code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
# Problem Simulation Tutorial
```
import pyblp
import numpy as np
import pandas as pd
pyblp.options.digits = 2
pyblp.options.verbose = False
pyblp.__version__
```
Before configuring and solving a problem with real data, it may be a good idea to perform Monte Carlo analysis on simulated data to verify that it is poss... | github_jupyter |
# Softmax exercise
*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*
This exercise is ... | github_jupyter |
<a href="https://colab.research.google.com/github/ai-fast-track/timeseries/blob/master/nbs/index.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# `timeseries` package for fastai v2
> **`timeseries`** is a Timeseries Classification and Regression p... | github_jupyter |
<a href="https://colab.research.google.com/github/mouctarbalde/concrete-strength-prediction/blob/main/Cement_prediction_.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
fr... | github_jupyter |
# The Extended Kalman Filter
선형 칼만 필터 (Linear Kalman Filter)에 대한 이론을 바탕으로 비선형 문제에 칼만 필터를 적용해 보겠습니다. 확장칼만필터 (EKF)는 예측단계와 추정단계의 데이터를 비선형으로 가정하고 현재의 추정값에 대해 시스템을 선형화 한뒤 선형 칼만 필터를 사용하는 기법입니다.
비선형 문제에 적용되는 성능이 더 좋은 알고리즘들 (UKF, H_infinity)이 있지만 EKF 는 아직도 널리 사용되서 관련성이 높습니다.
```
%matplotlib inline
# HTML("""
# <style>
# .ou... | github_jupyter |
# Method for visualizing warping over training steps
```
import os
import imageio
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(0)
```
### Construct warping matrix
```
g = 1.02 # scaling parameter
# Matrix for rotating 45 degrees
rotate = np.array([[np.cos(np.pi/4), -np.sin(np.pi/4)],
... | github_jupyter |
# Documenting Classes
It is almost as easy to document a class as it is to document a function. Simply add docstrings to all of the classes functions, and also below the class name itself. For example, here is a simple documented class
```
class Demo:
"""This class demonstrates how to document a class.
... | github_jupyter |
# Lab 4: EM Algorithm and Single-Cell RNA-seq Data
### Name: Your Name Here (Your netid here)
### Due April 2, 2021 11:59 PM
#### Preamble (Don't change this)
## Important Instructions -
1. Please implement all the *graded functions* in main.py file. Do not change function names in main.py.
2. Please read the des... | github_jupyter |
# Week 2 Tasks
During this week's meeting, we have discussed about if/else statements, Loops and Lists. This notebook file will guide you through reviewing the topics discussed and assisting you to be familiarized with the concepts discussed.
## Let's first create a list
```
# Create a list that stores the multiples... | github_jupyter |
# launch scripts through SLURM
The script in the cell below submits SLURM jobs running the requested `script`, with all parameters specified in `param_iterators` and the folder where to dump data as last parameter.
The generated SBATCH scipts (`.job` files) are saved in the `jobs` folder and then submitted.
Output ... | github_jupyter |
<a href="https://colab.research.google.com/github/Vanagand/DS-Unit-2-Applied-Modeling/blob/master/module1-define-ml-problems/Unit_2_Sprint_3_Module_1_LESSON.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Lambda School Data Science
*Unit 2, Sprint... | github_jupyter |
<img src="images/strathsdr_banner.png" align="left">
# An RFSoC Spectrum Analyzer Dashboard with Voila
----
<div class="alert alert-box alert-info">
Please use Jupyter Labs http://board_ip_address/lab for this notebook.
</div>
The RFSoC Spectrum Analyzer is an open source tool developed by the [University of Strathc... | github_jupyter |
# Examples I - Inferring $v_{\rm rot}$ By Minimizing the Line Width
This Notebook intends to demonstrate the method used in [Teague et al. (2018a)](https://ui.adsabs.harvard.edu/#abs/2018ApJ...860L..12T) to infer the rotation velocity as a function of radius in the disk of HD 163296. The following [Notebook](Examples%... | github_jupyter |
## Data Extraction and load from FRED API..
```
## Import packages for the process...
import requests
import pickle
import os
import mysql.connector
import time
```
### Using pickle to wrap the database credentials and Fred API keys
```
if not os.path.exists('fred_api_secret.pk1'):
fred_key = {}
fred_key['... | github_jupyter |
```
import pathlib
import lzma
import re
import os
import datetime
import copy
import numpy as np
import pandas as pd
# Makes it so any changes in pymedphys is automatically
# propagated into the notebook without needing a kernel reset.
from IPython.lib.deepreload import reload
%load_ext autoreload
%autoreload 2
impor... | github_jupyter |
<img src="https://storage.googleapis.com/arize-assets/arize-logo-white.jpg" width="200"/>
# Arize Tutorial: Surrogate Model Feature Importance
A surrogate model is an interpretable model trained on predicting the predictions of a black box model. The goal is to approximate the predictions of the black box model as cl... | github_jupyter |
<a href="https://colab.research.google.com/github/MingSheng92/AE_denoise/blob/master/DL_Example.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!pip show tensorflow
!git clone https://github.com/MingSheng92/AE_denoise.git
from google.colab impor... | github_jupyter |
```
!pwd
%matplotlib inline
```
PyTorch: nn
-----------
A fully-connected ReLU network with one hidden layer, trained to predict y from x
by minimizing squared Euclidean distance.
This implementation uses the nn package from PyTorch to build the network.
PyTorch autograd makes it easy to define computational graphs... | github_jupyter |
# Understanding Data Types in Python
Effective data-driven science and computation requires understanding how data is stored and manipulated. This section outlines and contrasts how arrays of data are handled in the Python language itself, and how NumPy improves on this. Understanding this difference is fundamental to... | github_jupyter |
```
import numpy as np
from keras.models import Model
from keras.layers import Input
from keras.layers.pooling import GlobalMaxPooling1D
from keras import backend as K
import json
from collections import OrderedDict
def format_decimal(arr, places=6):
return [round(x * 10**places) / 10**places for x in arr]
DATA = O... | github_jupyter |
# GWAS, PheWAS, and Mendelian Randomization
> Understanding Methods of Genetic Analysis
- categories: [jupyter]
## GWAS
Genome Wide Association Studies (GWAS) look for genetic variants across the genome in a large amount of individuals to see if any variants are associated with a specific trait such as height or dis... | github_jupyter |
```
import nltk
from nltk import *
emma = nltk.Text(nltk.corpus.gutenberg.words('austen-emma.txt'))
len(emma)
emma.concordance("surprise")
from nltk.corpus import gutenberg
print(gutenberg.fileids())
emma = gutenberg.words("austen-emma.txt")
type(gutenberg)
for fileid in gutenberg.fileids():
n_chars = len(gutenberg... | github_jupyter |
# Water quality
## Setup software libraries
```
# Import and initialize the Earth Engine library.
import ee
ee.Initialize()
ee.__version__
# Folium setup.
import folium
print(folium.__version__)
# Skydipper library.
import Skydipper
print(Skydipper.__version__)
import matplotlib.pyplot as plt
import numpy as np
import... | github_jupyter |
# Calculating Area and Center Coordinates of a Polygon
```
%load_ext lab_black
%load_ext autoreload
%autoreload 2
import geopandas as gpd
import pandas as pd
%aimport src.utils
from src.utils import show_df
```
<a id="toc"></a>
## [Table of Contents](#table-of-contents)
0. [About](#about)
1. [User Inputs](#user-inpu... | github_jupyter |
# PTN Template
This notebook serves as a template for single dataset PTN experiments
It can be run on its own by setting STANDALONE to True (do a find for "STANDALONE" to see where)
But it is intended to be executed as part of a *papermill.py script. See any of the
experimentes with a papermill script to get sta... | github_jupyter |
# Batch Normalization – Practice
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a convolutional neural network with 20 convolutional layers, followed by a fully connected layer. We'll use it to classify handwritten digits in the MNIST dataset, which should be f... | github_jupyter |
# Country Economic Conditions for Cargo Carriers
This report is written from the point of view of a data scientist preparing a report to the Head of Analytics for a logistics company. The company needs information on economic and financial conditions is different countries, including data on their international trade,... | github_jupyter |
```
import warnings
warnings.filterwarnings('ignore')
import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas as pd
import seaborn as sns
sns.set(rc={'figure.figsize':(12, 6),"font.size":20,"axes.titlesize":20,"axes.labelsize":20},style="darkgrid")
```
Is there any ... | github_jupyter |
# Set-up notebook environment
## NOTE: Use a QIIME2 kernel
```
import numpy as np
import pandas as pd
import seaborn as sns
import scipy
from scipy import stats
import matplotlib.pyplot as plt
import re
from pandas import *
import matplotlib.pyplot as plt
%matplotlib inline
from qiime2.plugins import feature_table
fro... | github_jupyter |
## Health, Wealth of Nations from 1800-2008
```
import os
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
from bqplot import Figure, Tooltip, Label
from bqplot import Axis, ColorAxis
from bqplot import LogScale, LinearScale, OrdinalColorScale
from bqplot import Scatter, Lines
from bqplot im... | github_jupyter |
I want to analyze changes over time in the MOT GTFS feed.
Agenda:
1. [Get data](#Get-the-data)
3. [Tidy](#Tidy-it-up)
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import partridge as ptg
from ftplib import FTP
import datetime
import re
import zipfile
import os
%m... | github_jupyter |
Write in the input space, click `Shift-Enter` or click on the `Play` button to execute.
```
(3 + 1 + 12) ** 2 + 2 * 18
```
Give a title to the notebook by clicking on `Untitled` on the very top of the page, better not to use spaces because it will be also used for the filename
Save the notebook with the `Diskette` b... | github_jupyter |
# Saving and Loading Models
In this notebook, I'll show you how to save and load models with PyTorch. This is important because you'll often want to load previously trained models to use in making predictions or to continue training on new data.
```
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
i... | github_jupyter |
```
Function Order in a Single File¶
In the following code example, the functions are out of order, and the code will not compile. Try to fix this by rearranging the functions to be in the correct order.
#include <iostream>
using std::cout;
void OuterFunction(int i)
{
InnerFunction(i);
}
void InnerFunction(int i... | github_jupyter |
# Deep Learning & Art: Neural Style Transfer
Welcome to the second assignment of this week. In this assignment, you will learn about Neural Style Transfer. This algorithm was created by Gatys et al. (2015) (https://arxiv.org/abs/1508.06576).
**In this assignment, you will:**
- Implement the neural style transfer alg... | github_jupyter |
# Predicting Student Admissions with Neural Networks
In this notebook, we predict student admissions to graduate school at UCLA based on three pieces of data:
- GRE Scores (Test)
- GPA Scores (Grades)
- Class rank (1-4)
The dataset originally came from here: http://www.ats.ucla.edu/
## Loading the data
To load the da... | github_jupyter |
<a href="https://colab.research.google.com/github/ahvblackwelltech/DS-Unit-2-Kaggle-Challenge/blob/master/module2-random-forests/Ahvi_Blackwell_LS_DS_222_assignment.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Lambda School Data Science
*Unit 2,... | github_jupyter |
```
# List all NVIDIA GPUs as avaialble in this computer (or Colab's session)
!nvidia-smi -L
import sys
print( f"Python {sys.version}\n" )
import numpy as np
print( f"NumPy {np.__version__}" )
import matplotlib.pyplot as plt
%matplotlib inline
import tensorflow as tf
print( f"TensorFlow {tf.__version__}" )
print( f"... | github_jupyter |
# Imports and Paths
```
import torch
from torch import nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
import pandas as pd
import os
import shutil
from skimage import io, transform
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import Dataset, Da... | github_jupyter |
```
%matplotlib inline
```
# Compute LCMV inverse solution on evoked data in volume source space
Compute LCMV inverse solution on an auditory evoked dataset in a volume source
space. It stores the solution in a nifti file for visualisation e.g. with
Freeview.
```
# Author: Alexandre Gramfort <alexandre.gramfort@te... | github_jupyter |
```
#default_exp dataset.dataset
#export
import os
import torch
import transformers
import pandas as pd
import numpy as np
import Hasoc.config as config
#hide
df = pd.read_csv(config.DATA_PATH/'fold_df.csv')
#hide
df.head(2)
#hide
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
le.fit_transform(df.t... | github_jupyter |
# Speeding-up gradient-boosting
In this notebook, we present a modified version of gradient boosting which
uses a reduced number of splits when building the different trees. This
algorithm is called "histogram gradient boosting" in scikit-learn.
We previously mentioned that random-forest is an efficient algorithm sinc... | github_jupyter |
```
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import Imputer
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn import metrics... | github_jupyter |
```
# Install libraries
!pip -qq install rasterio tifffile
# Import libraries
import os
import glob
import shutil
import gc
from joblib import Parallel, delayed
from tqdm import tqdm_notebook
import h5py
import pandas as pd
import numpy as np
import datetime as dt
from datetime import datetime, timedelta
import matplo... | github_jupyter |
# Explore endangered languages from UNESCO Atlas of the World's Languages in Danger
### Input
Endangered languages
- https://www.kaggle.com/the-guardian/extinct-languages/version/1 (updated in 2016)
- original data: http://www.unesco.org/languages-atlas/index.php?hl=en&page=atlasmap (published in 2010)
Countries of... | github_jupyter |
# Polynomials Class
```
from sympy import *
import numpy as np
x = Symbol('x')
class polinomio:
def __init__(self, coefficienti: list):
self.coefficienti = coefficienti
self.grado = 0 if len(self.coefficienti) == 0 else len(
self.coefficienti) - 1
i = 0
while i < len(sel... | github_jupyter |
# Function to list overlapping Landsat 8 scenes
This function is based on the following tutorial: http://geologyandpython.com/get-landsat-8.html
This function uses the area of interest (AOI) to retrieve overlapping Landsat 8 scenes. It will also output on the scenes with the largest portion of overlap and with less t... | 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 |
# 증식을 통한 데이터셋 크기 확장
## 1. Google Drive와 연동
```
from google.colab import drive
drive.mount("/content/gdrive")
path = "gdrive/'My Drive'/'Colab Notebooks'/CNN"
!ls gdrive/'My Drive'/'Colab Notebooks'/CNN/datasets
```
## 2. 모델 생성
```
from tensorflow.keras import layers, models, optimizers
```
0. Sequential 객체 생성
1. ... | github_jupyter |
<a href="https://colab.research.google.com/github/coenarrow/MNistTests/blob/main/MNIST.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
### Start Julia evironment
```
# Install any required python packages here
# !pip install <packages>
# Here we in... | github_jupyter |
# 컴파일러에서 변수, 조건문 다루기
변수를 다루기 위해서는 기계상태에 메모리를 추가하고 메모리 연산을 위한 저급언어 명령을 추가한다.
조건문을 다루기 위해서는 실행코드를 순차적으로만 실행하는 것이 아니라 특정 코드 위치로 이동하여 실행하는 저급언어 명령을 추가한다.
```
data Expr = Var Name -- x
| Val Value -- n
| Add Expr Expr -- e1 + e2
-- | Sub Expr Expr
-- | Mul Expr... | github_jupyter |
# <span style="color: #B40486">BASIC PYTHON FOR RESEARCHERS</span>
_by_ [**_Megat Harun Al Rashid bin Megat Ahmad_**](https://www.researchgate.net/profile/Megat_Harun_Megat_Ahmad)
last updated: April 14, 2016
-------
## _<span style="color: #29088A">8. Database and Data Analysis</span>_
---
<span style="color: #00... | github_jupyter |
# Investigation of No-show Appointments Data
## Table of Contents
<ul>
<li><a href="#intro">Introduction</a></li>
<li><a href="#wrangling">Data Wrangling</a></li>
<li><a href="#eda">Exploratory Data Analysis</a></li>
<li><a href="#conclusions">Conclusions</a></li>
</ul>
<a id='intro'></a>
## Introduction
The data ... | github_jupyter |
# Test for Embedding, to later move it into a layer
```
import numpy as np
# Set-up numpy generator for random numbers
random_number_generator = np.random.default_rng()
# First tokenize the protein sequence (or any sequence) in kmers.
def tokenize(protein_seqs, kmer_sz):
kmers = set()
# Loop over protein seque... | github_jupyter |
# Spectral encoding of categorical features
About a year ago I was working on a regression model, which had over a million features. Needless to say, the training was super slow, and the model was overfitting a lot. After investigating this issue, I realized that most of the features were created using 1-hot encoding ... | github_jupyter |
# Multivariate SuSiE and ENLOC model
## Aim
This notebook aims to demonstrate a workflow of generating posterior inclusion probabilities (PIPs) from GWAS summary statistics using SuSiE regression and construsting SNP signal clusters from global eQTL analysis data obtained from multivariate SuSiE models.
## Methods o... | github_jupyter |
```
from utils import config, parse_midas_data, sample_utils as su, temporal_changes_utils, stats_utils, midas_db_utils, parse_patric
from collections import defaultdict
import math, random, numpy as np
import pickle, sys, bz2
import matplotlib.pyplot as plt
# Cohort list
cohorts = ['backhed', 'ferretti', 'yassour', '... | github_jupyter |
```
import keras
from keras.applications import VGG16
from keras.models import Model
from keras.layers import Dense, Dropout, Input
from keras.regularizers import l2, activity_l2,l1
from keras.utils import np_utils
from keras.preprocessing.image import array_to_img, img_to_array, load_img
from keras.applications.vgg16 ... | github_jupyter |
# Guided Investigation - Anomaly Lookup
__Notebook Version:__ 1.0<br>
__Python Version:__ Python 3.6 (including Python 3.6 - AzureML)<br>
__Required Packages:__ azure 4.0.0, azure-cli-profile 2.1.4<br>
__Platforms Supported:__<br>
- Azure Notebooks Free Compute
- Azure Notebook on DSVM
__Data Source Re... | github_jupyter |
# 4 - Train models and make predictions
## Motivation
- **`tf.keras`** API offers built-in functions for training, validation and prediction.
- Those functions are easy to use and enable you to train any ML model.
- They also give you a high level of customizability.
## Objectives
- Understand the common training wor... | github_jupyter |
<a href="https://colab.research.google.com/github/dribnet/clipit/blob/future/demos/CLIP_GradCAM_Visualization.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# CLIP GradCAM Colab
This Colab notebook uses [GradCAM](https://arxiv.org/abs/1610.02391) ... | github_jupyter |
# Capstone Project - Flight Delays
# Does weather events have impact the delay of flights (Brazil)?
### It is important to see this notebook with the step-by-step of the dataset cleaning process:
[https://github.com/davicsilva/dsintensive/blob/master/notebooks/flightDelayPrepData_v2.ipynb](https://github.com/davicsilv... | github_jupyter |
```
# Reload when code changed:
%load_ext autoreload
%reload_ext autoreload
%autoreload 2
%pwd
import sys
import os
path = "../"
sys.path.append(path)
#os.path.abspath("../")
print(os.path.abspath(path))
import os
import core
import logging
import importlib
importlib.reload(core)
try:
logging.shutdown()
impor... | github_jupyter |
Notebook prirejen s strani http://www.pieriandata.com
# NumPy Indexing and Selection
In this lecture we will discuss how to select elements or groups of elements from an array.
```
import numpy as np
#Creating sample array
arr = np.arange(0,11)
#Show
arr
```
## Bracket Indexing and Selection
The simplest way to pic... | github_jupyter |
# Chapter 4: Linear models
[Link to outline](https://docs.google.com/document/d/1fwep23-95U-w1QMPU31nOvUnUXE2X3s_Dbk5JuLlKAY/edit#heading=h.9etj7aw4al9w)
Concept map:
#### Notebook setup
```
import numpy as np
import pandas as pd
impo... | github_jupyter |
```
try:
import saspy
except ImportError as e:
print('Installing saspy')
%pip install saspy
import pandas as pd
# The following imports are only necessary for automated sascfg_personal.py creation
from pathlib import Path
import os
from shutil import copyfile
import getpass
# Imports without the setup check... | github_jupyter |
# VacationPy
----
#### Note
* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think through the steps.
```
# Dependencies and Setup
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import requests
import gmaps
import os
... | github_jupyter |
# Support Vector Machine (SVM) Tutorial
Follow from: [link](https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47)
- SVM can be used for both regression and classification problems.
- The goal of SVM models is to find a hyperplane in an N-dimensional space that... | github_jupyter |
<small><i>This notebook was put together by [Jake Vanderplas](http://www.vanderplas.com). Source and license info is on [GitHub](https://github.com/jakevdp/sklearn_tutorial/).</i></small>
# Supervised Learning In-Depth: Random Forests
Previously we saw a powerful discriminative classifier, **Support Vector Machines**... | github_jupyter |
# Talks markdown generator for academicpages
Takes a TSV of talks 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.html)). The co... | github_jupyter |
<a href="https://colab.research.google.com/github/isb-cgc/Community-Notebooks/blob/master/MachineLearning/How_to_build_an_RNAseq_logistic_regression_classifier_with_BigQuery_ML.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# How to build an RNA-se... | github_jupyter |
# FireCARES ops management notebook
### Using this notebook
In order to use this notebook, a single production/test web node will need to be bootstrapped w/ ipython and django-shell-plus python libraries. After bootstrapping is complete and while forwarding a local port to the port that the ipython notebook server w... | github_jupyter |
# Solving vertex cover with a quantum annealer
The problem of vertex cover is, given an undirected graph $G = (V, E)$, colour the smallest amount of vertices such that each edge $e \in E$ is connected to a coloured vertex.
This notebooks works through the process of creating a random graph, translating to an optimiza... | github_jupyter |
```
import sys
sys.path.append("/Users/sgkang/Projects/DamGeophysics/codes/")
from Readfiles import getFnames
from DCdata import readReservoirDC
%pylab inline
from SimPEG.EM.Static import DC
from SimPEG import EM
from SimPEG import Mesh
```
Read DC data
```
fname = "../data/ChungCheonDC/20150101000000.apr"
survey = r... | github_jupyter |
# FloPy
## Using FloPy to simplify the use of the MT3DMS ```SSM``` package
A multi-component transport demonstration
```
import os
import sys
import numpy as np
# run installed version of flopy or add local path
try:
import flopy
except:
fpth = os.path.abspath(os.path.join('..', '..'))
sys.path.append(f... | github_jupyter |
# Home2
Your home away from home <br>
The best location for your needs, anywhere in the world <br>
### Inputs:
Addresses (eg. 'Pune, Maharashtra')
Category List (eg. 'Food', 'Restaurant', 'Gym', 'Trails', 'School', 'Train Station')
Limit of Results to return (eg. 75)
Radius of search in metres (eg. 10,... | github_jupyter |
# Introduction
In a prior notebook, documents were partitioned by assigning them to the domain with the highest Dice similarity of their term and structure occurrences. The occurrences of terms and structures in each domain is what we refer to as the domain "archetype." Here, we'll assess whether the observed similari... | github_jupyter |
```
import pandas as pd
import praw
import re
import datetime as dt
import seaborn as sns
import requests
import json
import sys
import time
## acknowledgements
'''
https://stackoverflow.com/questions/48358837/pulling-reddit-comments-using-python-praw-and-creating-a-da... | github_jupyter |
```
#pip install sklearn numpy scipy matplotlib
from sklearn import datasets
iris = datasets.load_iris()
digits = datasets.load_digits()
print(digits.data)
digits.target
digits.images[0]
```
create a support vector classifier and manually set the gamma
```
from sklearn import svm, metrics
clf = svm.SVC(gamma=0.001, C... | github_jupyter |
# Residual Networks
Welcome to the second assignment of this week! You will learn how to build very deep convolutional networks, using Residual Networks (ResNets). In theory, very deep networks can represent very complex functions; but in practice, they are hard to train. Residual Networks, introduced by [He et al.](h... | github_jupyter |
# Project 3 Sandbox-Blue-O, NLP using webscraping to create the dataset
## Objective: Determine if posts are in the SpaceX Subreddit or the Blue Origin Subreddit
We'll utilize the RESTful API from pushshift.io to scrape subreddit posts from r/blueorigin and r/spacex and see if we cannot use the Bag-of-words algorithm... | github_jupyter |
Uses Fine-Tuned BERT network to classify biomechanics papers from PubMed
```
# Check date
!rm /etc/localtime
!ln -s /usr/share/zoneinfo/America/Los_Angeles /etc/localtime
!date
# might need to restart runtime if timezone didn't change
## Install & load libraries
!pip install tensorflow==2.7.0
try:
from official.nl... | github_jupyter |
# Wilcoxon and Chi Squared
```
import numpy as np
import pandas as pd
df = pd.read_csv("prepared_neuror2_data.csv")
def stats_for_neuror2_range(lo, hi):
admissions = df[df.NR2_Score.between(lo, hi)]
total_patients = admissions.shape[0]
readmits = admissions[admissions.UnplannedReadmission]
total_readm... | github_jupyter |
Note:
This notebook was executed on google colab pro.
```
!pip3 install pytorch-lightning --quiet
from google.colab import drive
drive.mount('/content/drive')
import os
os.chdir('/content/drive/MyDrive/Colab Notebooks/atmacup11/experiments')
```
# Settings
```
EXP_NO = 27
SEED = 1
N_SPLITS = 5
TARGET = 'target'
GR... | github_jupyter |
```
%reset -f
# libraries used
# https://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn... | github_jupyter |
# Essential Objects
This tutorial covers several object types that are foundational to much of what pyGSTi does: [circuits](#circuits), [processor specifications](#pspecs), [models](#models), and [data sets](#datasets). Our objective is to explain what these objects are and how they relate to one another at a high lev... | github_jupyter |
<img src="Techzooka.png">
## Hacker Factory Cyber Hackathon Solution
### by Team Jugaad (Abhiraj Singh Rajput, Deepanshu Gupta, Manuj Mehrotra)
We are a team of members, that are NOT moved by the buzzwords like Machine Learning, Data Science, AI etc. However we are a team of people who get adrenaline rush for seekin... | 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 |
```
from plot import *
from gen import *
# from load_data import *
from func_tools import *
from AGM import *
from GM import *
from BFGS import *
from LBFGS import *
from sklearn import metrics
import warnings
warnings.filterwarnings('ignore')
def purity_score(y_true, y_pred):
contingency_matrix = metrics.cluster... | github_jupyter |
##### Copyright 2018 The AdaNet 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 agre... | github_jupyter |
# CW Attack Example
TJ Kim <br />
1.28.21
### Summary:
Implement CW attack on toy network example given in the readme of the github. <br />
https://github.com/tj-kim/pytorch-cw2?organization=tj-kim&organization=tj-kim
A dummy network is made using CIFAR example. <br />
https://pytorch.org/tutorials/beginner/blitz/c... | github_jupyter |
# Simple RNN
In this notebook, we're going to train a simple RNN to do **time-series prediction**. Given some set of input data, it should be able to generate a prediction for the next time step!
<img src='assets/time_prediction.png' width=40% />
> * First, we'll create our data
* Then, define an RNN in PyTorch
* Fin... | github_jupyter |
```
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib import rc,rcParams
rc('text', usetex=True)
rcParams['figure.figsize'] = (8, 6.5)
rcParams['ytick.labelsize'],rcParams['xtick.labelsize'] = 17.,17.
rcParams['axes.labelsize']=19.
rcParams['legend.fontsize']=17.
rcParams['axes.title... | github_jupyter |
# Load MNIST Data
```
# MNIST dataset downloaded from Kaggle :
#https://www.kaggle.com/c/digit-recognizer/data
# Functions to read and show images.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
d0 = pd.read_csv('./mnist_train.csv')
print(d0.head(5)) # print first five rows of d0.
# ... | github_jupyter |
```
!pip install vcrpy
import vcr
offline = vcr.VCR(
record_mode='new_episodes',
)
```
# APIs and data
Catherine Devlin (@catherinedevlin)
Innovation Specialist, 18F
Oakwood High School, Feb 16 2017
# Who am I?
(hint: not Jean Valjean)
:
return 1-(0.5*np.tanh(A*((np.abs(phi)-phi_o)))+0.5)
def annot_max(x,y, ax=None):
x=np.array(x)
y=np.array(y)
xmax = x[np.argmax(y)]
ymax = y.max()
text= "x={:.3f}, y={:.3f}".format(xmax, ymax)
if not ax:
... | github_jupyter |
# Description
This task is to do an exploratory data analysis on the balance-scale dataset
## Data Set Information
This data set was generated to model psychological experimental results. Each example is classified as having the balance scale tip to the right, tip to the left, or be balanced. The attributes are the ... | github_jupyter |
# Summarizing Emails using Machine Learning: Data Wrangling
## Table of Contents
1. Imports & Initalization <br>
2. Data Input <br>
A. Enron Email Dataset <br>
B. BC3 Corpus <br>
3. Preprocessing <br>
A. Data Cleaning. <br>
B. Sentence Cleaning <br>
C. Tokenizing <br>
4. Store Data <br>
A. Local... | github_jupyter |
```
# Load essential libraries
import csv
import numpy as np
import matplotlib.pyplot as plt
import statistics
import numpy as np
from scipy.signal import butter, lfilter, freqz
from IPython.display import Image
from datetime import datetime
# Time and robot egomotion
time = []
standardized_time = []
standardized_tim... | github_jupyter |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.