text stringlengths 2.5k 6.39M | kind stringclasses 3
values |
|---|---|
# Doppler Solve: Two Components
## Setup
```
%matplotlib inline
%run notebook_setup.py
import starry
from pathlib import Path
starry_path = Path(starry.__file__).parents[0]
starry.config.lazy = True
starry.config.quiet = True
import numpy as np
import matplotlib.pyplot as plt
import starry
import george
import pymc3... | github_jupyter |
# General API quickstart
```
%matplotlib inline
import numpy as np
import theano.tensor as tt
import pymc3 as pm
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_context('notebook')
plt.style.use('seaborn-darkgrid')
print('Running on PyMC3 v{}'.format(pm.__version__))
```
## 1. Model creation
Models i... | github_jupyter |
[Video](https://youtu.be/bA261BF0bdk) by Siraj Raval.
[DGL at a Glance](https://docs.dgl.ai/tutorials/basics/1_first.html) documentation.
```
%matplotlib inline
# Install DGL package
!pip install dgl
```
.. currentmodule:: dgl
DGL at a Glance
=========================
**Author**: `Minjie Wang <https://jermainew... | github_jupyter |
# Welcome to the OBD SDD BE!
Today, the goal is to understand how a distributed system can be useful when dealing with medium to large scale data sets.
We'll see that Dask start to be nice as soon as the Data we need to process doesn't quite fit in memory, but also if we
need to launch several computations in parall... | github_jupyter |
# Dependências
```
import os
import re
import unicodedata
import random
from enum import Enum
import nltk
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklea... | github_jupyter |
### 파이썬 알고리즘 6장: 문자열 조작
#### 팰린드롬
앞뒤가 똑같은 단어나 문장으로, 뒤집어도 같은 단어 또는 문장을 팰린드롬이라고 한다.
```
def isPalindrome(s:str) -> bool:
chars =[]
for char in s:
if char.isalnum():
chars.append(char.lower())
return chars == chars[::-1] #slicing을 통해 문자열을 뒤집어서 비교할 수 있다
s = 'race a car'
isPalindrome(s)
``... | github_jupyter |
# Encoders: Categorical Example
For categorical input we can also force the output to be binary.
----
#### Note on the data set
The data set used here is not particularly complex and/or big. It's not really all that challenging to find the fraud. In an ideal world we'd be using more complex data sets to show the rea... | github_jupyter |
# Process CoMMpass Data
In the following notebook, we process input RNAseq gene expression matrices for downstream machine learning applications.
Prior to processing, the input expression matrix was FPKM normalized.
We first calculate and visualize the per gene variability in the CoMMpass gene expression dataset.
We ... | github_jupyter |
```
# Jovian Commit Essentials
# Please retain and execute this cell without modifying the contents for `jovian.commit` to work
!pip install jovian --upgrade -q
import jovian
jovian.utils.colab.set_colab_file_id('17iO0rBs-gOFSUPbr6nd3IyfcKR-j2yYa')
```
# Cancer Mortality rate prediction for US counties using feedfowar... | github_jupyter |
```
import pandas as pd, numpy as np
from scipy import stats
stations=pd.read_csv('data/stations.csv').set_index('ID')
c='ro'
df=pd.read_csv('data/'+c+'_ds.csv') #daily data
# df=pd.read_csv('data/'+c+'_hs.csv') #high_res data
df['time']=pd.to_datetime(df['time'])
df['year']=df['time'].dt.year
df['month']=df['time'].dt... | github_jupyter |
<h1>Sustainable Energy Transitions</h1>
<div>A project by <a href="http://www.ssgouridis.org" target="_blank" >Sgouris Sgouridis</a> and <a href="http://www.csaladen.es" target="_blank" >Dénes Csala</a> at <a href="http://www.masdar.ac.ae" target="_blank">Masdar Institute of Science and Technology</a></div>
<h2><br>Pl... | github_jupyter |
# Convolutional Neural Networks
A CNN is made up of basic building blocks defined as tensor, neurons, layers and kernel weights and biases. In this lab, we use PyTorch to build a image classifier using CNN. The objective is to learn CNN using PyTorch framework.
Please refer to the link below for know more about CNN
htt... | github_jupyter |
# Bayes' law
Use Bayes’ law to calculate the probability of getting a data science job if you’ve gotten an interview for the job. This could be written P(get the DS job | interview). You’ll have to use Bayesian probability methods (your intuition or beliefs) to assign values to the different components of Bayes’ law.
... | github_jupyter |
# 20장. 군집화 (Clustering)
## 1. K-평균 군집화 (K-means clustering)
```
from scratch.linear_algebra import Vector
```
### 1.1 해밍 거리 (hamming distance)
두 벡터의 다른 값을 갖는 요소 개수
```
def num_differences(v1: Vector, v2: Vector) -> int:
assert len(v1) == len(v2)
return len([x1 for x1, x2 in zip(v1, v2) if x1 != x2])
assert... | github_jupyter |
### Configuration of the environment
```
%tensorflow_version 2.x
!pip3 install --upgrade pip
#!pip install -qU t5
!pip3 install git+https://github.com/google-research/text-to-text-transfer-transformer.git #extra_id_x support
import functools
import os
import time
import warnings
warnings.filterwarnings("ignore", cate... | github_jupyter |
With GPS-enabled devices, it's easy to collect a large quantity of trajectory data, i.e. a connected series of points in 2D or 3D. However, it's not so easy to plot large datasets with most plotting programs, and so people generally downsample the trajectories, which can hide important features of the data. Here we s... | github_jupyter |
```
# Import the usual suspects.
import pandas as pd
```
# Feature engineering for Resistance Profile
```
tbprofiler_df = pd.read_json("../data/raw/cohort.tbprofiler.json", encoding="UTF-8")
tbprofiler_df = tbprofiler_df.transpose()
tbprofiler_df.head()
tbprofiler_df.shape
resistance_status_df = tbprofiler_df
resista... | github_jupyter |
## Data and Model Preparation
The following code prepares TF-IDF used by KEA approaches. Please modify input_dir and output_file as per your local setup. For more details please look at https://boudinfl.github.io/pke/build/html/tutorials/training.html
```
# -*- coding: utf-8 -*-
import logging
import sys
from string... | github_jupyter |
# Unit 9: LightFM
You almost made it - this is the final lesson and it is also going to be the easiest one.
As you may already assume - there are a lot of recommender packages in Python out there. In this lesson we will look at LightFM - an easy to use and lightweight implementation of different approaches and algori... | github_jupyter |
[Sebastian Raschka](http://sebastianraschka.com), 2015
https://github.com/rasbt/python-machine-learning-book
# Python Machine Learning - Code Examples
# Chapter 13 - Parallelizing Neural Network Training with Theano
Note that the optional watermark extension is a small IPython notebook plugin that I developed to ma... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
from timeit import default_timer as timer
from functools import partial
from random import choices
import logging
import sdgym
from sdgym import load_dataset
from sdgym import benchmark
from sdgym import load_dataset
import numpy as np
import pandas as pd
import matplotlib.pyplot ... | github_jupyter |
#### Copyright 2019 The TensorFlow Hub Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
#... | github_jupyter |
<a href="https://colab.research.google.com/github/cateto/python4NLP/blob/main/ml_lec/cost_function.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import matplotlib.pyplot as plt
X = [1... | github_jupyter |
### 1- Check GPU type
```
!nvidia-smi
```
### 2- Install SimpleRepresentations library
```
!pip install simplerepresentations
```
### 3- Download the Large Movie Review Dataset
```
!wget https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
!tar xzf aclImdb_v1.tar.gz
!rm aclImdb_v1.tar.gz
```
### 4- Loa... | github_jupyter |
# Smart Queue Monitoring System - Transportation Scenario
## Overview
Now that you have your Python script and job submission script, you're ready to request an edge node and run inference on the different hardware types (CPU, GPU, VPU, FPGA).
After the inference is completed, the output video and stats files need to... | github_jupyter |
# Decision Trees and Random Forests in Python
**Learning Objectives**
1. Explore and analyze data using a Pairplot
2. Train a single Decision Tree
3. Predict and evaluate the Decision Tree
4. Compare the Decision Tree model to a Random Forest
## Introduction
In this lab, you explore and analyze data using a Pai... | 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 |
```
import datat_duocpu
import argparse
import logging
logger = logging.getLogger()
import win_unicode_console
win_unicode_console.enable()
def init_logger(log_file=None, log_file_level=logging.NOTSET):
log_format = logging.Formatter("[%(asctime)s %(levelname)s] %(message)s")#上面的%Y等是时间格式
logger = logging.getLo... | github_jupyter |
<img alt="QuantRocket logo" src="https://www.quantrocket.com/assets/img/notebook-header-logo.png">
<a href="https://www.quantrocket.com/disclaimer/">Disclaimer</a>
# Zipline Strategy Code
The strategy code is provided in [winners.py](winners.py).
## Install strategy file
To "install" the strategy, execute the foll... | github_jupyter |
# [Hashformers](https://github.com/ruanchaves/hashformers)
Hashformers is a framework for hashtag segmentation with transformers. For more information, please check the [GitHub repository](https://github.com/ruanchaves/hashformers).
# Installation
Here we install `mxnet-cu110` and `hashformers`.
`mxnet-cu110` is co... | github_jupyter |
# Writing custom Jaxpr interpreters in JAX
JAX offers several composable function transformations (`jit`, `grad`, `vmap`,
etc.) that enable writing concise, accelerated code.
Here we show how to add your own function transformations to the system, by writing a custom Jaxpr interpreter. And we'll get composability wi... | github_jupyter |
## 1. Setup
```
import sys
sys.path.append('../..')
import config
import matplotlib.pyplot as plt
import numpy as np
import os
import warnings
from keras.callbacks import ModelCheckpoint, Callback, TensorBoard
from neural_networks.unet import UNet
from neural_networks.keras_utils import EvalMetricsCallback
from utils... | github_jupyter |
```
import altair as alt
import pandas as pd
alt.renderers.enable('png')
#alt.renderers.enable('mimetype')
# import briefings with calculated emotion and topic values
briefings_df = pd.read_csv('../data/topic_scored_briefings.csv')
briefings_df
briefings_df.describe()
emotions_df = briefings_df.drop(columns=['tb_polari... | 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 |
# MAGIC Gamma Telescope - TPOT Classification Study
The below gives information about the data set:
The data are MC generated (see below) to simulate registration of high energy gamma particles in a ground-based atmospheric Cherenkov gamma telescope using the imaging technique. Cherenkov gamma telescope observes high... | github_jupyter |
# Installation
- Run these commands
- git clone https://github.com/Tessellate-Imaging/Monk_Object_Detection.git
- cd Monk_Object_Detection/3_mxrcnn/installation
- Select the right requirements file and run
- cat requirements_cuda9.0.txt | xargs -n 1 -L 1 pip install
# Monk Format
... | 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 |
# Plots for years experiments
Python code to generate the plots from the matlab experiments.
```
import scipy.io as spio
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
#matplotlib.rcParams['ps.useafm'] = True
#matplotlib.rcParams['pdf.use14corefonts'] = True
#matplotlib.rcParams['text.usetex'] = ... | github_jupyter |
Currently, Canadian/US phone numbers having the following format are supported as valid input:
* Country code of "1" (optional)
* Three-digit area code (optional)
* Three-digit central office code
* Four-digit station code
* Extension number preceded by "#", "x", "ext", or "extension" (optional)
A combination of numb... | github_jupyter |
<a href='http://www.holoviews.org'><img src="assets/hv+bk.png" alt="HV+BK logos" width="40%;" align="left"/></a>
<div style="float:right;"><h2>04. Exploration with Containers</h2></div>
In the first two sections of this tutorial we discovered how to declare static elements and compose them one by one into composite ob... | github_jupyter |
Below, you will find a series of methods that I (NG) tried to install Dedalus on Graham. Not all work, but some do. Raw cells can be directly copied-and-pasted.
# Using Graham's native modules
## The version that is currently (Dec. 2019) running
### Installation
Thanks to Julio/Jose Fuentes from McGill.
Create a d... | github_jupyter |
Our data generator will have three components. First, we define the link between the "magic" integer ids and the object attributes (e.g. name, location, etc. of activities in EXIOBASE). This will be in a Pandas dataframe.
```
import pandas as pd
df = pd.DataFrame([
{'index': 0, 'name': 'foo', 'location': 'CH'},
... | github_jupyter |
# SNLP Assignment 4
Name 1: Nikhil Paliwal<br/>
Student id 1: 7009915<br/>
Email 1: nipa00002@stud.uni-saarland.de<br/>
Name 2: Sangeet Sagar<br/>
Student id 2: 7009050<br/>
Email 2: sasa00001@stud.uni-saarland.de<br/>
**Instructions:** Read each question carefully. <br/>
Make sure you appropriately comment your c... | github_jupyter |
```
%pylab inline
```
# Annotation (`pyannote.core.annotation.Annotation`)
```
from pyannote.core import Annotation
```
**`Annotation`** instances are used to describe sets of annotated temporal fragments.
For instance, one can use an **`Annotation`** to store the result of speaker identification approach applied... | github_jupyter |
## 贝叶斯分类基本原理
### 贝叶斯定理：
条件概率公式
$$
P(A|B) = \dfrac{P(AB)}{P(B)}
$$
贝叶斯定理
$$
P(B_i|A)=\dfrac{P(A|B_i)P(B_i)}{\sum\limits_j P(A|B_j)P(B_j)}
$$
假设有N种可能的类别标记$\{c_1,c_2,...,c_N\}$，$P(c_i|\textbf{x})$，将样本x标记为$c_i$的后验概率
$$
P(c|\textbf{x})=\dfrac{P(\textbf{x},c)}{P(\textbf{x})}=\dfrac{P(c)P(\textbf{x}|c)}{P(\textbf{x})} ... | github_jupyter |
# Classification Exercise
We'll be working with some California Census Data, we'll be trying to use various features of an individual to predict what class of income they belogn in (>50k or <=50k).
Here is some information about the data:
<table>
<thead>
<tr>
<th>Column Name</th>
<th>Type</th>
<th>Description</th>
... | github_jupyter |
# REIFF
Regression Estimated Iterative Football Forecaster
```
from __future__ import division
from pandas import concat, read_csv, to_datetime
from ggplot import *
from sklearn import linear_model
import pandas as pd
import numpy as np
from numpy import floor, histogram
from scipy import stats
from scipy.stats impor... | github_jupyter |
# Unit 3: Demographic Recommendations
In this section we leave the boring field of unpersonalized content and do our first steps for more personalization. But, before tailoring content to individuals we first tailor content to groups of individuals that by some criteria seem to be similar and therefore - assumed to - ... | github_jupyter |
# Non-linear Gaussian filtering and smoothing
Provided are two examples of nonlinear state-space models on which one can perform Bayesian filtering and smoothing in order to obtain
a posterior distribution over a latent state trajectory based on noisy observations.
In order to understand the theory behind these method... | github_jupyter |
# Nonlinear Regression in [`SciPy`](https://docs.scipy.org/doc/scipy/reference/) and [R](https://www.r-project.org/about.html)
We often need to find a function $y=f(x,\beta)$ of variable $x$ and $p$ unknown parameters $\beta$ which fits a given set of $n$ predictor, {$x_1,...,x_n$}, and response, {$y_1,...,y_n$}, valu... | github_jupyter |
# TensorFlow Tutorial #02
# Convolutional Neural Network
by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)
/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)
## Introduction
The previous tutorial sho... | github_jupyter |
$$\frac{\partial u}{\partial t} + a \frac{\partial u}{\partial x} = 0$$
```
import numpy #here we load numpy
from matplotlib import pyplot #here we load matplotlib
%matplotlib inline
# Euler adelante
nx = 201
dx = 2 / (nx-1)
nt = 100 #nt is the number of timesteps we want to calculate
d... | github_jupyter |
## Analyze Logs of Evaluation Runs
- Copy the AWS RoboMaker evaluation simulation identification number.
- AWS SageMaker training job saves checkpoint and frozen graphs into an S3 model bucket. Copy the bucket and prefix from your training job.
```
s3_bucket = 'FILL_HERE'
s3_prefix = 'FILL_HERE'
```
## Imports
```... | github_jupyter |
# Train a dataset from Interface 2018/12 with Keras
- Unlike small book image dataset, it was little bit harder to fine-tune parameters.
- Similar accuracy with fast.ai could be achieved, but spent a lot more effort.
Using fast.ai library would be the shortest path to reach the goal.
```
##### import warnings
warnin... | github_jupyter |
```
%%html
<link href="http://mathbook.pugetsound.edu/beta/mathbook-content.css" rel="stylesheet" type="text/css" />
<link href="https://aimath.org/mathbook/mathbook-add-on.css" rel="stylesheet" type="text/css" />
<style>.subtitle {font-size:medium; display:block}</style>
<link href="https://fonts.googleapis.com/css?fa... | github_jupyter |
# Creating a Sentiment Analysis Web App
## Using PyTorch and SageMaker
_Deep Learning Nanodegree Program | Deployment_
---
Now that we have a basic understanding of how SageMaker works we will try to use it to construct a complete project from end to end. Our goal will be to have a simple web page which a user can u... | github_jupyter |
Before you turn this problem in, make sure everything runs as expected. First, **restart the kernel** (in the menubar, select Kernel$\rightarrow$Restart) and then **run all cells** (in the menubar, select Cell$\rightarrow$Run All).
Make sure you fill in any place that says `YOUR CODE HERE` or "YOUR ANSWER HERE", as we... | github_jupyter |
# Week 3: Project
- ✅ Were you able to create new models to answer the data questions on conversion rate?
- ✅ Were you able to add a new macro to your dbt project? (`grant`, `sum_if`)
- ✅ Were you able to add a post hook to your project to apply grants to the role "reporting"?
- ✅ Were you able to install a package? (... | github_jupyter |
<a href="https://colab.research.google.com/github/r-dube/fakejobs/blob/main/fj_roc_auc.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
# Load the modules used
import numpy as np
import scipy as sci
import pandas as pd
from sklearn.metrics import... | github_jupyter |
# 2D map fitting
## Prerequisites:
- To understand how a generel modelling and fiiting works in gammapy, please refer to the [analysis_3d tutorial](analysis_3d.ipynb)
## Context:
We often want the determine the position and morphology of an object. To do so, we don't necessarily have to resort to a full 3D fitting b... | github_jupyter |
```
# default_exp config
%load_ext autoreload
%autoreload 2
```
# Config File Handling
> We create a default blocklist.yaml file that stores the blocked URLs. It can be edited with command line arguments.
```
#export
import yaml
DEFAULT_URLS = ["twitter.com", "youtube.com", "facebook.com",
"instagram.com", "red... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
```
This notebook explores the background of the Kalman filter.
# Weighting the past against the present
The central idea of the Kalman filter is to weight the past against the present.
We're actually very familiar with this idea and ues it whenever averaging or... | github_jupyter |
# Delta Method
Code for reproducing all the results in the paper _The Delta-method and influence function in epidemiology: a reproducible tutorial_
### Authors
Rodrigo Zepeda-Tello 1| Michael Schomaker 2,3| Aurelien Belot 4| Camille Maringe 4| Mathew Smith 4| Bernard Rachet 4| Mireille E.Schnitzer 5,6| Miguel Ange... | github_jupyter |
# 常见的优化操作和优化算法
## 1. 标准化 Normalization
### 1.1 标准化输入 Normalizing inputs
对输入做标准化其实就是三个步骤：
1. 求训练集 $X_{train}$ 的均值 $\mu$ 和标准差 $\sigma$
2. $\frac{X_{train}-\mu}{\sigma}$
3. $\frac{X_{test}-\mu}{\sigma}$
做这一步的时候唯一要注意的就是求均值和标准差的方向。
```
import numpy as np
```
随机生成一个7行5列的 array ，表示一个有7个样本5个特征的数据集
```
X = np.random.r... | github_jupyter |
#### Reinforcement Learning Agent to play **Frozen Lake** Game!
**Game Rules:**
- We are in a 3x3 grid world which is 0-indexed.
- Starting from (0,0), Player should move in the grid inorder to maximise the reward.
- The player will receive a reward of +1 if he enters the grid numbered with 4/6 ( Treasure ).
- The pla... | github_jupyter |
```
from __future__ import print_function, absolute_import, with_statement
# from IPython import display as ipythondisplay
import tensorflow as tf
# tf.enable_eager_execution()
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import cv2
import os
# Import plot utilities
from dl_utils import myplo... | github_jupyter |
# Задание 3.1 - Сверточные нейронные сети (Convolutional Neural Networks)
Это последнее задание на numpy, вы до него дожили! Остался последний марш-бросок, дальше только PyTorch.
В этом задании вы реализуете свою собственную сверточную нейронную сеть.
```
import numpy as np
import matplotlib.pyplot as plt
%matplotl... | github_jupyter |
From : https://docs.python.org/3.6/reference/index.html
```
import os
os.getpid()
import inspect
import hybridcuda
cures = hybridcuda.initcuda()
hybridcuda.registerheader("hybpython.cuh", os.getcwd() + os.sep + ".." + os.sep + ".." + os.sep + "hybpython.cuh")
assert cures == 0
class hybridkernel:
gridDimX = 1
... | github_jupyter |
```
from kamodo.kamodo import Kamodo
```
## LaTeX support
Kamodo supports both python and LaTex-formatted expressions as input. For LaTeX, you must wrap your expression in ```$ $```:
```
Kamodo(f = 'x**2 + y**2', g = '$2x^2 + 3y^2$')
```
## Conventions
Kamodo's variable names have to follow python's naming conventio... | github_jupyter |
# Production Management Model
**Randall Romero Aguilar, PhD**
This demo is based on the original Matlab demo accompanying the <a href="https://mitpress.mit.edu/books/applied-computational-economics-and-finance">Computational Economics and Finance</a> 2001 textbook by Mario Miranda and Paul Fackler.
Original (Matlab... | github_jupyter |
# Regression
*Supervised* machine learning techniques involve training a model to operate on a set of *features* and predict a *label* using a dataset that includes some already-known label values. The training process *fits* the features to the known labels to define a general function that can be applied to new feat... | github_jupyter |
# Create Your Own Cognitive Portrait
## Technique: Circular Faces
Hello! Let's create some **Science Art** together with this **Cogntivie Portrait** challenge!
This is short notebook with mostly the code, you can view more detailed instructions in `CognitivePortrait.ipynb`.
```
import sys
!{sys.executable} -m pip in... | github_jupyter |
```
import logging
from tfprop_vis import ViewTFP, potential_func, kmeans_clust
import tfprop_som as tfpsom
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import ipywidgets as widgets
# may a pox befall the sompy dev who put logging configuration inside a programming library
logging.getLogger()... | github_jupyter |
# T81-558: Applications of Deep Neural Networks
**Module 5: Regularization and Dropout**
* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)
* For more information visit the [cla... | github_jupyter |
# Testing ML architectures implemented on the MLTSA package
In this packaged there are multiple architectures built in for testing on the different data available
```
"""First we import our dataset examples, and as usual generate data to work with"""
from OneD_pot_data import potentials
from OneD_pot_data import data... | github_jupyter |
# TME 8: Split
> Consignes: le fichier TME8_Sujet.ipynb est à déposer sur le site Moodle de l'UE https://moodle-sciences.upmc.fr/moodle-2019/course/view.php?id=4248. Si vous êtes en binôme, renommez-le en TME8_nom1_nom2.ipynb.
N'oubliez pas de sauvegarder fréquemment votre notebook !!
```
from PIL import Image
from p... | github_jupyter |
```
import warnings, gc
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import auc, roc_curve
from sklearn.preprocessing import LabelEncoder
# h2o modules
import h2o
from h2o.frame import H2OFrame
from h2o.grid.grid_search import H2OGridSearch
from h2o.... | 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 |
```
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import gc
from os.path import join as ospath
import tensorflow as tf
# from .utils import *
from tensorflow.keras.layers import Input,Flatten,Reshape,Dense, Lambda
from tensorflow.keras.l... | github_jupyter |
# Demo Script for ERDDAP transformations
Take Mooring Timeseries data and grid to 1hr so parameter(time,depth) - which is 1hr, 1m traditionally for EcoFOCI. Do not interpolate in depth. Use ERDDAP as datasource
Take CTD Collection of casts and grid ? (is this useful - not really)
**designed with akutan in mind**
... | github_jupyter |
# Customizing datasets in fastai
```
from fastai import *
from fastai.gen_doc.nbdoc import *
```
In this tutorial, we'll see how to create custom subclasses of [`ItemBase`](/core.html#ItemBase) or [`ItemList`](/data_block.html#ItemList) while retaining everything the fastai library has to offer. To allow basic functi... | github_jupyter |
# SageMaker PySpark XGBoost MNIST Example
1. [Introduction](#Introduction)
2. [Setup](#Setup)
3. [Loading the Data](#Loading-the-Data)
4. [Training and Hosting a Model](#Training-and-Hosting-a-Model)
5. [Inference](#Inference)
6. [More on SageMaker Spark](#More-on-SageMaker-Spark)
## Introduction
This notebook will s... | github_jupyter |
```
public abstract class Room {
abstract void connect(Room room);
}
public abstract class MazeGame {
private final List<Room> rooms = new ArrayList<>();
public MazeGame() {
Room room1 = makeRoom();
Room room2 = makeRoom();
room1.connect(room2);
rooms.add(room1);
... | github_jupyter |
```
#import needed modules
import os
import pandas as pd
pd.set_option('display.max_rows', 200)
import numpy as np
import matplotlib.pyplot as plt
from statsmodels.graphics.gofplots import qqplot
from scipy.stats import boxcox
from sklearn.linear_model import LinearRegression, RidgeCV, Ridge, LassoCV
from datetime impo... | github_jupyter |
# Transfer Learning
With certain data types it is possible to use the weights learned in one task to be **transferred** to another task. For example in a task that is used to detect Animals and Vehicles in images (as done in CIFAR10) could be reused to classify dogs and cats.
Transfer Learning is heavily used in Ima... | github_jupyter |
# SNRM Extension Steindl
## Preparations:
* Checkout original snrm code with extended functions
* download datasets, embedding
* extract download files
* move required data files in project directory
* setup anaconda with package dependencies
**Google Colab Runtime Type**
Set `Runtime -> Change Runtime t... | github_jupyter |
# About: Simple Hivemall query for Test
---
Hivemallの動作確認として、 [a9a binary classification](https://github.com/myui/hivemall/wiki#a9a-binary-classification) で示されたLogistic Regressionの動作確認をしてみる。
## *Operation Note*
*This is a cell for your own recording. ここに経緯を記述*
# Notebookと環境のBinding
Inventory中のgroup名でBind対象を指示する。... | github_jupyter |
# Multiscale Object Detection
:label:`sec_multiscale-object-detection`
In :numref:`sec_anchor`,
we generated multiple anchor boxes centered on each pixel of an input image.
Essentially these anchor boxes
represent samples of
different regions of the image.
However,
we may end up with too many anchor boxes to compu... | github_jupyter |
# Project Milestones
---------------
## Mar. 24, Milestone 1
|Deliverable | Percent Complete | Estimated Completion Date | Percent Complete by Next Milestone |
|-----------:|-----------------:|---------------:|-----------:|
|Code | 30%| Apr 2| 75% |
|Paper| 10%| Apr 21| 30%|
|De... | github_jupyter |
# Custom Estimator with Keras
**Learning Objectives**
- Learn how to create custom estimator using tf.keras
## Introduction
Up until now we've been limited in our model architectures to premade estimators. But what if we want more control over the model? We can use the popular Keras API to create a custom model.... | github_jupyter |
```
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 matplotlib import pyplot as plt
%matplotlib inline
```
# Create ... | github_jupyter |
# Grouping and sorting reference
This is the reference component to the "Grouping and sorting" section of the Advanced Pandas track.
```
import pandas as pd
reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv", index_col=0)
pd.set_option("display.max_rows", 5)
```
Grouping is so important that it h... | github_jupyter |
```
import duet
import numpy as real_numpy
from duet import pandas as pd
from duet import numpy as np
from duet import map
from duet import L2
from duet import LInf
from duet import zip
import matplotlib.pyplot as plt
import urllib.request
import os
epsilon = 1.0
alpha = 10
if not os.path.exists('../data_long/'):... | github_jupyter |
# Description
Runs hierarchical clustering on the umap version of the data.
# Environment variables
```
from IPython.display import display
import conf
N_JOBS = conf.GENERAL["N_JOBS"]
display(N_JOBS)
%env MKL_NUM_THREADS=$N_JOBS
%env OPEN_BLAS_NUM_THREADS=$N_JOBS
%env NUMEXPR_NUM_THREADS=$N_JOBS
%env OMP_NUM_THREA... | github_jupyter |
# Decision Trees and Random Forests in Python
**Learning Objectives**
1. Explore and analyze data using a Pairplot
2. Train a single Decision Tree
3. Predict and evaluate the Decision Tree
4. Compare the Decision Tree model to a Random Forest
## Introduction
In this lab, you explore and analyze data using a Pai... | github_jupyter |
```
import xarray as xr
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.ndimage import gaussian_filter
import yaml
from os.path import join
from hwtmode.data import load_patch_files, min_max_scale, storm_max_value, get_meta_scalars, combine_patch_data
import cartopy.crs as ccrs
import ... | github_jupyter |
```
import nltk
import difflib
import time
import gc
import itertools
import multiprocessing
import pandas as pd
import numpy as np
import xgboost as xgb
import lightgbm as lgb
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from sklearn.metri... | github_jupyter |
```
import argparse
import json
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from datetime import datetime
_version = int(datetime.now().strftime("%s"))
def init_flags():
global FLAGS
parser = argparse.ArgumentParser()
parser.add_argument("--rundir", default="./runs... | github_jupyter |
# TensorFlow Tutorial #17
# Estimator API
by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)
/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)
## WARNING!
**This tutorial does not work with TensorFlo... | github_jupyter |
```
import pandas as pd
import numpy as np
import os
import datetime
def edit_column_date(frame,index):
#Edits the date format of columns of dataframes
#index: index of the first column of dates + 1
i = 0
for col in frame:
i += 1
if i >= index:
new_d = date_format(col)
... | github_jupyter |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.