text stringlengths 2.5k 6.39M | kind stringclasses 3
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|---|---|
```
%pylab inline
```
# Генерирование гауссовских случайных процессов
## 1. Генерирование с помощью многомерного нормального вектора
Если вам нужно сгенерировать реализацию гауссовского случайного процесса $X = (X_t)_{t \geqslant 0}$ фиксированной известной (и не слишком большой) длины $n$, можно воспользоваться тем... | github_jupyter |
```
import numpy as np
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
import py21cmfast as p21c
from py21cmfast import global_params
from py21cmfast import plotting
random_seed = 1605
EoR_colour = matplotlib.colors.LinearSegmentedColormap.from_list('mycmap',\
[(0, 'white'),(0.33,... | github_jupyter |
# Maximising the utility of an Open Address
Anthony Beck (GeoLytics), John Daniels (UU), Paul Williams (UU), Dave Pearson (UU), Matt Beare (Beare Essentials)
Go down for licence and other metadata about this presentation
... | github_jupyter |
This notebook was prepared by Marco Guajardo. Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).
# Challenge Notebook
## Problem: Implement a binary search tree with insert, delete, different traversals & max/min node values
* [Constraints](#Constraints)
* [Test Case... | github_jupyter |
```
import numpy as np
import env
import catalog as Cat
import sham_hack as SHAM
import observables as Obvs
import AbundanceMatching as AM
import corner as DFM
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rcParams['text.usetex'] = True
mpl.rcParams['font.family'] = 'serif'
mpl.rcParams['axes.linew... | github_jupyter |
```
# 1
# Load The dataset
import numpy
data = numpy.loadtxt("./data/pima-indians-diabetes.csv", delimiter=",")
X = data[:,0:8]
y = data[:,8]
# 2
# Create the function that returns the keras model
from keras.models import Sequential
from keras.layers import Dense
from keras.regularizers import l2
def build_model(lambda... | github_jupyter |
# Which is the fastest axis of an array?
I'd like to know: which axes of a NumPy array are fastest to access?
```
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
```
## A tiny example
```
a = np.arange(9).reshape(3, 3)
a
' '.join(str(i) for i in a.ravel(order='C'))
' '.join(str(i) for i in a.... | github_jupyter |
## Summary
In this notebook we load a network trained to solve Sudoku puzzles and use this network to solve a single Sudoku.
----
## Imports
```
import functools
import io
import os
import sys
import tempfile
import time
from collections import deque
from pathlib import Path
import ipywidgets as widgets
import num... | github_jupyter |
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID";
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import ktrain
from ktrain import graph as gr
```
# Node Classification in Graphs
In this notebook, we will use *ktrain* to perform node classificaiton on the Cora... | github_jupyter |
# Gaussian Process Example 1 #
The GP model is widely considered at the reference when doing ensemble modeling. This notebook can serve to test the behaviour of GP within the context of scikit learn. In theory, 1 GP should be "an ensemble" in it's own right...so comparison should be made to single GP instances. Basic ... | github_jupyter |
<a href="https://colab.research.google.com/github/deepchatterjeevns/Pytorch-Udacity-Challenge/blob/master/intro-to-pytorch/Part%205%20-%20Inference%20and%20Validation%20(Exercises%20solved).ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import ... | github_jupyter |
# Demonstration notebook for the Pulse of the City project.
In this notebook, you will find examples of how to run the scripts and obtain results from the pedestrian traffic prediction, as well as the spatial interpolation and visualisation systems.
** Index: **
1. [Part 1: Predicting pedestrian traffic](#Part-1:-Pre... | github_jupyter |
## Estimating the coefficient of a regression model via scikit-learn
```
'''
loading the dataset
'''
from data import load_data
import numpy as np
from sklearn.preprocessing import StandardScaler
df = load_data()
X = df[['RM']].values
y = df['MEDV'].values
sc_x = StandardScaler()
sc_y = StandardScaler()
X_std = sc_x.... | github_jupyter |
# San Diego Burrito Analytics: Linear models
Scott Cole
21 May 2016
This notebook attempts to predict the overall rating of a burrito as a linear combination of its dimensions. Interpretation of these models is complicated by the significant correlations between dimensions (such as meat quality and non-meat filling ... | github_jupyter |
## Scaling to Minimum and Maximum values - MinMaxScaling
Minimum and maximum scaling squeezes the values between 0 and 1. It subtracts the minimum value from all the observations, and then divides it by the value range:
X_scaled = (X - X.min / (X.max - X.min)
```
import pandas as pd
# dataset for the demo
from skle... | github_jupyter |
# Land Use/Land Cover
```
import networkx as nx
import osmnx as ox
import pygeohydro as gh
from pynhd import NLDI
```
Land cover, imperviousness, and canopy data can be retrieved from the [NLCD](https://www.mrlc.gov/data) database. First, we use [PyNHD](https://github.com/cheginit/pynhd) to get the contributing water... | github_jupyter |
# Profiling OpenACC Code
This lab is intended for C/C++ programmers. If you prefer to use Fortran, click [this link.](../Fortran/README.ipynb)
You will receive a warning five minutes before the lab instance shuts down. At this point, make sure to save your work! If you are about to run out of time, please see the [Po... | github_jupyter |
<a href="https://colab.research.google.com/github/ayulockin/Explore-NFNet/blob/main/Train_Basline_Cifar10.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
* This is the baseline notebook to setup training a ResNet20 model on Cifar10 dataset.
* Hori... | github_jupyter |
```
# %load defaults.ipy
import numpy as np
import matplotlib
matplotlib.rcParams['savefig.dpi'] = 600
%matplotlib inline
import matplotlib.pyplot as plt
import sys
sys.path.append('../python')
from plot_info import showAndSave, savePlot, get_environment
import plot_info
plot_info.set_notebook_name("WassersteinDistanc... | github_jupyter |
# Reshaping data: Portland housing developments
In this notebook, we're going to work with some data on Portland (Oregon) housing developments since 2014. Right now, the data are scattered across a jillion spreadsheets. Our goal is to parse them all into one clean CSV. (Thanks to [Kelly Kenoyer of the Portland Mercury... | github_jupyter |
# Composing Time Constructions
In this notebook we build and test a Hebrew phrase parser.
```
import sys
import collections
import pickle
import random
import re
import copy
import numpy as np
import networkx as nx
from datetime import datetime
import matplotlib.pyplot as plt
from Levenshtein import distance as lev_d... | github_jupyter |
# Sample testing for DEM & slopes
This is basically a sandbox. By playing with smaller area, eg, a single tile of TMS zoom 10, we can get accurate comparison of approaches.
* The `cut_extent` command will extract from an existing DEM.
* The `slope` command converts to mbtile.
See [gdal_slope_util.py](../src/gdal_slo... | github_jupyter |
# 3D Frangi vesselness measure example
```
import numpy as np
import pyqtgraph as pg
from scipy import ndimage as ndi
import matplotlib.pyplot as plt
% matplotlib inline
%gui qt
import sys
sys.path.append('..')
if sys.version_info >= (3,0):
print("Sorry, requires Python 2.x, not Python 3.x")
import core.frangi as ... | github_jupyter |
# Output part of infinite matter dataframe as LaTeX table
This notebook generates Tables II and III in the Appendix of _Quantifying uncertainties and correlations in the nuclear-matter equation of state_ by [BUQEYE](https://buqeye.github.io/) members Christian Drischler, Jordan Melendez, Dick Furnstahl, and Daniel Phi... | github_jupyter |
## Basic relationship plots
Last time, we played around with plotting the distributions of variables, and comparing distributions to one another. Oftentimes, however two variables intimately related such that knowing a particular value of one variable allows you to predict, to some extent, the value of another variabl... | github_jupyter |
# Table of Contents
<p><div class="lev1"><a href="#Correlation-and-Causation"><span class="toc-item-num">1 </span>Correlation and Causation</a></div><div class="lev1"><a href="#Mortality"><span class="toc-item-num">2 </span>Mortality</a></div><div class="lev1"><a href="#Deciding"><span class="toc... | github_jupyter |
Lambda School Data Science
*Unit 2, Sprint 1, Module 4*
---
# Logistic Regression
- do train/validate/test split
- begin with baselines for classification
- express and explain the intuition and interpretation of Logistic Regression
- use sklearn.linear_model.LogisticRegression to fit and interpret Logistic Regressi... | github_jupyter |
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
from shingle import *
from text import *
import pandas as pd
from sklearn.metrics import f1_score, accuracy_score
from tqdm.notebook import tqdm
import matplotlib.pyplot as plt
import gc
plt.style.use("ggplot")
```
# Utility Functions
```
def merge(texts):
... | github_jupyter |
Following https://medium.com/technovators/machine-learning-based-multi-label-text-classification-9a0e17f88bb4
```
import sys
sys.path.append('/usr/local/lib/python3.9/site-packages')
from sklearn.svm import LinearSVC
from sklearn.multiclass import OneVsRestClassifier
from sklearn.calibration import CalibratedClassifie... | github_jupyter |
# Forecasting Air Passenger using Random Forest
```
import numpy as np
import pandas as pd
import os
import warnings
from copy import copy
import pickle
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_err... | github_jupyter |
https://twitter.com/yujitach/status/1424030835771023363
```
VERSION
]st
Threads.nthreads()
"""
Original
* https://gist.github.com/yujitach/c30d7a174bbc3d3d3e40a3c0f9f9d47f
* TAB を " " で置換
"""
module Original
using LinearAlgebra,LinearMaps
import Arpack
const L=20
diag_ = zeros(Float64,2^L)
function pre... | github_jupyter |
```
import os
import zipfile
import random
import tensorflow as tf
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from shutil import copyfile
# If the URL doesn't work, visit https://www.microsoft.com/en-us/download/confirmation.aspx?id=54765
# And ri... | github_jupyter |
# 18 - K Nearest Neighbors (KNN) - Theory
- Here we will understand the K Nearest Neighbour Algorithm and how to use it for classification problems.
## Reading Assignment
Chapter 4 : Introduction to Statistical Learning (ISLR) By Gareth James, et al.
## What is KNN ?
- K Nearest Neighbors is a classification algor... | github_jupyter |
```
from pyspark.conf import SparkConf
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import BooleanType, IntegerType
from datetime import *
from settings import obtener_timestamp, obtener_dia_semana
""" Configuramos Spark """
conf = SparkConf()
conf.setAppName("Procesam... | github_jupyter |
```
from traitlets.config.manager import BaseJSONConfigManager
# To make this work, replace path with your own:
# On the command line, type juypter --paths to see where your nbconfig is stored
# Should be in the environment in which you install reveal.js
# path = "/Users/jacobperricone/anaconda/envs/py36/bin/jupyter"
#... | github_jupyter |
```
from robotsearch.robots import rct_robot
import numpy as np
import os
import pandas as pd
```
## Prepping CoronaWhy dataset
```
coy_df = pd.read_csv('/media/axhue/WD/Data/Coronawhy/Annotationv2.csv')
def clean_labels(labels):
labels.rename(columns=labels.iloc[0,5:-1].to_dict(),inplace=True)
labels.drop(0... | github_jupyter |
## Looking Through Tree-Ring Data in the Southwestern USA Using Pandas
**Pandas** provides a useful tool for the analysis of tabular data in Python, where previously we would have had to use lists of lists, or use R.
```
## Bringing in necessary pckages
%config InlineBackend.figure_format = 'svg'
%matplotlib inline
i... | github_jupyter |
# Guide
## Quick-start
Let's import our package and define two small lists that we would like to compare in similarity
```
from polyfuzz import PolyFuzz
from_list = ["apple", "apples", "appl", "recal", "house", "similarity"]
to_list = ["apple", "apples", "mouse"]
```
Then, we instantiate our PolyFuzz model and choo... | github_jupyter |
# TV Script Generation
In this project, you'll generate your own [Seinfeld](https://en.wikipedia.org/wiki/Seinfeld) TV scripts using RNNs. You'll be using part of the [Seinfeld dataset](https://www.kaggle.com/thec03u5/seinfeld-chronicles#scripts.csv) of scripts from 9 seasons. The Neural Network you'll build will ge... | github_jupyter |
```
# hide
# all_tutorial
! [ -e /content ] && pip install -Uqq mrl-pypi # upgrade mrl on colab
```
# Tutorial - RL Train Cycle Overview
>Overview of the RL training cycle
## RL Train Cycle Overview
The goal of this tutorial is to walk through the RL fit cycle to familiarize ourselves with the `Events` cycle and g... | github_jupyter |
```
## This script is used to read genomic data (in tabular format) from S3 and store features in SageMaker FeatureStore
import boto3
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import io, os
from time import gmtime, strftime, sleep
import time
import sagemaker
from sagemaker.session import S... | github_jupyter |
## Chemical kinetics
In chemistry one is often interested in how fast a chemical process proceeds. Chemical reactions (when viewed as single events on a molecular scale) are probabilitic. However, most reactive systems of interest involve very large numbers of molecules (a few grams of a simple substance containts on t... | github_jupyter |
# Combine datasets together
```
# Import libraries
import os #operating system
import glob # for reading multiple files
from glob import glob
import pandas as pd #pandas for dataframe management
import matplotlib.pyplot as plt #matplotlib for plotting
import matplotlib.dates as mdates # alias for date formatting
impor... | github_jupyter |
# Additional analyses for manuscript revisions
This notebook contains additional analyses performed for a revised version of the manuscript. In particular, two analyses are performed:
1. Determining whether there is a bias in the linear arrangement of motifs in strong enhancers and silencers.
2. Associating differentia... | github_jupyter |
# LAB: Introdução a Pandas 1
## 1. Introdução
Neste caso usaremos uma versão muito resumida dos dados do [Censo Demográfico (levantamento realizado pelo INDEC)](http://www.indec.gov.ar/bases-de-datos.asp). Trata-se de uma pesquisa contínua cujo objetivo principal é gerar informações sobre o funcionamento do mercado d... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/gdrive')
import os
os.chdir('/content/gdrive/My Drive/finch/tensorflow2/text_classification/imdb/main')
%tensorflow_version 2.x
!pip install tensorflow-addons
import tensorflow as tf
import numpy as np
import pprint
import logging
import time
from tensorflow_add... | github_jupyter |
# 一、数据预处理
```
import pandas as pd
df = pd.read_csv('../data/qualitydata_3/jmt0718withGeoLocation.csv')
print df.shape
print df.columns.values
# print df.dtypes
# print df.describe(include='all')
df.head(10)
# 把REGION和CITY字段为 NaN 的部分填充为 Unknown
df.COUNTRY= df.COUNTRY.fillna('Unknown')
df.REGION= df.REGION.fillna('... | github_jupyter |
# Activity 5: Assembling a Deep Learning System
In this activity, we will train the first version of our LSTM model using Bitcoin daily closing prices. These prices will be organized using the weeks of both 2016 and 2017. We do that because we are interested in predicting the prices of a week's worth of trading.
```
%... | github_jupyter |
# Neural Sequence Distance Embeddings
[](https://colab.research.google.com/github/gcorso/NeuroSEED/blob/master/tutorial/NeuroSEED.ipynb)
The improvement of data-dependent heuristics and representation for biological sequences is a critical requ... | github_jupyter |
## Python Generator
파이썬 제너레이터는 메모리를 효율적으로 사용하면서 반복을 수행하도록 돕는 객체입니다.
제너레이터가 무엇인지 감을 잡기 위해 먼저 다음과 같은 문제를 상상해보겠습니다.
**문제: 특정한 길이의 숫자 배열이 주어졌을 때, 이를 제곱한 수들을 담은 배열을 출력하라**
이를 list를 활용하여 풀면 다음과 같이 풀 수 있습니다.
```
num_count = 10
nums = [i for i in range(num_count)]
print(nums)
def square_list(nums):
result = []
fo... | github_jupyter |
# Dano's CORVO & TPOT notebook
In this notebook, I will try and use TPOT to asses what traditional ML algorithms would be useful to predict cognitive performance from EEG data in Neurodoro
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sklearn as sk
from os import walk
from os impo... | github_jupyter |
# Training and Serving with TensorFlow on Amazon SageMaker
*(This notebook was tested with the \"Python 3 (Data Science)\" kernel.)*
Amazon SageMaker is a fully-managed service that provides developers and data scientists with the ability to build, train, and deploy machine learning (ML) models quickly. Amazon SageMa... | github_jupyter |
# Saving and Loading Models
<a href="https://colab.research.google.com/github/jwangjie/gpytorch/blob/master/examples/00_Basic_Usage/Saving_and_Loading_Models.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
In this bite-sized notebook, we'll go over... | github_jupyter |
# Demonstrate the Sankey class by producing three basic diagrams
Code taken from the [Sankey API](http://matplotlib.org/api/sankey_api.html) at Matplotlib doc
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.sankey import Sankey
```
## Example 1 -- Mostly defaults
This demo... | github_jupyter |
# Amazon SageMaker Workshop
## _**Introduction**_
This workshop has been adapted from an [AWS blog post](https://aws.amazon.com/blogs/ai/predicting-customer-churn-with-amazon-machine-learning/).
Losing customers is costly for any business. Identifying unhappy customers early on gives you a chance to offer them incen... | github_jupyter |
# BOSS: Bag-of-SFA Symbols
* Website: https://www2.informatik.hu-berlin.de/~schaefpa/boss/
* Paper: https://www2.informatik.hu-berlin.de/~schaefpa/boss.pdf
**Note: an Internet connection is required to download the datasets used in this benchmark.**
```
import numpy as np
from pyts.transformation import BOSS
from p... | github_jupyter |
## RetinaNet
Keras-RetinaNet 모델 훈련 및 예측 과정입니다. [keras-retinanet](https://github.com/fizyr/keras-retinanet) 패키지가 필요합니다.
- Tensorflow를 다운로드 및 설치합니다. 2.3.0 이후 버전이 필요합니다.
```
python -m pip install tensorflow
```
- Git 저장소에서 최신 패키지를 다운로드 및 설치합니다.
```
git clone https://github.com/fizyr/keras-retinanet.git
cd keras-... | github_jupyter |
```
# default_exp timeseries.data
```
# timeseries.data
> API details.
```
#export
from fastai.torch_basics import *
from fastai.data.all import *
from fastai.tabular.data import *
from fastai.tabular.core import *
from fastrenewables.tabular.core import *
from fastrenewables.timeseries.core import *
import glob
#hi... | github_jupyter |
# Real-world data analysis example: PPC Campaign Performance
In the following example, we will load and analyze a generated set of data. The dataset is almost in the same format as could be obtained from AdWords using its reporting API, but the data itself is completely generated and any similarities with any existing... | github_jupyter |
# High-level RNN TF Example
```
import numpy as np
import os
import sys
import tensorflow as tf
from common.params_lstm import *
from common.utils import *
# Force one-gpu
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
print("OS: ", sys.platform)
print("Python: ", sys.version)
print("Numpy: ", np.__version__)
print("Tensorf... | github_jupyter |
```
!pip install gluoncv
import boto3
from IPython.display import clear_output, Image, display, HTML
import numpy as np
import cv2
import base64
from bokeh.plotting import figure
from bokeh.io import output_notebook, show, push_notebook
import time
import json
output_notebook()
STREAM_NAME = "pi4-001"
kvs = boto3.clien... | github_jupyter |
# Effect of the sample size in cross-validation
In the previous notebook, we presented the general cross-validation framework
and how to assess if a predictive model is underfiting, overfitting, or
generalizing. Besides these aspects, it is also important to understand how
the different errors are influenced by the nu... | github_jupyter |
<!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="fig/cover-small.jpg">
*This notebook contains an excerpt from the [Whirlwind Tour of Python](http://www.oreilly.com/programming/free/a-whirlwind-tour-of-python.csp) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jak... | github_jupyter |
# Prerequisites
Install the `OpenPAI` sdk from `github` and specify your cluster information in `~/.openpai/clusters.yaml`.
And for simplicity and security, we recommand user to setup necessary information in `.openpai/defaults.json` other than shown in the example notebook. (Refer to for [README](https://github.com/... | github_jupyter |
# Riemann Staircase
A notebook to caclulate functions to visualise the prime staircase using Riemann's formula.
```
from mpmath import *
from sympy import mobius
import numpy as np
import matplotlib.pyplot as plt
from tqdm.notebook import trange
mp.dps = 30; mp.pretty = True
def Li(x, rho=1):
return ei(rho * log(... | github_jupyter |
# Foundations of Computational Economics #12
by Fedor Iskhakov, ANU
<img src="_static/img/dag3logo.png" style="width:256px;">
## Enumeration of discrete compositions
<img src="_static/img/lab.png" style="width:64px;">
<img src="_static/img/youtube.png" style="width:65px;">
[https://youtu.be/eU2WRHBTFBw](https://y... | github_jupyter |
**A high-level plotting API for the PyData ecosystem built on HoloViews.**
<img src="./assets/diagram.png" width="70%"></img>
The PyData ecosystem has a number of core Python data containers that allow users to work with a wide array of datatypes, including:
* [Pandas](https://pandas.pydata.org): DataFrame, Series ... | github_jupyter |
Face detection with OpenCV isn't something new or complicated. There is however the aspect of face recognition. Combining all of that plus some PIL image processing we can make a fun machine vision app.
```
import pytest
import ipytest
ipytest.autoconfig()
```
### Detect faces, draw memes
```
import os
import cv2
... | github_jupyter |
### Test create data func
```
!dvc pull ../data/observations_ad_0.0.pickle.dvc
import sys
sys.path.append('../src')
import yaml
import math
import pickle
import numpy as np
from pickle_wrapper import unpickle, pickle_it
import matplotlib.pyplot as plt
from pickle_wrapper import unpickle, pickle_it
from mcmc_norm_le... | github_jupyter |
# datasets
```
import h5py
import cupy as cp
#加载数据的function
def load_dataset():
train_dataset = h5py.File('../datasets/train_signs.h5', "r")
train_set_x_orig = cp.array(train_dataset["train_set_x"][:]) # your train set features
train_set_y_orig = cp.array(train_dataset["train_set_y"][:]) # your train set... | github_jupyter |
### VQE（Variational quantum eigensolver）
パラメータ付き量子回路で変分的に基底状態を求めましょう。
### 必要なライブラリをインポート
```
from sympy import *
from sympy.physics.quantum import *
from sympy.physics.quantum.qubit import Qubit,QubitBra,measure_all,measure_partial
from sympy.physics.quantum.gate import X,Y,Z,H,CNOT,SWAP,CPHASE,CGateS
from sympy.phys... | github_jupyter |
# load package and settings
```
import cv2
import sys
import dlib
import time
import socket
import struct
import numpy as np
import tensorflow as tf
from win32api import GetSystemMetrics
import win32gui
from threading import Thread, Lock
import multiprocessing as mp
from config import get_config
import pickle
import ... | github_jupyter |
**author**: lukethompson@gmail.com<br>
**date**: 9 Oct 2017<br>
**language**: Python 3.5<br>
**license**: BSD3<br>
## physicochemical_pairplot.ipynb
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
from empcolors import get_empo_cat_color
%matplotlib inline
p... | github_jupyter |
# Improving generalization with regularizers and constraints
Neural networks usually have a very large number of parameters, which may lead to overfitting in many cases (especially when you do not have a large dataset). There's a large number of methods for regularization, and here we cover the most usual ones which a... | github_jupyter |
## astropy.wcs
Implements the FITS WCS standard and some commonly used distortion conventions.
This tutorial will show how to create a WCS object from a FITS file and how to use it to transform coordinates.
```
import numpy as np
%matplotlib inline
from matplotlib import pyplot as plt
import os
from astropy.io import... | github_jupyter |
# Stack
This chapter will cover the basics of stack.
Let's import the necessary libraries for our code to run.
```
import java.util.*;
import java.io.*;
```
## Section 1. The Basics of Stack
A stack is a collection based on the principle of adding elements and retrieving them in the opposite order.
- Last-In, Fir... | github_jupyter |
```
import pandas as pd
import os
os.chdir('..')
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import numpy as np
import requests
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
%matplotlib inline
url = "https://en.wikivoyage.org/w/api.php?format=json... | github_jupyter |
```
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.feature.HashingTF
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.MLUtils
import com.amazonaws.services.sagemaker.sparksdk.IAMRole
import com.amazonaws.services.sagemaker.sparksdk.algorithms.XG... | github_jupyter |
# Identify DOSTA Sensors with Missing Two-Point Calibrations
During a review of the dissolved oxygen data, it was discovered that there was an error in how the instrument calibration coefficients were being applied. The two-point calibration values, supplied by the vendor if a multipoint calibration was not warranted,... | github_jupyter |
# Logistic Regression
Here is logistic regression to sats.csv. We have 3 collumns, exam 1 , exam 2 and if it's submitted.
#### Initialize
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
df=pd.read_csv("sats.csv")
X=df.iloc[:,:-1].values
y=df.iloc[:,-1].values
df.head()
df.describe()
```
#... | github_jupyter |
<i>Copyright (c) Microsoft Corporation. All rights reserved.<br>
Licensed under the MIT License.</i>
<br>
# Model Comparison for NCF Using the Neural Network Intelligence Toolkit
This notebook shows how to use the **[Neural Network Intelligence](https://nni.readthedocs.io/en/latest/) toolkit (NNI)** for tuning hyperpa... | github_jupyter |
## Adding the required Libraries
```
import numpy as np
import pandas as pd
pd.set_option('display.max_columns',None)
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.colors as colors
import seaborn as sns
import nltk
from nltk.tokenize import sent_tokenize
from nltk.corpus import words
... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
from struct import unpack
from sklearn import cluster
import datetime
import hdbscan
import seaborn as sns
from sklearn.preprocessing import PowerTransformer, normalize, MinMaxScaler, StandardScaler
from tsnecuda import TSNE
from struct import pack
from sklearn_ext... | github_jupyter |
# 목차
## 1. 데이터 분석에 앞서 (워밍업)
1-1. 분석의 목적
1-2. 요구 조건 정의
## 2. 통계의 기초
2-1. 평균과 표준편차
2-1-1. 대표값
2-1-2. 모집단과 표본
2-1-3. Random Sampling
2-2. 기술통계 추론통계
2-2-1. 기술통계
2-2-2. 추론통계
2-3. EDA
2-3-1. Visualization
2-3-2. 중심극한정리
2-4. 점추정과 구간추정
2-... | github_jupyter |
# Load data
```
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
np.set_printoptions(precision=3, linewidth=120)
import sys
sys.path.append("..")
from scem import ebm, stein, kernel, util, gen
from scem.datasets import *
import matplotlib.pyplot as plt
from tqdm import notebook as ... | github_jupyter |
<a href="https://colab.research.google.com/github/csaybar/EarthEngineMasterGIS/blob/master/module06/04_RUSLE.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<!--COURSE_INFORMATION-->
<img align="left" style="padding-right:10px;" src="https://user-im... | github_jupyter |
## Cleaning up Data
Sometimes data comes to us in a form that requires some cleaning before we can begin with further analyses. In this exercise we will explore some tools and strategies for that.
We'll begin by reading in a modified version of the Ithaca climate dataset that we worked with previously. You should n... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import csv
import itertools
import os
from dataclasses import dataclass
from datetime import datetime
import numpy as np
import pandas as pd
from func_timeout import FunctionTimedOut, func_timeout
from sklearn.metrics import accuracy_score
from sklearn.svm import LinearSVC
from t... | github_jupyter |
# Work with Data
Data is the foundation on which machine learning models are built. Managing data centrally in the cloud, and making it accessible to teams of data scientists who are running experiments and training models on multiple workstations and compute targets is an important part of any professional data scien... | github_jupyter |
# Import Packages
```
import os
import numpy as np
import matplotlib.pyplot as plt
import quantities as pq
import neo
from neurotic._elephant_tools import CausalAlphaKernel, instantaneous_rate
pq.markup.config.use_unicode = True # allow symbols like mu for micro in output
pq.mN = pq.UnitQuantity('millinewton', pq.N/... | github_jupyter |
# BERT: As one of Autoencoding Language Models
```
import os
from google.colab import drive
drive.mount('/content/drive')
!pip install transformers
!pip install tokenizers
os.chdir("drive/My Drive/data/")
os.listdir()
import pandas as pd
imdb_df = pd.read_csv("IMDB Dataset.csv")
reviews = imdb_df.review.to_string(ind... | github_jupyter |
```
import torch
import torch.nn as nn
from collections import OrderedDict
import shutil
import time
import gzip
import os
import json
import numpy as np
from dpp_nets.utils.io import make_embd, make_tensor_dataset, load_tensor_dataset
from dpp_nets.utils.io import data_iterator, load_embd
from torch.autograd import Va... | github_jupyter |
# Monitoring Data Drift
Over time, models can become less effective at predicting accurately due to changing trends in feature data. This phenomenon is known as *data drift*, and it's important to monitor your machine learning solution to detect it so you can retrain your models if necessary.
In this lab, you'll conf... | github_jupyter |
```
# set tf 1.x for colab
%tensorflow_version 1.x
# setup only for running on google colab
# ! shred -u setup_google_colab.py
! wget https://raw.githubusercontent.com/hse-aml/intro-to-dl/master/setup_google_colab.py -O setup_google_colab.py
import setup_google_colab
# please, uncomment the week you're working on
# se... | github_jupyter |
# Imports
```
import os, re, sys, pickle, datetime
import itertools
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import pandas as pd
from scipy import stats
from sklearn import metrics
from sklearn.metrics import confusion_matrix,f1_score
from sklearn.model_selection... | github_jupyter |
<div class="alert alert-block alert-info" style="margin-top: 20px">
<a href="http://cocl.us/NotebooksPython101"><img src = "https://ibm.box.com/shared/static/yfe6h4az47ktg2mm9h05wby2n7e8kei3.png" width = 750, align = "center"></a>
<a href="https://www.bigdatauniversity.com"><img src = "https://ibm.box.com/shared/sta... | github_jupyter |
# Template File
The data is given
in CSV form precisely as would be given in data files. For each table started by the cell
magic `%%Table`, the table name follows immediately.
```
from Frame2D import Frame2D
theframe = Frame2D('Template')
```
# Input Data
## Nodes
Table `no... | github_jupyter |
[](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/streamlit_notebooks/TEXT_FINDER_EN.ipynb)
# **Find words/phrases in text us... | github_jupyter |
# Genre recognition: experiment
Goal:
Conclusion:
Observations:
## Hyper-parameters
### Parameter under test
```
Pname = 'lg'
Pvalues = [1, 10, 100]
# Regenerate the graph or the features at each iteration.
regen_graph = False
regen_features = True
regen_baseline = False
```
### Model parameters
```
p = {}
# ... | github_jupyter |
## Boulder Watershed Demo
Process ATL03 data from the Boulder Watershed region and produce a customized ATL06 elevation dataset.
### What is demonstrated
* The `icesat2.atl06p` API is used to perform a SlideRule parallel processing request of the Boulder Watershed region
* The `matplotlib` and `cartopy` packages are... | github_jupyter |
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