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# Top Influential Features for Cats and Dogs
This file compares the top 50 features that influence the three main outcomes (adoption, euthanasia/died, and transfer) from both the xgboost and logistic models, and finds the matching entries from both models.
```
# Import dependencies
import pandas as pd
import numpy as... | github_jupyter |
# Name
Data preparation by executing an Apache Beam job in Cloud Dataflow
# Labels
GCP, Cloud Dataflow, Apache Beam, Python, Kubeflow
# Summary
A Kubeflow Pipeline component that prepares data by submitting an Apache Beam job (authored in Python) to Cloud Dataflow for execution. The Python Beam code is run with Cloud... | github_jupyter |
```
import lifelines
import pymc as pm
from pyBMA.CoxPHFitter import CoxPHFitter
import matplotlib.pyplot as plt
import numpy as np
from numpy import log
from datetime import datetime
import pandas as pd
%matplotlib inline
```
The first step in any data analysis is acquiring and munging the data
Our starting data set... | github_jupyter |
# CS5489- Machine Learning
# Lecture 9a - Convolutional Neural Networks (CNNs)
## Dr. Antoni B. Chan
### Dept. of Computer Science, City University of Hong Kong
# Outline
- Convolutional neural network (CNN)
- Regularization
```
# setup
%matplotlib inline
import IPython.core.display # setup output image forma... | github_jupyter |
## Test the Numerical Libraries being Used
```
import os
import numpy as np
import theano
import theano.tensor as T
import time
def show_config():
print("OMP_NUM_THREADS = %s" %
os.environ.get('OMP_NUM_THREADS','#CAREFUL : OMP_NUM_THREADS Not-defined!'))
print("theano.con... | github_jupyter |
```
import ray
ray.init(plasma_directory="/workspaces/sefik/temp")
import modin.pandas as modin
import time
import pandas as pd
tic = time.time()
modin_df = modin.read_csv("/workspaces/sefik/train.csv")
toc = time.time()
modin_time = toc-tic
print("Lasts ",modin_time," seconds in Modin")
#----------------------------
t... | github_jupyter |
```
import akshare as ak
import json
import pandas as pd
import sys
import datetime
import os
import pandas as pd
import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.commons.utils import JsCode
from pyecharts import options as opts
from pyecharts.charts import Scatter
from pyecharts.option... | github_jupyter |
# 1. Setup Paths
```
import os
CUSTOM_MODEL_NAME = 'my_ssd_mobnet'
PRETRAINED_MODEL_NAME = 'ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8'
PRETRAINED_MODEL_URL = 'http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.tar.gz'
TF_RECORD_SCRIPT_NAME = 'generate... | github_jupyter |
# LeNet Lab Solution
Source: Yan LeCun
## Load Data
Load the MNIST data, which comes pre-loaded with TensorFlow.
You do not need to modify this section.
```
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", reshape=False)
X_... | github_jupyter |
## Getting ROC Curve
**Author**: Thodoris Petropoulos
**Label**: Evaluating Models
### Scope
The scope of this notebook is to provide instructions on how to get ROC Curve data of a specific model using the Python API.
### Background
Insights provided by the ROC Curve are helpful in evaluating the performance of mac... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/AssetManagement/export_TimeSeries2.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_b... | github_jupyter |
Back to **[Fan](https://fanwangecon.github.io/)**'s R4Econ Homepage **[Table of Content](https://fanwangecon.github.io/R4Econ/)**
# Constrained Share Parameters to Unconstrained Parameters
Sometimes, the parameters we are optimizing over are constrained, we might be optimizing by choosing $a,b,c$. They sum up ot $1$,... | github_jupyter |
```
# Load the Numpy package, and rename to "np"
import numpy as np
```
### Iteration ###
It is often the case in programming – especially when dealing with randomness
– that we want to repeat a process multiple times.
We know the numpy function `random.randint` claims to choose randomly
between the integers in the ... | github_jupyter |
## Q-learning
This notebook will guide you through implementation of vanilla Q-learning algorithm.
You need to implement QLearningAgent (follow instructions for each method) and use it on a number of tests below.
```
#XVFB will be launched if you run on a server
import os
if type(os.environ.get("DISPLAY")) is not st... | github_jupyter |
# Prepare data for training dataframe
The aim is to produce a training table where each `order_id` is mapped to all coupons available in the departments from which products were selected in that order on the date the order was made, along with information (`True`/`False`) if such available coupon was used in that orde... | github_jupyter |
```
import io
import os
import json
import re
from text_to_num import alpha2digit
from PIL import Image
import base64
from io import BytesIO
# Imports the Google Cloud client library
from google.cloud import vision
from google.cloud.vision import types
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "Key.json"
im = I... | github_jupyter |
<img src="images/dask_horizontal.svg" align="right" width="30%">
# Distributed, Advanced
## Distributed futures
```
from dask.distributed import Client
c = Client(n_workers=4)
c.cluster
```
In chapter Distributed, we showed that executing a calculation (created using delayed) with the distributed executor is identi... | github_jupyter |
# NEB using ASE
### 1. Setting up an EAM calculator.
Suppose we want to calculate the minimum energy path of adatom diffusion on a (100) surface. We first need to choose an energy model, and in ASE, this is done by defining a "calculator". Let's choose our calculator to be Zhou's aluminum EAM potential, which we've u... | github_jupyter |
```
import mglearn
import matplotlib.pyplot as plt
%matplotlib inline
#mglearn.plots.plot_logistic_regression_graph()
mglearn.plots.plot_single_hidden_layer_graph()
mglearn.plots.plot_two_hidden_layer_graph()
```
# Dependencies
```
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test... | github_jupyter |
# Strategy Analysis with **Pandas TA** and AI/ML
* This is a **Work in Progress** and subject to change!
* Contributions are welcome and accepted!
* Examples below are for **educational purposes only**.
* **NOTE:** The **watchlist** module is independent of Pandas TA. To easily use it, copy it from your local pandas_ta... | github_jupyter |
# Part 2: Secure Model Serving with Syft Keras
Now that you have a trained model with normal Keras, you are ready to serve some private predictions. We can do that using Syft Keras.
To secure and serve this model, we will need three TFEWorkers (servers). This is because TF Encrypted under the hood uses an encryption ... | github_jupyter |
# Microsoft Azure - DP100
> This note helps you to prepare the Azure Assoicate Data Scientist DP-100 exam. I took DP100 in Mar 2021 and includes some important notes for study. Particularly, syntax types questions are very common. You need to study the lab and make sure you understand and remember some syntax to pass t... | github_jupyter |
# Finding Image Logs
## Go find them!
```
%matplotlib inline
import os
import pandas as pd
import dlisio
import matplotlib.pyplot as plt
import numpy as np
import numpy.lib.recfunctions as rfn
folderpath = r"C:\Users\aruss\Documents\FORCE Data"
```
#### Assumptions: Data is compiled into static and dynamic images (... | github_jupyter |
*best viewed in [nbviewer](https://nbviewer.jupyter.org/github/CambridgeSemiticsLab/BH_time_collocations/blob/master/results/notebooks/3_head_lexemes.ipynb)*
# Time Adverbial Distribution and Collocations
## Head Lexemes
### Cody Kingham
<a href="../../../docs/sponsors.md"><img height=200px width=200px align="left" sr... | github_jupyter |
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
```
# Define your model and create a residual function
In this example, we want to fit a cubic polynomial of the form $y = ax^3 + bx^2 + cx + d$ to data. For later convenience, we'll create a simple method to evaluate the polynomial, although t... | github_jupyter |
```
# -*- coding: utf-8 -*-
"""
This program makes learning ev-gmm.
"""
# __future__ module make compatible python2 and python3
from __future__ import division, print_function
# basic modules
import os
import os.path
import time
# for warning ignore
import warnings
#warning.filterwarnings('ignore')
# for file syste... | github_jupyter |
```
'''This script is a demo of how labels of each mutation types are added'''
import pandas as pd
import re
import numpy as np
from itertools import chain
df_rearrangement = pd.read_excel('report_rearrangement.xlsx')
df_rearrangement.fillna(0,inplace = True)
df_rearrangement
def deletion_check(protospacer, pattern):
... | github_jupyter |
### Script for generating prediction CSV files for Dev and Test sets
**GA-Ensembling weights and individual model predictions already available**
```
import pandas as pd
import numpy as np
# Reading CSV from link
def read_csv_from_link(url):
path = 'https://drive.google.com/uc?export=download&id='+url.split('/')[... | github_jupyter |
## Tune Model Parameters
Like in previous years, I'm going to use `RandomizedSearchCV` to tune the params. I tried grid search, but realised that tuning the models individually missed some params and functionality from the wrapper class (like filtering by minimum season, and adding column names to the `DataFrameConver... | github_jupyter |
Branching GP Regression on synthetic data
--
*Alexis Boukouvalas, 2017*
Branching GP regression with Gaussian noise on the hematopoiesis data described in the paper "BGP: Gaussian processes for identifying branching dynamics in single cell data".
This notebook shows how to build a BGP model and plot the posterior mo... | github_jupyter |
# Groupby Architecture
## Problem
Pandas has a well-designed groupby architecture, but when developing against it I often hit three challenges:
* It involves 4 to 5 classes, which can be hard to keep track of.
* Its design is similar to Categoricals--but what class names `codes`, another might name `labels`.
* Corre... | github_jupyter |
<!--NOTEBOOK_HEADER-->
*This notebook contains material from [nbpages](https://jckantor.github.io/nbpages) by Jeffrey Kantor (jeff at nd.edu). The text is released under the
[CC-BY-NC-ND-4.0 license](https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode).
The code is released under the [MIT license](https://opens... | github_jupyter |
# Start of the analysis notebook
**Author : Benjamin Thomitzni, Nils Oberhof, Marco Bauer**
*Date : 11.03*
*Affiliation : Team 4, IWR, Dreuw*
Place the required modules in the top, followed by required constants and global functions.
```
# required modules
import numpy as np
import matplotlib.pyplot as plt
imp... | github_jupyter |
# Multi-Label Baseline Models
This is the notebook containing End-To-End models for multi-label classification of CRO's, for both level using TF-IDF as input features for a set of classifiers
```
############################## CONFIG ##############################
# Task config
TASK = "binary" #@param ["multi-label",... | github_jupyter |
```
%run Config/ImgConfig.ipynb
```
- **Student:** Adam Napora (ID 18197892)
- **Supervisor:** Alessio Benavoli
- **Date:** 23 August 2020
- **Course:** MSc in Artificial Intelligence, 2019/2020
- **Faculty:** Science and Engineering
- **Title:** Enriched Camera Monitoring System with Computer Vision and Machine Learn... | github_jupyter |
# ShapRFECV vs sklearn RFECV
In this section we will compare the performance of the model trained on the features selected using the probatus [ShapRFECV](https://ing-bank.github.io/probatus/api/feature_elimination.html) and the [sklearn RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection... | github_jupyter |
```
import pandas as pd
import pyspark.sql.functions as F
from datetime import datetime
from pyspark.sql.types import *
from pyspark import StorageLevel
import numpy as np
pd.set_option("display.max_rows", 1000)
pd.set_option("display.max_columns", 1000)
pd.set_option("mode.chained_assignment", None)
from pyspark.ml i... | github_jupyter |
<center>
<img src="https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png" width="300" alt="cognitiveclass.ai logo" />
</center>
# **Space X Falcon 9 First Stage Landing Prediction**
## Web scraping Falcon 9 and Falcon Heavy Launches Records from Wikipedia
... | github_jupyter |
#### New to Plotly?
Plotly's Python library is free and open source! [Get started](https://plotly.com/python/getting-started/) by downloading the client and [reading the primer](https://plotly.com/python/getting-started/).
<br>You can set up Plotly to work in [online](https://plotly.com/python/getting-started/#initiali... | github_jupyter |
# Práctica 3: Regresión Logística Multi-clase y Redes Neuronales
Mario Quiñones Pérez y Guillermo García Patiño Lenza
## Parte 1: Regresión logística multi-clase
### Visualización de los datos
En esta parte nos encargamos de crear dos funciones, la principal que usaremos en toda la práctica (cargaDatos), que servir... | github_jupyter |
Using kernels in GPflow
--
*James Hensman 2016*
GPflow comes with a range of kernels that can be combined to make new kernels. In this notebook, we examine some of the kernels, show how kernels can be combined, discuss the active_dims feature and show how one can build a new kernel.
```
import gpflow
import numpy a... | github_jupyter |
<h3>Step 1: Import the requests library</h3>
```
import requests
```
<h3>Step 2: Send an HTTP request, get the response, and save in a variable</h3>
```
response = requests.get("http://www.epicurious.com/search/Tofu+Chili")
```
<h3>Step 3: Check the response status code to see if everything went as planned</h3>
<li... | github_jupyter |
```
import os
import sys
import re
import json
import numpy as np
import pandas as pd
from collections import defaultdict
module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
sys.path.append(module_path)
module_path = os.path.abspath(os.path.join('../onmt'))
if module_path not in sys.p... | github_jupyter |
# Differentiation
```
# With all python examples, beware that python can't handle numbers too small so some results will be inaccurate
import matplotlib.pyplot as plt
import numpy as np
```
### Limits
limits are useful in functions to achieve (or get as close as possible to) the result.
$$\lim_{x\to c}f(x) = L$$
I... | github_jupyter |
**What is a Matrix?**
A matrix is a 2-dimensional array that has m number of rows and n number of columns. In other words, matrix is a combination of two or more vectors with the same data type.
Note: It is possible to create more than two dimensions arrays with R.
<h1><center>Microsoft Malware Prediction</center></h1>
<h2><center>Can you predict if a machine will soon be hit with malware?</center></h2>
### Dependencies
```
#@title
import warn... | github_jupyter |
<a href="https://colab.research.google.com/github/AI4Finance-Foundation/ElegantRL/blob/master/tutorial_BipedalWalker_v3.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# **BipedalWalker-v3 Example in ElegantRL**
# **Task Description**
[BipedalWalk... | github_jupyter |
<a href="https://www.nvidia.com/en-us/deep-learning-ai/education/"> <img src="images/DLI Header.png" alt="Header" style="width: 400px;"/> </a>
# Improving your Model
Now that you've learned to successfully train a model, let's work towards a state of the art model. In this lab, we'll learn the levers that you, as a d... | github_jupyter |
<a href="https://colab.research.google.com/github/CPJKU/partitura_tutorial/blob/main/content/Partitura_tutorial.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# An Introduction to Symbolic Music Processing with Partitura
Partitura is python 3 pack... | 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 |
```
import numpy as np
```
# Scalar operations
This is simply done with the multiplication operator (`*`) in Python, where one of the objects is a scalar and the other is a Numpy array.
This is notated for a scalar $s$ and a matrix $A$ as:
$$C = sA$$
Scalar multiplication is commutative: $$C = sA = As$$
Create exa... | github_jupyter |
<a href="https://colab.research.google.com/github/imfreeman1/StartGitHubDeskTop/blob/main/%5BE_03%5DCat_nose2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import os
import cv2
import matplotlib.pyplot as plt
import numpy as np
import dlib
my_... | github_jupyter |
# Preparing and Analysing Expert Evaluation
## Imports
```
import pandas as pd
import numpy as np
import scipy as sp
from scipy.linalg import eigh
import matplotlib.pyplot as plt
import matplotlib as matplotlib
import networkx as nx
from networkx.algorithms import bipartite
from argparse import Namespace
import rando... | github_jupyter |
# Publications markdown generator for academicpages
Takes a TSV of publications with metadata and converts them for use with [academicpages.github.io](academicpages.github.io). This is an interactive Jupyter notebook ([see more info here](http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# AutoML 03: Remote Execution using Batch AI
In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you ca... | github_jupyter |
<H1>N.B: This example is not currently working due to issues with recently updated dependencies</H1>
```
%matplotlib inline
import os
import netCDF4
import numpy as np
from geophys_utils import NetCDFGridUtils
from geophys_utils import NetCDFLineUtils
from geophys_utils import get_gdal_wcs_dataset, get_gdal_grid_value... | github_jupyter |
# Sector Neutral
## Install packages
```
import sys
!{sys.executable} -m pip install -r requirements.txt
import cvxpy as cvx
import numpy as np
import pandas as pd
import time
import os
import quiz_helper
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (14, ... | github_jupyter |
## _*Using Qiskit Aqua for set packing problems*_
Given a collection $S$ of subsets of a set $X$, the set packing problem tries to find the subsets that are pairwise disjoint (in other words, no two of them share an element). The goal is to maximize the number of such subsets.
We will go through three examples to sho... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W3D2_HiddenDynamics/W3D2_Tutorial2.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 2
# Hidden Mar... | github_jupyter |
# CNTK 204: Sequence to Sequence Networks with Text Data
## Introduction and Background
This hands-on tutorial will take you through both the basics of sequence-to-sequence networks, and how to implement them in the Microsoft Cognitive Toolkit. In particular, we will implement a sequence-to-sequence model to perform... | github_jupyter |
# Calculating Rydberg atom transition frequencies
The wavelength of the transition between the $n_1$th and $n_2$th levels is given by,
\begin{equation}
\frac{1}{\lambda} = R_{M} \left( \frac{1}{(n_1-\delta_1)^2} - \frac{1}{(n_2-\delta_2)^2} \right)
\end{equation}
where $\delta_x$ are the quantum defects, and $R_... | github_jupyter |
```
import cartopy.crs as ccrs
import cosima_cookbook as cc
import cartopy.crs as ccrs
import cosima_cookbook as cc
import cartopy.feature as cft
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import cmocean as cm
from dask.distributed import Client
import matplotlib.path as mpath
import matp... | github_jupyter |
<!--HEADER-->
[*NBBinder test on a collection of notebooks about some thermodynamic properperties of water*](https://github.com/rmsrosa/nbbinder)
<!--BADGES-->
<a href="https://nbviewer.jupyter.org/github/rmsrosa/nbbinder/blob/master/tests/nb_export_builds/nb_water_md/02.00-Data.md"><img align="left" src="https://img.... | github_jupyter |
# Plotting with Python: Introduction to `Matplotlib`
One of the most important skills when working with Python, especially in a scientific setting, is learning how to plot data. While the process for getting to these plots may seem difficult at first, with enough practice (and with referencing the plotting documentati... | github_jupyter |
# Neural Network
**Learning Objectives:**
* Use the `DNNRegressor` class in TensorFlow to predict median housing price
The data is based on 1990 census data from California. This data is at the city block level, so these features reflect the total number of rooms in that block, or the total number of people who liv... | github_jupyter |
# Image Registration and Combination using the JWST Level 3 Pipeline - MIRI example
Stage 3 image (Image3, calwebb_image3) processing is intended for combining the calibrated data from multiple exposures (e.g., a dither or mosaic pattern) into a single distortion corrected product. Before being combined, the exposures... | github_jupyter |
```
%matplotlib inline
import os
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
bool_cmap = colors.ListedColormap([(1, 1, 1, 0), 'black'])
from fastadjust.io import h5read
from flyion import initialize, fly
from flyion.trajectory import final_position, trajectory
# constants
from scipy... | github_jupyter |
```
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import os.path as op
import pickle
import tensorflow as tf
from tensorflow import keras
from keras.models import Model,Sequential,load_model
from keras.layers import Input, Embedding
from keras.layers import Dense, Bidirectional
from keras... | github_jupyter |
# Unsupervised learning
### AutoEncoders
An autoencoder, is an artificial neural network used for learning efficient codings.
The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction.
<img src ="imgs/autoencoder.png" width="25%">
Un... | github_jupyter |
# Introduction to Feature Engineering
**Learning Objectives**
* Improve the accuracy of a model by using feature engineering
* Understand there's two places to do feature engineering in Tensorflow
1. Using the `tf.feature_column` module
2. In the input functions
## Introduction
Up until now we've been... | github_jupyter |
# Hello World!
Here's an example notebook with some documentation on how to access CMIP data.
```
%matplotlib inline
import xarray as xr
import intake
# util.py is in the local directory
# it contains code that is common across project notebooks
# or routines that are too extensive and might otherwise clutter
# the... | github_jupyter |
# Statistics in Python
In this section, we will cover how you can use Python to do some statistics. There are many packages to do so, but we will focus on four:
- [pandas](https://pandas.pydata.org/)
- [scipy's stats module](https://docs.scipy.org/doc/scipy/reference/stats.html)
- [statsmodels](http://www.statsmodels... | github_jupyter |
contributed by:
Tobias Rasse
Max Planck Institute for Heart and Lung Research
61231 Bad Nauheim, Germany
tobias.rasse@mpi-bn.mpg.de
Images recorded by:
Tobias Rasse with a Samsung Galaxy S6 Active Smartphone,
CC-BY 4.0 Licence
```
import pickle
import numpy as np
```
## General description of OpSeF
The analysis pi... | github_jupyter |
# Úloha 1 - určovanie príbuznosti pomocou kompresie
```
import gzip
def loadfasta(filename,verbose=0):
""" Parses a classically formatted and possibly
compressed FASTA file into a dictionary where the key
for a sequence is the first part of its header without
any white space; if verbose ... | github_jupyter |
<!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/Pytho... | github_jupyter |
<center><h1><b><span style="color:blue">Fitting</span></b></h1></center>
#### **Quick intro to the following packages**
- The core package `iminuit`.
- Model building and a word on the affiliated package `zfit`.
<center>
<br><h1><b>iminuit</b></h1>
<h2><b><span style="color:green">Python wrapper to Minuit2 min... | github_jupyter |
```
from pathlib import Path
import sys; sys.path.insert(0, str(Path('src').absolute()))
import os
cwd =os.getcwd()
import ast
import inspect
from IPython.display import Markdown as md
def flink(title: str, name: str=None):
# name is method name
if name is None:
name = title.replace('`', '') # meh
... | github_jupyter |
## Sample 1.2 for Astrostatistics
This sample displays some simple but important codes from which you can quickly learn how to program by python, in the context of astronomy.
```
import numpy as np
```
Here, I show the frequently used data type in python.
```
'''
Data type
'''
#list
a = [1, 3., 'rrr']
print(a)
print... | github_jupyter |
## Dependencies
```
import json
from tweet_utility_scripts import *
from transformers import TFDistilBertModel, DistilBertConfig
from tokenizers import BertWordPieceTokenizer
from tensorflow.keras.models import Model
from tensorflow.keras import optimizers, metrics, losses
from tensorflow.keras.callbacks import EarlyS... | github_jupyter |
##### Copyright 2020 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 |
# Changes In The Daily Growth Rate
> Changes in the daily growth rate for select countries.
- comments: true
- author: Thomas Wiecki
- categories: [growth]
- image: images/covid-growth.png
- permalink: /growth-analysis/
```
#hide
from pathlib import Path
loadpy = Path('load_covid_data.py')
if not loadpy.exists():
... | github_jupyter |
# NLP model creation and training
```
from fastai.gen_doc.nbdoc import *
from fastai.text import *
from fastai import *
```
The main thing here is [`RNNLearner`](/text.learner.html#RNNLearner). There are also some utility functions to help create and update text models.
```
show_doc(RNNLearner, doc_string=False)
``... | github_jupyter |
```
import msiwarp as mx
from msiwarp.util.read_sbd import read_sbd_meta, read_spectrum_fs
from msiwarp.util.warp import to_mz, peak_density_mz, plot_range, get_mx_spectrum, generate_mean_spectrum
import matplotlib.pyplot as plt
import numpy as np
# scaling to test impact of sigma on alignment performance
sigma_1 = 2... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '3'
import xlnet
import numpy as np
import tensorflow as tf
from tqdm import tqdm
import model_utils
import pickle
import json
pad_sequences = tf.keras.preprocessing.sequence.pad_sequences
import sentencepiece as spm
from prepro_utils import preprocess_text, encode_ids... | github_jupyter |
This notebook demonstrates how to create an analysis ready spatialite database for borehoel data. All data has been processed filtered and the depths corrected onto to metres below ground level. Induction and gamma data are resampled to 5cm intervals and are on the same table.
Neil Symington neil.symington@ga.gov.au
... | github_jupyter |
```
import os,sys
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib
from matplotlib import pyplot as plt
```
# Get the data sorted
## Temperature from environment DB
```
temp = pd.read_csv("/home/prokoph/CTA/ArrayClockSystem/WRS/MonitoringWRSS/weather_Jan13.csv",index_col=0, parse_dates=Tru... | github_jupyter |
<a href="https://colab.research.google.com/github/souravgopal25/DeepLearnigNanoDegree/blob/master/NumpyReFresher.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#Numpy Refresher
```
# Use the numpy library
import numpy as np
def prepare_inputs(inp... | github_jupyter |
# Introduction to Deep Learning. Lecture
## Introduction
### As a part of ML
<div style="width:image width px;
font-size:80%;
text-align:center;
float: left; padding-left-right-top-bottom:0.5em;
border-style: solid; border-color: rgba(211, 211, 211, 0.000);
... | github_jupyter |
# $\color{blue}{\text{Final Project }}$
## $\color{blue}{\text{Group AU }}$
## $\color{blue}{\text{Project Subject:}}$ Stock market indices in Israel and the United States, Number of coronavirus infections in the US And their effects on each other
<div>
<img src="https://raw.githubusercontent.com/ArielHezi/DS_Stock... | github_jupyter |
```
# Standard imports
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import time
# Insert path to mavenn beginning of path
import os
import sys
abs_path_to_mavenn = os.path.abspath('../../')
sys.path.insert(0, abs_path_to_mavenn)
# Load mavenn
import mavenn
print(mavenn.__path__)
# Load examp... | github_jupyter |
```
%matplotlib inline
import pandas as pd
import numpy as np
from numpy.linalg import inv, eig, svd
from numpy.random import uniform, randn, seed
from itertools import repeat
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn.decomposition import PCA
from sklearn.datasets import load_... | github_jupyter |
**Reinforcement Learning with TensorFlow & TRFL: Deep Q Network**
* Introduce the Deep Q Network (DQN) and its key parts
* Show how TRFL Q learning works with DQN
* Customize target network updating with TRFL's flexible usage
Outline:
1. Introduce CartPole
2. Introduce DQN
3. TRFL Q learning using DQN and loss output
... | github_jupyter |
# Day and Night Image Classifier
---
The day/night image dataset consists of 200 RGB color images in two categories: day and night. There are equal numbers of each example: 100 day images and 100 night images.
We'd like to build a classifier that can accurately label these images as day or night, and that relies on f... | github_jupyter |
# Computer Vision
We import `numpy` (as before) and `mahotas` (for image processsing/computer vision):
```
import numpy as np
import mahotas as mh
```
Make plots inline:
```
%matplotlib inline
```
## Basic image processing
First example:
```
image = mh.imread('scene00.jpg')
from matplotlib import pyplot as plt
fi... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import datetime
import os, sys
import numpy as np
import matplotlib.pyplot as plt
import casadi as cas
import pickle
import copy as cp
import argparse
PROJECT_PATH = '/home/nbuckman/Dropbox (MIT)/DRL/2020_01_cooperative_mpc/mpc-multiple-vehicles/'
sys.path.append(PROJECT_PATH)... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import dataset_builder as db
ACT_LABELS = ["dws","ups", "wlk", "jog", "std"]
TRIAL_CODES = {
ACT_LABELS[0]:[1,2,11],
ACT_LABELS[1]:[3,4,12],
ACT_LABELS[2]:[7,8,15],
ACT_LABELS[3]:[9,16],
ACT_LABELS[4]:[6,14],
}
import tensorf... | github_jupyter |
# Regularization of linear regression model
In this notebook, we will see the limitations of linear regression models and
the advantage of using regularized models instead.
Besides, we will also present the preprocessing required when dealing
with regularized models, furthermore when the regularization parameter
need... | github_jupyter |
<h1> Create TensorFlow wide-and-deep model </h1>
This notebook illustrates:
<ol>
<li> Creating a model using the high-level Estimator API
</ol>
```
# change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.environ['BUCKET'] = BUCKE... | github_jupyter |
```
from bokeh.plotting import figure
from bokeh.io import output_notebook,show
import numpy as np
output_notebook()
```
# Bayes Theorem Mini-Lab
This lab is a chance to work with Bayes Theorem. The underlying dataset is a collection of SMS (text) messages
that were labelled as either 'junk' or 'real' as part of an ... | github_jupyter |
# Prédiction de la note des vins
Le notebook compare plusieurs de modèles de régression.
```
%matplotlib inline
import warnings
warnings.simplefilter('ignore')
from papierstat.datasets import load_wines_dataset
df = load_wines_dataset()
X = df.drop(['quality', 'color'], axis=1)
y = yn = df['quality']
from sklearn.mod... | github_jupyter |
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