ensemble models from machine learning: an example of wave runup and coastal dune erosion</center>
### <center>Tomas Beuzen<sup>1</sup>, Evan B. Goldstein<sup>2</sup>, Kristen D. Splinter<sup>1</sup></center>
<center><sup>1</sup>Water Research Laboratory, School of Civil and Environmental Engineering, UNSW Sy... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = ''
# !git pull
import tensorflow as tf
import malaya_speech
import malaya_speech.train
from malaya_speech.train.model import fastspeech2
import numpy as np
_pad = 'pad'
_start = 'start'
_eos = 'eos'
_punctuation = "!'(),.:;? "
_special = '-'
_letters = 'ABCDEFGHIJKLMN... | github_jupyter |
# Building and using data schemas for computer vision
This tutorial illustrates how to use raymon profiling to guard image quality in your production system. The image data is taken from [Kaggle](https://www.kaggle.com/ravirajsinh45/real-life-industrial-dataset-of-casting-product) and is courtesy of PILOT TECHNOCAST, S... | github_jupyter |
```
pip install pandas
pip install numpy
pip install sklearn
pip install matplotlib
from sklearn import cluster
from sklearn.cluster import KMeans
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
df = pd.read_csv("sample_stocks.csv")
df
df.describe()
df.head()
df.info()
# x = df['returns']
# ... | github_jupyter |
```
import numpy as np
import sklearn
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
# Load the Boston Housing Dataset from sklearn
from sklearn.datasets import load_boston
boston_dataset = load_boston()
print(boston_dataset.keys())
print(boston_dataset.DESCR)
# Create t... | github_jupyter |
```
from IPython.core.display import display, HTML
import pandas as pd
import numpy as np
import copy
import os
%load_ext autoreload
%autoreload 2
import sys
sys.path.insert(0,"/local/rankability_toolbox")
PATH_TO_RANKLIB='/local/ranklib'
from numpy import ix_
import numpy as np
D = np.loadtxt(PATH_TO_RANKLIB+"/prob... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.metrics import mean_squared_error, accuracy_score, f1_score, r2_score, explained_variance_score, roc_auc_score
from sklearn.preprocessing import MinMaxScale... | github_jupyter |
<a href="https://qworld.net" target="_blank" align="left"><img src="../qworld/images/header.jpg" align="left"></a>
$ \newcommand{\bra}[1]{\langle #1|} $
$ \newcommand{\ket}[1]{|#1\rangle} $
$ \newcommand{\braket}[2]{\langle #1|#2\rangle} $
$ \newcommand{\dot}[2]{ #1 \cdot #2} $
$ \newcommand{\biginner}[2]{\left\langle... | github_jupyter |
# SQLAlchemy Homework - Surfs Up!
### Before You Begin
1. Create a new repository for this project called `sqlalchemy-challenge`. **Do not add this homework to an existing repository**.
2. Clone the new repository to your computer.
3. Add your Jupyter notebook and `app.py` to this folder. These will be the main scr... | github_jupyter |
# Maximum Likelihood Estimation (Generic models)
This tutorial explains how to quickly implement new maximum likelihood models in `statsmodels`. We give two examples:
1. Probit model for binary dependent variables
2. Negative binomial model for count data
The `GenericLikelihoodModel` class eases the process by prov... | github_jupyter |
```
#Download the dataset from opensig
import urllib.request
urllib.request.urlretrieve('http://opendata.deepsig.io/datasets/2016.10/RML2016.10a.tar.bz2', 'RML2016.10a.tar.bz2')
#decompress the .bz2 file into .tar file
import sys
import os
import bz2
zipfile = bz2.BZ2File('./RML2016.10a.tar.bz2') # open the file
data ... | github_jupyter |
<a href="https://colab.research.google.com/github/lvisdd/object_detection_tutorial/blob/master/object_detection_face_detector.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
# restart (or reset) your virtual machine
#!kill -9 -1
```
# [Tensorfl... | github_jupyter |
[Index](Index.ipynb) - [Next](Widget List.ipynb)
# Simple Widget Introduction
## What are widgets?
Widgets are eventful python objects that have a representation in the browser, often as a control like a slider, textbox, etc.
## What can they be used for?
You can use widgets to build **interactive GUIs** for your ... | github_jupyter |
```
import numpy as np
from pandas import Series, DataFrame
import pandas as pd
from sklearn import preprocessing, tree
from sklearn.metrics import accuracy_score
# from sklearn.model_selection import train_test_split, KFold
from sklearn.neighbors import KNeighborsClassifier
from sklearn.cross_validation import KFold
d... | github_jupyter |
# `Практикум по программированию на языке Python`
<br>
## `Занятие 2: Пользовательские и встроенные функции, итераторы и генераторы`
<br><br>
### `Мурат Апишев (mel-lain@yandex.ru)`
#### `Москва, 2021`
### `Функции range и enumerate`
```
r = range(2, 10, 3)
print(type(r))
for e in r:
print(e, end=' ')
for ind... | github_jupyter |
```
%pylab inline
import re
from pathlib import Path
import pandas as pd
import seaborn as sns
datdir = Path('data')
figdir = Path('plots')
figdir.mkdir(exist_ok=True)
mpl.rcParams.update({'figure.figsize': (2.5,1.75), 'figure.dpi': 300,
'axes.spines.right': False, 'axes.spines.top': False,
... | github_jupyter |
# Running the Direct Fidelity Estimation (DFE) algorithm
This example walks through the steps of running the direct fidelity estimation (DFE) algorithm as described in these two papers:
* Direct Fidelity Estimation from Few Pauli Measurements (https://arxiv.org/abs/1104.4695)
* Practical characterization of quantum ... | github_jupyter |
# Gujarati with CLTK
See how you can analyse your Gujarati texts with <b>CLTK</b> ! <br>
Let's begin by adding the `USER_PATH`..
```
import os
USER_PATH = os.path.expanduser('~')
```
In order to be able to download Gujarati texts from CLTK's Github repo, we will require an importer.
```
from cltk.corpus.utils.impor... | github_jupyter |
<h1>CREAZIONE MODELLO SARIMA REGIONE SARDEGNA
```
import pandas as pd
df = pd.read_csv('../../csv/regioni/sardegna.csv')
df.head()
df['DATA'] = pd.to_datetime(df['DATA'])
df.info()
df=df.set_index('DATA')
df.head()
```
<h3>Creazione serie storica dei decessi totali della regione Sardegna
```
ts = df.TOTALE
ts.head()... | github_jupyter |
# Logistic Regression on 'HEART DISEASE' Dataset
Elif Cansu YILDIZ
```
from pyspark.sql import SparkSession
from pyspark.sql.types import *
from pyspark.sql.functions import col, countDistinct
from pyspark.ml.feature import OneHotEncoderEstimator, StringIndexer, VectorAssembler, MinMaxScaler, IndexToString
from pysp... | github_jupyter |
# Recommending Movies: Retrieval
Real-world recommender systems are often composed of two stages:
1. The retrieval stage is responsible for selecting an initial set of hundreds of candidates from all possible candidates. The main objective of this model is to efficiently weed out all candidates that the user is not i... | github_jupyter |
##### Copyright 2018 The TF-Agents 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 a... | github_jupyter |
```
import pandas as pd
import numpy as np
from tqdm import tqdm
tqdm.pandas()
import os, time, datetime
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score, f1_score, roc_curve, auc
import lightgbm as lgb
import xgboost as xgb
def format_time(elapsed):
'''
Takes a ti... | github_jupyter |
# Example usage of the O-C tools
## This example shows how to construct and fit with MCMC the O-C diagram of the RR Lyrae star OGLE-BLG-RRLYR-02950
### We start with importing some libraries
```
import numpy as np
import oc_tools as octs
```
### We read in the data, set the period used to construct the O-C diagram ... | github_jupyter |
# Consensus Optimization
This notebook contains the code for the toy experiment in the paper [The Numerics of GANs](https://arxiv.org/abs/1705.10461).
```
%load_ext autoreload
%autoreload 2
import tensorflow as tf
from tensorflow.contrib import slim
import numpy as np
import scipy as sp
from scipy import stats
from m... | github_jupyter |
<a href="https://colab.research.google.com/github/bhuwanupadhyay/codes/blob/main/ipynbs/reshape_demo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
pip install pydicom
# Import tensorflow
import logging
import tensorflow as tf
import keras.bac... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
EXPERIMENT = 'bivariate_power'
TAG = ''
df = pd.read_csv(f'./results/{EXPERIMENT}_results{TAG}.csv', sep=', ', engine='python')
plot_df = df
x_var_rename_dict = {
'sample_size': '# Samples',
'Number of environments... | github_jupyter |
# This Notebook uses a Session Event Dataset from E-Commerce Website (https://www.kaggle.com/mkechinov/ecommerce-behavior-data-from-multi-category-store and https://rees46.com/) to build an Outlier Detection based on an Autoencoder.
```
import mlflow
import numpy as np
import os
import shutil
import pandas as pd
impor... | github_jupyter |
<a href="https://colab.research.google.com/github/rwarnung/datacrunch-notebooks/blob/master/dcrunch_R_example.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
**Data crunch example R script**
---
author: sweet-richard
date: Jan 30, 2022
require... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Datasets/Terrain/srtm_mtpi.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" h... | github_jupyter |
#Introduction to Data Science
See [Lesson 1](https://www.udacity.com/course/intro-to-data-analysis--ud170)
You should run it in local Jupyter env as this notebook refers to local dataset
```
import unicodecsv
from datetime import datetime as dt
enrollments_filename = 'dataset/enrollments.csv'
engagement_filename = ... | github_jupyter |
```
import pandas as pd
import numpy as np
import keras
from keras.models import Sequential,Model
from keras.layers import Dense, Dropout,BatchNormalization,Input
from keras.optimizers import RMSprop
from keras.regularizers import l2,l1
from keras.optimizers import Adam
from sklearn.model_selection import LeaveOneOut
... | github_jupyter |
# 2 Dead reckoning
*Dead reckoning* is a means of navigation that does not rely on external observations. Instead, a robot’s position is estimated by summing its incremental movements relative to a known starting point.
Estimates of the distance traversed are usually obtained from measuring how many times the wheels ... | github_jupyter |
# Computer Vision Nanodegree
## Project: Image Captioning
---
In this notebook, you will use your trained model to generate captions for images in the test dataset.
This notebook **will be graded**.
Feel free to use the links below to navigate the notebook:
- [Step 1](#step1): Get Data Loader for Test Dataset
-... | github_jupyter |
```
# TensorFlow pix2pix implementation
from __future__ import absolute_import, division, print_function, unicode_literals
try:
# %tensorflow_version only exists in Colab.
%tensorflow_version 2.x
except Exception:
pass
import tensorflow as tf
import os
import time
from matplotlib import pyplot as plt
from IPyt... | github_jupyter |
# Basic Workflow
```
# Always have your imports at the top
import pandas as pd
from sklearn.pipeline import make_pipeline
from sklearn.impute import SimpleImputer
from sklearn.ensemble import RandomForestClassifier
from sklearn.base import TransformerMixin
from hashlib import sha1 # just for grading purposes
import j... | github_jupyter |
# Lab 11: MLP -- exercise
# Understanding the training loop
```
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from random import randint
import utils
```
### Download the data and print the sizes
```
train_data=torch.load('../data/fashion-mnist/train_data.pt')
print... | github_jupyter |
## Main points
* Solution should be reasonably simple because the contest is only 24 hours long
* Metric is based on the prediction of clicked pictures one week ahead, so clicks are the most important information
* More recent information is more important
* Only pictures that were shown to a user could be clicked, so... | github_jupyter |
This challenge implements an instantiation of OTR based on AES block cipher with modified version 1.0. OTR, which stands for Offset Two-Round, is a blockcipher mode of operation to realize an authenticated encryption with associated data (see [[1]](#1)). AES-OTR algorithm is a campaign of CAESAR competition, it has suc... | github_jupyter |
```
import re
import os
import keras.backend as K
import numpy as np
import pandas as pd
from keras import layers, models, utils
import json
def reset_everything():
import tensorflow as tf
%reset -f in out dhist
tf.reset_default_graph()
K.set_session(tf.InteractiveSession())
# Constants for our networks... | github_jupyter |
# AWS Marketplace Product Usage Demonstration - Algorithms
## Using Algorithm ARN with Amazon SageMaker APIs
This sample notebook demonstrates two new functionalities added to Amazon SageMaker:
1. Using an Algorithm ARN to run training jobs and use that result for inference
2. Using an AWS Marketplace product ARN - w... | github_jupyter |
# Logistic Regression with a Neural Network mindset
Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning.... | github_jupyter |
# Azure ML Training Pipeline for COVID-CXR
This notebook defines an Azure machine learning pipeline for a single training run and submits the pipeline as an experiment to be run on an Azure virtual machine.
```
# Import statements
import azureml.core
from azureml.core import Experiment
from azureml.core import Workspa... | github_jupyter |
# SIT742: Modern Data Science
**(Week 01: Programming Python)**
---
- Materials in this module include resources collected from various open-source online repositories.
- You are free to use, change and distribute this package.
- If you found any issue/bug for this document, please submit an issue at [tulip-lab/sit74... | github_jupyter |
# General Equilibrium
This notebook illustrates **how to solve GE equilibrium models**. The example is a simple one-asset model without nominal rigidities.
The notebook shows how to:
1. Solve for the **stationary equilibrium**.
2. Solve for (non-linear) **transition paths** using a relaxtion algorithm.
3. Solve for ... | github_jupyter |
```
import numpy as np
import scipy as sp
import scipy.interpolate
import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats
import scipy.optimize
from scipy.optimize import curve_fit
import minkowskitools as mt
import importlib
importlib.reload(mt)
n=4000
rand_points = np.random.uniform(size=(2, n-2))
... | github_jupyter |
# Generative Adversarial Networks
Throughout most of this book, we've talked about how to make predictions.
In some form or another, we used deep neural networks learned mappings from data points to labels.
This kind of learning is called discriminative learning,
as in, we'd like to be able to discriminate between ph... | github_jupyter |
*This notebook is part of course materials for CS 345: Machine Learning Foundations and Practice at Colorado State University.
Original versions were created by Asa Ben-Hur.
The content is availabe [on GitHub](https://github.com/asabenhur/CS345).*
*The text is released under the [CC BY-SA license](https://creativecom... | github_jupyter |
# Lecture 3.3: Anomaly Detection
[**Lecture Slides**](https://docs.google.com/presentation/d/1_0Z5Pc5yHA8MyEBE8Fedq44a-DcNPoQM1WhJN93p-TI/edit?usp=sharing)
This lecture, we are going to use gaussian distributions to detect anomalies in our emoji faces dataset
**Learning goals:**
- Introduce an anomaly detection pro... | github_jupyter |
# Import Necessary Libraries
```
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn import svm
from sklearn.metrics import precision_score, recall_score
# display images
from IPy... | github_jupyter |
# 第8章: ニューラルネット
第6章で取り組んだニュース記事のカテゴリ分類を題材として，ニューラルネットワークでカテゴリ分類モデルを実装する．なお，この章ではPyTorch, TensorFlow, Chainerなどの機械学習プラットフォームを活用せよ．
## 70. 単語ベクトルの和による特徴量
***
問題50で構築した学習データ，検証データ，評価データを行列・ベクトルに変換したい．例えば，学習データについて，すべての事例$x_i$の特徴ベクトル$\boldsymbol{x}_i$を並べた行列$X$と正解ラベルを並べた行列（ベクトル）$Y$を作成したい．
$$
X = \begin{pmatrix}
\boldsy... | github_jupyter |
# Analyse a series
<div class="alert alert-block alert-warning">
<b>Under construction</b>
</div>
```
import os
import pandas as pd
from IPython.display import Image as DImage
from IPython.core.display import display, HTML
import series_details
# Plotly helps us make pretty charts
import plotly.offline as py
imp... | github_jupyter |
# SLU07 - Regression with Linear Regression: Example notebook
# 1 - Writing linear models
In this section you have a few examples on how to implement simple and multiple linear models.
Let's start by implementing the following:
$$y = 1.25 + 5x$$
```
def first_linear_model(x):
"""
Implements y = 1.25 + 5*x
... | github_jupyter |
```
#importing libraries
import pandas as pd
import boto3
import json
import configparser
from botocore.exceptions import ClientError
import psycopg2
def config_parse_file():
"""
Parse the dwh.cfg configuration file
:return:
"""
global KEY, SECRET, DWH_CLUSTER_TYPE, DWH_NUM_NODES, \
DWH_NOD... | github_jupyter |
# Task 4: Support Vector Machines
_All credit for the code examples of this notebook goes to the book "Hands-On Machine Learning with Scikit-Learn & TensorFlow" by A. Geron. Modifications were made and text was added by K. Zoch in preparation for the hands-on sessions._
# Setup
First, import a few common modules, en... | github_jupyter |
# Create TensorFlow Deep Neural Network Model
**Learning Objective**
- Create a DNN model using the high-level Estimator API
## Introduction
We'll begin by modeling our data using a Deep Neural Network. To achieve this we will use the high-level Estimator API in Tensorflow. Have a look at the various models availab... | github_jupyter |
# Compare different DEMs for individual glaciers
For most glaciers in the world there are several digital elevation models (DEM) which cover the respective glacier. In OGGM we have currently implemented 10 different open access DEMs to choose from. Some are regional and only available in certain areas (e.g. Greenland ... | github_jupyter |
Created from https://github.com/awslabs/amazon-sagemaker-examples/blob/master/introduction_to_amazon_algorithms/random_cut_forest/random_cut_forest.ipynb
```
import boto3
import botocore
import sagemaker
import sys
bucket = 'tdk-awsml-sagemaker-data.io-dev' # <--- specify a bucket you have access to
prefix = ''
ex... | github_jupyter |
<br>
# Analysis of Big Earth Data with Jupyter Notebooks
<img src='./img/opengeohub_logo.png' alt='OpenGeoHub Logo' align='right' width='25%'></img>
Lecture given for OpenGeoHub summer school 2020<br>
Tuesday, 18. August 2020 | 11:00-13:00 CEST
#### Lecturer
* [Julia Wagemann](https://jwagemann.com) | Independent c... | github_jupyter |
```
import pandas as pd
import numpy as np
from tools import acc_score
df_train = pd.read_csv("../data/train.csv", index_col=0)
df_test = pd.read_csv("../data/test.csv", index_col=0)
train_bins = seq_to_num(df_train.Sequence, target_split=True, pad=True, pad_adaptive=True,
pad_maxlen=100, dtype=... | github_jupyter |
# 📃 Solution of Exercise M6.01
The aim of this notebook is to investigate if we can tune the hyperparameters
of a bagging regressor and evaluate the gain obtained.
We will load the California housing dataset and split it into a training and
a testing set.
```
from sklearn.datasets import fetch_california_housing
fr... | github_jupyter |
## Recommendations with MovieTweetings: Collaborative Filtering
One of the most popular methods for making recommendations is **collaborative filtering**. In collaborative filtering, you are using the collaboration of user-item recommendations to assist in making new recommendations.
There are two main methods of ... | github_jupyter |
# Figure 4: NIRCam Grism + Filter Sensitivities ($1^{st}$ order)
***
### Table of Contents
1. [Information](#Information)
2. [Imports](#Imports)
3. [Data](#Data)
4. [Generate the First Order Grism + Filter Sensitivity Plot](#Generate-the-First-Order-Grism-+-Filter-Sensitivity-Plot)
5. [Issues](#Issues)
6. [About this... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sb
import gc
prop_data = pd.read_csv("properties_2017.csv")
# prop_data
train_data = pd.read_csv("train_2017.csv")
train_data
# missing_val = prop_data.isnull().sum().reset_index()
# missing_val.columns = ['column_name', 'missi... | github_jupyter |
```
from IPython.core.display import HTML
with open('../style.css', 'r') as file:
css = file.read()
HTML(css)
```
# A Crypto-Arithmetic Puzzle
In this exercise we will solve the crypto-arithmetic puzzle shown in the picture below:
<img src="send-more-money.png">
The idea is that the letters
"$\texttt{S}$", "$\t... | github_jupyter |
# Solution based on Multiple Models
```
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
```
# Tokenize and Numerize - Make it ready
```
training_size = 20000
training_sentences = sent... | github_jupyter |
# Tutorial on Python for scientific computing
Marcos Duarte
This tutorial is a short introduction to programming and a demonstration of the basic features of Python for scientific computing. To use Python for scientific computing we need the Python program itself with its main modules and specific packages for scient... | github_jupyter |
```
import numpy as np
import pandas as pd
import json as json
from scipy import stats
from statsmodels.formula.api import ols
import matplotlib.pyplot as plt
from scipy.signal import savgol_filter
from o_plot import opl # a small local package dedicated to this project
# Prepare the data
# loading the data
file_name =... | github_jupyter |
# **OPTICS Algorithm**
Ordering Points to Identify the Clustering Structure (OPTICS) is a Clustering Algorithm which locates region of high density that are seperated from one another by regions of low density.
For using this library in Python this comes under Scikit Learn Library.
## Parameters:
**Reachability Dis... | github_jupyter |
# Chapter 7. 텍스트 문서의 범주화 - (4) IMDB 전체 데이터로 전이학습
- 앞선 전이학습 실습과는 달리, IMDB 영화리뷰 데이터셋 전체를 사용하며 문장 수는 10개 -> 20개로 조정한다
- IMDB 영화 리뷰 데이터를 다운로드 받아 data 디렉토리에 압축 해제한다
- 다운로드 : http://ai.stanford.edu/~amaas/data/sentiment/
- 저장경로 : data/aclImdb
```
import os
import config
from dataloader.loader import Loader
from pre... | github_jupyter |
```
import pandas as pd
import numpy as np
data = np.array([1,2,3,4,5,6])
name = np.array(['' for x in range(6)])
besio = np.array(['' for x in range(6)])
entity = besio
columns = ['name/doi', 'data', 'BESIO', 'entity']
df = pd.DataFrame(np.array([name, data, besio, entity]).transpose(), columns=columns)
df.iloc[1,0] =... | github_jupyter |
# CTR预估(1)
资料&&代码整理by[@寒小阳](https://blog.csdn.net/han_xiaoyang)(hanxiaoyang.ml@gmail.com)
reference：
* [《广告点击率预估是怎么回事？》](https://zhuanlan.zhihu.com/p/23499698)
* [从ctr预估问题看看f(x)设计—DNN篇](https://zhuanlan.zhihu.com/p/28202287)
* [Atomu2014 product_nets](https://github.com/Atomu2014/product-nets)
关于CTR预估的背景推荐大家看欧阳辰老师在知... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import os
destdir = '/Users/argha/Dropbox/CS/DatSci/nyc-data'
files = [ f for f in os.listdir(destdir) if os.path.isfile(os.path.jo... | github_jupyter |
```
import os
from tensorflow.keras import layers
from tensorflow.keras import Model
from tensorflow.keras.applications.inception_v3 import InceptionV3
#!wget --no-check-certificate \
# https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5 \
# -O /tmp/inception_v... | github_jupyter |
CER041 - Install signed Knox certificate
========================================
This notebook installs into the Big Data Cluster the certificate signed
using:
- [CER031 - Sign Knox certificate with generated
CA](../cert-management/cer031-sign-knox-generated-cert.ipynb)
Steps
-----
### Parameters
```
app_na... | github_jupyter |
```
!pip install -q condacolab
import condacolab
condacolab.install()
!conda install -c chembl chembl_structure_pipeline
import chembl_structure_pipeline
from chembl_structure_pipeline import standardizer
from IPython.display import clear_output
# https://www.dgl.ai/pages/start.html
# !pip install dgl
!pip install dg... | github_jupyter |
# Normalize text
```
herod_fp = '/Users/kyle/cltk_data/greek/text/tlg/plaintext/TLG0016.txt'
with open(herod_fp) as fo:
herod_raw = fo.read()
print(herod_raw[2000:2500]) # What do we notice needs help?
from cltk.corpus.utils.formatter import tlg_plaintext_cleanup
herod_clean = tlg_plaintext_cleanup(herod_raw, rm... | github_jupyter |
```
import pandas as pd
import numpy as np
# set the column names
colnames=['price', 'year_model', 'mileage', 'fuel_type', 'mark', 'model', 'fiscal_power', 'sector', 'type', 'city']
# read the csv file as a dataframe
df = pd.read_csv("./data/output.csv", sep=",", names=colnames, header=None)
# let's get some simple vi... | github_jupyter |
```
import glob
import os
import random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
import math
from tqdm.auto import tqdm
from sklearn import linear_model
import optuna
import seaborn as sns
FEAT_OOFS = [
{
'model' : 'feat_lasso',
'fn' : '../output/2021011_se... | github_jupyter |
```
#import necessary modules, set up the plotting
import numpy as np
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
import matplotlib;matplotlib.rcParams['figure.figsize'] = (8,6)
from matplotlib import pyplot as plt
import GPy
```
# Interacting with models
### November 2014, by Max Zwiessele
#### wi... | github_jupyter |
```
%matplotlib inline
```
# Partial Dependence Plots
Sigurd Carlsen Feb 2019
Holger Nahrstaedt 2020
.. currentmodule:: skopt
Plot objective now supports optional use of partial dependence as well as
different methods of defining parameter values for dependency plots.
```
print(__doc__)
import sys
from skopt.plot... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import tensorflow as tf
import numpy as np
import pandas as pd
import altair as alt
import shap
from interaction_effects.marginal import MarginalExplainer
from interaction_effects import utils
n = 3000
d = 3
batch_size = 50
learning_rate = 0.02
X = np.random.randn(n, d)
y = (np.s... | github_jupyter |
### Prepare stimuli in stereo with sync tone in the L channel
To syncrhonize the recording systems, each stimulus file goes in stereo, the L channel has the stimulus, and the R channel has a pure tone (500-5Khz).
This is done here, with the help of the rigmq.util.stimprep module
It uses (or creates) a dictionary of {st... | github_jupyter |
# Scaling and Normalization
```
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler
from scipy.cluster.vq import whiten
```
Terminology (from [this post](https://towardsdatascience.com/scale-standardi... | github_jupyter |
# Tutorial 6.3. Advanced Topics on Extreme Value Analysis
### Description: Some advanced topics on Extreme Value Analysis are presented.
#### Students are advised to complete the exercises.
Project: Structural Wind Engineering WS19-20
Chair of Structural Analysis @ TUM - R. Wüchner, M. Péntek
Autho... | github_jupyter |
```
# Load necessary modules and libraries
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Perceptron
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.model_selection import learning_curve
from sklearn.neural_network import M... | github_jupyter |
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