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The Wedin bound and random direction samples are embarassingly parallel and can be the main computational bottleneck in AJIVE. These can be done in parallel in two different ways: using multiple cores on one computer or over multiple computers. The AJIVE object easily handles the former of these using `sklearn.external... | github_jupyter |
```
import numpy as np
import itertools
from enum import Enum
```
### Define Latex Tokens
```
# give all tokens in latex expressions
# tokens for natural expression would be words
latex_token = ['{', '}', '\\', '-', '+', '^', '_', '(', ')', ',', ' ',
'frac', 'times', 'ne', 'ge', 'le',
'... | github_jupyter |
# Grover and quantum search
The purpose of this notebook is to briefly demonstrate the programming and execution of a simple Quantum search algorithm in the QLM.
## Grover's algorithm
Grover's algorithm rely on a two main ingredients:
- a diffusion operator $\mathcal{D} = 2 |s\rangle\langle s| - I$, where $|s\rangle... | github_jupyter |
## ํ์ํ ๋ผ์ด๋ธ๋ฌ๋ฆฌ ๋ก๋
```
# ๋ฐ์ดํฐ ๋ถ์์ ์ํ pandas, ์์น๊ณ์ฐ์ ์ํ numpy
# ์๊ฐํ๋ฅผ ์ํ seaborn, matplotlib.pyplot ์ ๋ก๋ํฉ๋๋ค.
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
```
## ๋ฐ์ดํฐ์
๋ก๋
```
df = pd.read_csv("data/diabetes.csv")
df.shape
```
## ๋ฐ์ดํฐ ์ ์ฒ๋ฆฌ
```
df_notnull = d... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import Lasso
from sklearn.linear_model import Ridge
path_train = r"Data\Polynomial Features Train.csv"
... | github_jupyter |
```
import os
from os.path import exists
####################you will need to change some paths here!#####################
#output files
filename_out_nc='F:/data/cruise_data/saildrone/baja-2018/daily_files/sd-1002/saildrone-gen_4-baja_2018-EP-sd1002-ALL-1_min-v1.nc'
#F:/data/cruise_data/saildrone/baja-2018/data_so_far.... | github_jupyter |
# Prep filtered scaffold sets for distributed design
### Imports
```
%load_ext lab_black
# Python standard library
from glob import glob
import os
import socket
import sys
# 3rd party library imports
import dask
import matplotlib.pyplot as plt
import pandas as pd
import pyrosetta
import numpy as np
import scipy
impo... | github_jupyter |
**Chapter 15 โ Processing Sequences Using RNNs and CNNs**
_This notebook contains all the sample code in chapter 15._
# Setup
First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Pyth... | github_jupyter |
# Tests on Extreme Inputs
The following test section has the purpose is comprising tests that we expect to fail. This comprise tests that will show the limitation when working with floating point numbers. In particular this relates to testing for input values (x) in our matrices that exceed our limit of x < sqrt(2**53... | github_jupyter |
# A TUTORIAL ON LINEAR REGRESSION
by Sebastian T. Glavind, May, 2020
```
import numpy as np
import math
import scipy.stats as ss
import seaborn as sns
import pandas as pd
import pickle
from matplotlib import pyplot as plt
%matplotlib inline
```
## The model
In the simplest case of linear regression, sometimes calle... | github_jupyter |
```
import dill as pickle
import pandas as pd
import os
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
import numpy as np
import re
from scipy import stats
from sklearn import metrics
from sklearn.feature_selection import SelectPercentile, SelectFromModel
from sklearn.model_selection impor... | github_jupyter |
```
#Visualize Samples from the model
import sys, os, glob
from collections import OrderedDict
sys.path.append('../../')
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['lines.linewidth']=5
mpl.rcParams['lines.markersize']=15
mpl.rcParams['text.usetex']=True
m... | github_jupyter |
# Ridge operators
### Dr. Tirthajyoti Sarkar, Fremont CA 94536
Ridge filters can be used to detect ridge-like structures, such as neurites, tubes, vessels, wrinkles, or rivers.
Different ridge filters may be suited for detecting different structures, e.g., depending on contrast or noise level.
The present class of ... | github_jupyter |
```
import pandas as pd
full_df = pd.read_csv('vsa_descriptors.csv', index_col=0)
full_df.describe()
hist = full_df.hist(bins=15,figsize=(25, 15))
df = full_df.sample(frac=0.9, random_state=786)
df_unseen = full_df.drop(df.index)
df.reset_index(drop=True, inplace=True)
df_unseen.reset_index(drop=True, inplace=True)
... | github_jupyter |
### Example 3 , part B: Diffusion for non uniform material properties
In this example we will look at the diffusion equation for non uniform material properties and how to handle second-order derivatives. For this, we will reuse Devito's `.laplace` short-hand expression outlined in the previous example and demonstrat... | github_jupyter |
```
# Imports
import numpy as np
import torch
from phimal_utilities.data import Dataset
from phimal_utilities.data.burgers import BurgersDelta
from DeePyMoD_SBL.deepymod_torch.library_functions import library_1D_in
from DeePyMoD_SBL.deepymod_torch.DeepMod import DeepMod, DeepModDynamic
from sklearn.linear_model import... | github_jupyter |
```
"""
The MIT License (MIT)
Copyright (c) 2021 NVIDIA
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, pub... | github_jupyter |
attempt 1
```
from openmmtools.testsystems import AlanineDipeptideExplicit
hge =
system, positions, topology = hge.system, hge.positions, hge.topology
from qmlify.openmm_torch.force_hybridization import HybridSystemFactory
from simtk import unit
hge.system.getForces()
from openmmtools.testsystems import HostGuestExpli... | github_jupyter |
### Unidad 3 - Lecciรณn 1: *Hablemos de funciones*
ยกFunciones hechas por tรญ!: Construcciรณn de funciones de forma simple, con/sin valores de retorno y con/sin parรกmetros
```
# define a single function hello to say hi
def hello():
print('Quiubo')
# call the function hello
hello()
# define the function hello, passing a... | github_jupyter |
```
import pandas as pd
import datetime
import plotly.express as px
def wrangle(file,services=10):
df = pd.read_csv(file, parse_dates=["date"], index_col="date")
df = df[~df.index.isna()]
df["zipcode"] = [i.split()[-3].rstrip(",") for i in df["address"]]
zips = [i.isnumeric() for i in df["zipcod... | github_jupyter |
### Import Libraries and Read Data
```
!pip install geopandas -qq
!pip install fiona -qq
!pip install folium -qq
!pip install pandas==1.0.0
!pip install selenium -qq
from selenium import webdriver
!wget https://bitbucket.org/ariya/phantomjs/downloads/phantomjs-2.1.1-linux-x86_64.tar.bz2
!tar xvjf phantomjs-2.1.1-linux... | github_jupyter |
### Cluster state knitting
Let's build some cluster states out of elementary gates.
```
%matplotlib inline
import numpy as np
import qutip as qt
import matplotlib
import matplotlib.pyplot as plt
from functools import reduce
import networkx as nx
def show_graph(V, E):
G = nx.Graph()
G.graph['dpi'] = 40
G.... | github_jupyter |
# Desafio 6
Neste desafio, vamos praticar _feature engineering_, um dos processos mais importantes e trabalhosos de ML. Utilizaremos o _data set_ [Countries of the world](https://www.kaggle.com/fernandol/countries-of-the-world), que contรฉm dados sobre os 227 paรญses do mundo com informaรงรตes sobre tamanho da populaรงรฃo, ... | github_jupyter |
# FastAI Experiments Using Google Colab-GPU
<a href="https://colab.research.google.com/github/rambasnet/DeepLearningMaliciousURLs/blob/master/FastAI-ExperimentsUsingGPU.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Import Libraries
```
from f... | github_jupyter |
# <div align="center">**`dm_control` tutorial**</div>
# <div align="center">[](https://colab.research.google.com/github/deepmind/dm_control/blob/master/tutorial.ipynb)</div>
> <p><small><small>Copyright 2020 The dm_control Authors.</small></p>
>... | github_jupyter |
# Video Segmentation Data
This is a demonstration of a single simulation of SEM on a single video. In practice, this simulation is repeated in multiple batches and over three videos. We only include one for simplicity and computational resources.
For reasons of space, we have only included the processed video data ... | github_jupyter |
##### Copyright 2018 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 |
# Let's code a Neural Network in plainย NumPy
---
***Author: Piotr Skalski***
Using high-level frameworks like Keras, TensorFlow or PyTorch allows us to build very complex models quickly. However, it is worth taking the time to look inside and understand underlying concepts. This time we will try to make use of our kn... | github_jupyter |
# Dependencies
```
# Dependencies:
import os
import pandas as pd
import numpy as np
import pickle
import time
import pymongo
from bs4 import BeautifulSoup
from splinter import Browser
import requests
from pprint import pprint
```
# Portland MLS
-Navigate to homepage
-Collect the number of pages and number... | github_jupyter |
```
!pip install -r https://raw.githubusercontent.com/datamllab/automl-in-action-notebooks/master/requirements.txt
```
### Load dataset
```
from sklearn.datasets import fetch_california_housing
house_dataset = fetch_california_housing()
# Import pandas package to format the data
import pandas as pd
# Extract featu... | github_jupyter |
# Endpoint layer pattern
**Author:** [fchollet](https://twitter.com/fchollet)<br>
**Date created:** 2019/05/10<br>
**Last modified:** 2019/05/10<br>
**Description:** Demonstration of the "endpoint layer" pattern (layer that handles loss management).
## Setup
```
import tensorflow as tf
from tensorflow import keras
i... | github_jupyter |
```
import geoglows
import netCDF4 as nc
import plotly
import plotly.graph_objs as go
import pandas as pd
import numpy as np
import datetime
import calendar
fr = nc.Dataset('forecast_record-2020-central_america-geoglows.nc')
time = pd.to_datetime(fr['time'][:].data, unit='s', origin='unix')
flow = list(fr['Qout'][list(... | github_jupyter |
```
import __init__
from __init__ import DATA_PATH
from __init__ import PACKAGE_PATH
import numpy as np
import pandas as pd
import os
import matplotlib as mplt
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import utilities
from descriptor import rdkitDescriptors
df=pd.read_csv(os.path.join(DA... | github_jupyter |
```
# default_exp augmentation
```
# Data Augmentation
> Improve predictions doing data augmentation
```
# export
import random
import math
import numpy as np
import pandas as pd
from rdkit import Chem
# hide
from rxn_yields.data import generate_buchwald_hartwig_rxns
# hide
test_df = pd.DataFrame({"Ligand":{"0":"CC(... | github_jupyter |
# Earth temperature over time
Is global temperature rising? How much? This is a question of burning importance in today's world!
Data about global temperatures are available from several sources: NASA, the National Climatic Data Center (NCDC) and the University of East Anglia in the UK. Check out the [University Corp... | github_jupyter |
<a href="https://colab.research.google.com/github/PyTorchLightning/lightning-flash/blob/master/flash_notebooks/custom_task_tutorial" target="_parent">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
# Tutorial: Creating a Custom Task
In this tutorial we will go over ... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Gena/hillshade_and_water.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" hre... | github_jupyter |
```
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchsummary import summary
import gym
import os
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import clear_output
# for using sampling with gradient-tracking when selecting an action
# Ca... | github_jupyter |
```
#Check for available devices
from tensorflow.python.client import device_lib
device_lib.list_local_devices()
import os
import sys
import json
import datetime
import numpy as np
import skimage.draw
# Root directory of the project
ROOT_DIR = os.path.abspath("../../")
# Import Mask RCNN
sys.path.append(ROOT_DIR) # ... | github_jupyter |
Shah, Jai
1001-380-311
2017-02-07
Assignment_01_01
```
%matplotlib inline
import cv2
import matplotlib.pyplot as plt
import matplotlib.colors
import numpy as np
from PIL import Image
from skimage import data
import scipy
import math
from scipy.ndimage import measurements
from skimage import data
from ipywid... | github_jupyter |
# POC of model finetuning using AISG FAQ data
Note: this is not a rigorous proof of the finetuning abilities, it merely shows that we are able to overfit to one dataset. The main purpose is to build and test the finetuning code using tensorflow.
Tested with just minimizing cosine loss. This kind of works, but having ... | github_jupyter |
<a href="https://hub.callysto.ca/jupyter/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fcallysto%2Fcurriculum-notebooks&branch=master&subPath=Mathematics/StatisticsProject/Accessing... | github_jupyter |
# Step 0.0. Install LightAutoML
```
# !pip install -U lightautoml
```
# Step 0.1. Import necessary libraries
```
# Standard python libraries
import os
import time
# Installed libraries
import numpy as np
import pandas as pd
from sklearn.metrics import roc_auc_score, f1_score
from sklearn.model_selection import trai... | github_jupyter |
This file contains the first experiment of the paper, where we illustrate that a significant reduction of the storage is possible with little loss of performance with the LB-SDA-LM algorithm we propose.
```
import arms
import numpy as np
import matplotlib.pyplot as plt
from tracker import Tracker2, SWTracker, Discount... | github_jupyter |
## Required Frameworks
```
import matplotlib.pyplot as plt
from google.colab.patches import cv2_imshow
import cv2
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.utils import to_categorical
from keras.optimizers import SGD, Adam
from keras.callbacks import ReduceLROnPlateau, Ea... | github_jupyter |
### Direct links to results
[TF-MoDISco results](#tfm-results)
[Summary of motifs](#motif-summary)
[TOMTOM matches to motifs](#tomtom)
[Sample of seqlets for each motif](#seqlets)
```
import os
import util
from tomtom import match_motifs_to_database
import viz_sequence
import numpy as np
import pandas as pd
import ... | github_jupyter |
## Rhetorical relations classification used in tree building: ESIM
Prepare data and model-related scripts.
Evaluate models.
Make and evaluate ansembles for ESIM and BiMPM model / ESIM and feature-based model.
Output:
- ``models/relation_predictor_esim/*``
```
%load_ext autoreload
%autoreload 2
import os
import gl... | github_jupyter |
```
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import numpy as np
from IPython.display import Image
# in order to always get the same result
tf.rando... | github_jupyter |
```
%matplotlib inline
```
# MathText WX
Demonstrates how to convert mathtext to a wx.Bitmap for display in various
controls on wxPython.
```
import matplotlib
matplotlib.use("WxAgg")
from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg as FigureCanvas
from matplotlib.backends.backend_wx import Navigati... | github_jupyter |
# Introduction to Python and Natural Language Technologies
__Lecture 01-2, Type system and built-in types__
__Sept 16, 2020__
__Judit รcs__
# Type system
__Dynamic__:
- No need to declare variables
- The `=` operator binds a reference to any arbitrary object
```
i = 2
type(i), id(i)
i = "foo"
type(i), id(i)
```
... | github_jupyter |
```
#include "random.hpp"
#include "xplot/xfigure.hpp"
#include "xplot/xmarks.hpp"
#include "xplot/xaxes.hpp"
```
## Basic Heat map
```
auto data = randn(10, 15);
xpl::linear_scale xs, ys;
xpl::color_scale cs;
xpl::grid_heat_map grid_map(data, xs, ys, cs);
xpl::figure fig1;
fig1.add_mark(grid_map);
fig1.padding_y = 0... | github_jupyter |
ๆฆ็่ฎบๅจ่งฃๅณๆจกๅผ่ฏๅซ้ฎ้ขๆถๅ
ทๆ้่ฆไฝ็จ๏ผๅฎๆฏๆๆๆดๅคๆๆจกๅ็ๅบ็ณใ
ๆฆ็ๅๅธ็ไธไธชไฝ็จๆฏๅจ็ปๅฎๆ้ๆฌก่งๆตx1, . . . , xN็ๅๆไธ๏ผๅฏน้ๆบๅ้x็ๆฆ็ๅๅธp(x)ๅปบๆจกใ่ฟไธช้ฎ้ข่ขซ็งฐไธบๅฏๅบฆไผฐ่ฎก๏ผdensity estimation๏ผใ้ๆฉไธไธชๅ้็ๅๅธไธๆจกๅ้ๆฉ็้ฎ้ข็ธๅ
ณ๏ผ่ฟๆฏๆจกๅผ่ฏๅซ้ขๅ็ไธไธชไธญๅฟ้ฎ้ขใ
## ไบๅ
ๅ้
```
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
```
### 1. ไผฏๅชๅฉๅๅธ
่่ไธไธชไบๅ
้ๆบๅ้$x\in\{0,1\}$๏ผ$x=1$็ๆฆ็่ขซ่ฎฐไฝๅๆฐ$\mu$๏ผๅ ๆญค$p(x=1|\mu)=\... | github_jupyter |
# Lecture 8: Who owns the code? And how can I use it?
## Learning objectives:
By the end of this lecture, students should be able to:
- Explain who owns the copyright of code they write in a give situation, and why
- Choose an appropriate license for software (i.e., packages or analysis code)
- Choose an appropriate ... | github_jupyter |
<a href="https://colab.research.google.com/github/ShrayankM/Covid-19_India_Analysis/blob/master/Covid_19_India_(11_04_2020).ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#Main Covid-19 Data
```
import numpy as np
import pandas as pd
import matplo... | github_jupyter |
# PRMT-1953 Part two: Investigate the impact of data pipeline Duplicate EHR fix on analytics dataset
We re-ran the data pipeline with the Duplicate EHR fix, output is within transfer-sample-5, with a description of what inputs we used to generate the data.
We did a quick analysis of the transfers between Sept 2020-Fe... | github_jupyter |
# Smart insole activity modelling
In this notebook we analyse some time series data taken from the sensors on the insole and attempt to fit a predictive model to determine what kind of activity is being performed.
## Overview
### The data
The [../data](data) consists of timestamped sensor readings from the device. T... | github_jupyter |
Deep Learning with TensorFlow
=============
Credits: Forked from [TensorFlow](https://github.com/tensorflow/tensorflow) by Google
Setup
------------
Refer to the [setup instructions](https://github.com/donnemartin/data-science-ipython-notebooks/tree/feature/deep-learning/deep-learning/tensor-flow-exercises/README.md... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
import pandas as pd
import numpy as np
import json,ast
from scipy.sparse import csr_matrix as csr
# SKLEARN
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.metrics.pairwise import linear_kernel, pairwise_distances... | 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/Pyth... | github_jupyter |
```
from pyspark.sql import SparkSession
import pyspark.sql.functions as f
if not 'spark' in locals():
spark = SparkSession.builder \
.master("local[*]") \
.config("spark.driver.memory","64G") \
.getOrCreate()
spark
```
# Get Data from S3
First we load the data source containing raw weat... | github_jupyter |
# T1531 - Account Access Removal
Adversaries may interrupt availability of system and network resources by inhibiting access to accounts utilized by legitimate users. Accounts may be deleted, locked, or manipulated (ex: changed credentials) to remove access to accounts.
Adversaries may also subsequently log off and/or... | github_jupyter |
# SageMaker Debugger Profiling Report
SageMaker Debugger auto generated this report. You can generate similar reports on all supported training jobs. The report provides summary of training job, system resource usage statistics, framework metrics, rules summary, and detailed analysis from each rule. The graphs and tab... | github_jupyter |
```
"""Testing On Segmentation Task."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import math
import h5py
import argparse
import importlib
import data_utils
import numpy as np
import tensorflow as tf
from datetime import datetime
... | github_jupyter |
# Managing offline map areas
With ArcGIS you can take your web maps and layers offline in field apps to continue work in places with limited or no connectivity. Using [ArcGIS Runtime SDKs](https://developers.arcgis.com/features/offline/), you can bu... | github_jupyter |
# Studio 4: How to Use Neural Networks with Python
In this studio you will learn the basics of building Artifical Neural Netowkrs in Python. You will also learn how to compare different types of modeling tehcniques and find the best tool for the question you are trying to answer.<br>
<br>
Each *P... | github_jupyter |
```
from gurobipy import GRB
from importlib import reload
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import parameters as par
import data
import model
reload(par)
# par = {
# 'model': 'iML1515.json',
# 'biomass_rxn_id': 'BIOMASS_Ec_iML1515_core_75p37M',
# '... | github_jupyter |
# Lista 03 - ICs + Bootstrap
```
# -*- coding: utf 8
from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
plt.style.use('seaborn-colorblind')
plt.ion()
```
# Exercรญcio 01:
Vamos utilizar a base de dados de recรฉm-nascidos disponibilizada no exercรญcio.
```
df = pd.read_csv('baby.csv')
# Conv... | github_jupyter |
```
from __future__ import division
```
## Introduction
We want to generate samples of a given density, $f(x)$. In this case, we can assume we already have a reliable way to generate samples from a uniform distribution, $\mathcal{U}[0,1]$. How do we know a random sample ($v$) comes from the $f(x)$ distribution? One w... | github_jupyter |
# Here we perform regression on the polynomial features dataset
```
import os, sys
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns ; sns.set()
from google.colab import drive
drive.mount('/content/drive')
sys.path.append("/content/drive/MyDrive/GSOC-NMR-project/Work/Notebooks")
from auxillary... | github_jupyter |
[Running a notebook server](https://jupyter-notebook.readthedocs.io/en/stable/public_server.html)
https://jupyter-client.readthedocs.io/en/stable/messaging.html
https://realpython.com/python-sockets/
```
import socket
TCP_IP = '192.168.254.3'
TCP_PORT = 1212
buffer_size = 1024
message = bytes('%R1Q,9081:0,150.0,200.0... | github_jupyter |
## Text Summarization Examples
- Text summarization examples using pretrained transformer models.
- Pororo supports 3 different types of summarization like below.
```
from pororo import Pororo
input_text1 = """๊ฐ์ ๊นํ์ฐ์ ๊ฑธ ๊ทธ๋ฃน ์๋
์๋, ์๋
์๋-ํํฐ์ ๋ฐ ์๋
์๋-Oh!GG์ ๋ฆฌ๋์ด์ ๋ฉ์ธ๋ณด์ปฌ์ด๋ค. 2004๋
SM์์ ์ฃผ์ตํ ์ฒญ์๋
๋ฒ ์คํธ ์ ๋ฐ ๋ํ์์ ๋
ธ๋์งฑ ๋์์ ์์ํ๋ฉฐ SM ์ํฐํ
์ธ๋จผํธ์... | github_jupyter |
# 02 - Introduction to Python for Data Analysis
by [Alejandro Correa Bahnsen](albahnsen.com/)
version 0.2, May 2016
## Part of the class [Machine Learning for Security Informatics](https://github.com/albahnsen/ML_SecurityInformatics)
This notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Un... | github_jupyter |
```
%pylab inline
from IPython.display import Audio
import librosa
import scipy as sp
figsize(20,6)
import tensorflow as tf
tf.enable_eager_execution()
prefix="osc_rect"
def filepre(nm):
return "tmp/"+prefix+"_"+nm
def nrmse(output,target):
combinedVar = 0.5 * (np.var(target, ddof=1) + np.var(output, ddof=... | github_jupyter |
# Tidy Up Web-Scraped Media Franchise Data
## Background
This example combines functionalities of [pyjanitor](https://anaconda.org/conda-forge/pyjanitor) and [pandas-flavor](https://anaconda.org/conda-forge/pandas-flavor) to showcase an explicit--and thus reproducible--workflow enabled by dataframe __method chaining__... | github_jupyter |
**[Machine Learning Micro-Course Home Page](https://www.kaggle.com/learn/intro-to-machine-learning)**
---
## Recap
Here's the code you've written so far.
```
# Code you have previously used to load data
import pandas as pd
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test... | github_jupyter |
```
import os, sys
import json
import collections
import importlib
from typing import *
import numpy as np
import pandas as pd
from sklearn import metrics
import scipy.stats
from Levenshtein import distance as l_dist
from matplotlib import pyplot as plt
import anndata as ad
import scanpy as sc
import torch
import t... | github_jupyter |
# Function
```
"""Text cleansing function which are used very frequently.
Usage:
```
from yellowduck.preprocessing.text import TextCleansing
Using all function
text = TextCleansing.pipeline(my_text)
-Individual-
text = TextCleansing.http_https(text)
text = TextCleansing.new_line(text)
text = TextClean... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
```
datasets.URLs.IMAGENETTE_160
```
## Image ItemList
Previously we were reading in to RAM the whole MNIST dataset ... | github_jupyter |
# Validation on Coco Dataset Based on Light-Head Mask R-CNN
```
"""
Based on the work of Waleed Abdulla (Matterport)
written by github.com/wozhouh
"""
import os
import sys
# Root directory of the project
ROOT_DIR = os.path.abspath("../../../")
# Import Mask RCNN
sys.path.append(ROOT_DIR) # To find local version of... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import math
from numpy.linalg import norm
diff90_threshold = 10 #Angle needs to be within 10 degrees of 90 degrees to qualify as reset point !!Need to test!!
#read in the data
df=pd.read_csv('Data/26_07_15_56_53.csv')
df.head()
#Keep only relev... | github_jupyter |
# Support Vector Machines
Corresponds with modu
```
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import nltk
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import classification_report, confusion_matrix
import re
i... | github_jupyter |
```
%load_ext watermark
%watermark -d -u -a 'Andreas Mueller, Kyle Kastner, Sebastian Raschka' -v -p numpy,scipy,matplotlib
```
The use of watermark (above) is optional, and we use it to keep track of the changes while developing the tutorial material. (You can install this IPython extension via "pip install watermar... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import matplotlib.pyplot as plt
import numpy as np
import os
from time import time
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torch
torch.set_default_tensor_type(torch.DoubleTensor)
import gait
import utils
os.makedirs('./data/mnist', exist... | github_jupyter |
```
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
from math import sqrt
from sklearn.neighbors import KNeighborsRegressor
from sklearn.model_selection import cross_val_score
from sklearn.metrics impor... | github_jupyter |
```
import os, requests, re, time, numpy as np, pandas as pd, pickle
from IPython.display import clear_output
import xmltodict
def get_node_names(parent):
node_names = []
for item in parent.items():
if item[1] != None:
if type(item[1]) == str:
node_names.append(item[0])... | github_jupyter |
# Adding a background to the simple peakbag
I'm going to add a proper treatment of the mode frequencies. The remaining caveats are:
- I will not impose a complex prior on linewidth
- I will not impose a complex prior on mode heights
- I am not accounting for any asphericities due to near-surface magnetic fields
The ... | github_jupyter |
# Name
Data preparation by using a template to submit a job to Cloud Dataflow
# Labels
GCP, Cloud Dataflow, Kubeflow, Pipeline
# Summary
A Kubeflow Pipeline component to prepare data by using a template to submit a job to Cloud Dataflow.
# Details
## Intended use
Use this component when you have a pre-built Cloud D... | github_jupyter |
<img src="../../../images/qiskit-heading.gif" alt="Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook" width="500 px" align="left">
## _*Winning The Game of Magic Square with Quantum Pseudo-Telepathy *_
The latest version of this notebook is available on https:/... | github_jupyter |
# Arbitrage Pricing Theory
By Evgenia "Jenny" Nitishinskaya, Delaney Granizo-Mackenzie, and Maxwell Margenot.
Part of the Quantopian Lecture Series:
* [www.quantopian.com/lectures](https://www.quantopian.com/lectures)
* [github.com/quantopian/research_public](https://github.com/quantopian/research_public)
Notebook ... | github_jupyter |
## ๆฌ ๆๅๅ่ฟๆๅ
```
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(666)
x = np.random.uniform(-3.0, 3.0, size=100)
X = x.reshape(-1, 1)
y = 0.5 * x ** 2 + x + x + np.random.normal(0, 1, size=100)
plt.scatter(x, y)
plt.show()
```
## ไฝฟ็จ็บฟๆงๅๅฝๆฅๆๅ
```
from sklearn.linear_model import LinearRegression
lin_re... | github_jupyter |
# MSTICPy Settings
This notebook takes you through setting up your MSTICPy configuration
for the first time. Some sections are specific to using MSTICPy
with Azure Sentinel.
You must have msticpy installed to run this notebook:
```
%pip install --upgrade msticpy
```
MSTICpy versions >= 1.0.0
```
from msticpy.confi... | github_jupyter |
# Blauth-Arimotho Algorithm
Assuming X and Y as input and output variables of the channel respectively and r(x) is the input distributions. <br>
The capacity of a channel is defined by <br>
$C = \max_{r(x)} I(X;Y) = \max_{r(x)} \sum_{x} \sum_{y} r(x) p(y|x) \log \frac{r(x) p(y|x)}{r(x) \sum_{\tilde{x}} r(\tilde{x})p(y|... | github_jupyter |
# Notebook for plotting figures for generalized KT
## import libraries, and fix plot setting
```
import numpy as np
import numpy.random as npr
import numpy.linalg as npl
from scipy.spatial.distance import pdist
import pathlib
import os
import os.path
import pickle as pkl
# Fitting linear models
import statsmodels.a... | github_jupyter |
```
import sys
sys.path.append('src/')
import numpy as np
import torch, torch.nn
from library_function import library_1D_new
from neural_net import LinNetwork
from DeepMod import DeepMod
import matplotlib.pyplot as plt
plt.style.use('seaborn-notebook')
import torch.nn as nn
from torch.autograd import grad
```
# Prepar... | github_jupyter |
# Linear Regression: Using a Decomposition (Cholesky Method)
--------------------------------
This script will use TensorFlow's function, `tf.cholesky()` to decompose our design matrix and solve for the parameter matrix from linear regression.
For linear regression we are given the system $A \cdot x = y$. Here, $A$ ... | github_jupyter |
# Stock_Analytica
This script imports data from the master data list CSV and analyzes it for significant information.
Project Script PFD:
1. Stock_Query.ipynb
2. Stock_Analytica.ipynb
```
'''
@TODO:
1. consider plt.xticks(rotation=45)
2. replace tickers with company names
3. create standard function for plotting
'''... | github_jupyter |
[](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/7.Clinical_NER_Chunk_Merger.ipynb)
# Clini... | github_jupyter |
# Transfer Learning on CIFAR-10 Dataset
## Introduction
In this tutorial, you learn how to train an image classification model using [Transfer Learning](https://en.wikipedia.org/wiki/Transfer_learning). Transfer learning is a popular machine learning technique that uses a model trained on one problem and applies it ... | github_jupyter |
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