code stringlengths 2.5k 150k | kind stringclasses 1
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|---|---|
# CA Coronavirus Cases and Deaths Trends
CA's [Blueprint for a Safer Economy](https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/COVID19CountyMonitoringOverview.aspx) assigns each county [to a tier](https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/COVID19CountyMonitoringOverview.aspx) based on case rate ... | github_jupyter |
```
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
sess_config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
np.random.seed(219)
tf.set_random_seed(219)
# Load training and eval data from tf.ker... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import json
import tensorflow as tf
import nltk
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequence... | github_jupyter |
# Simulators
## Introduction
This notebook shows how to import the *Qiskit Aer* simulator backend and use it to run ideal (noise free) Qiskit Terra circuits.
```
import numpy as np
# Import Qiskit
from qiskit import QuantumCircuit
from qiskit import Aer, transpile
from qiskit.tools.visualization import plot_histogr... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Train and deploy a model
_**Create and d... | github_jupyter |
# Setup
```
!pip install git+https://github.com/hafidhrendyanto/gpt2-absa.git
```
# Code Sample
```
from gpt2absa.constant import restaurant_aspect_categories, laptop_aspect_categories
from gpt2absa import aspect_polarity_pair
from transformers import TFAutoModelWithLMHead
model = TFAutoModelWithLMHead.from_pretrain... | github_jupyter |
# Regression using Decision Trees
In this notebook, we will use decision trees to solve regression problems.
The dataset used here originates from a project to build a surrogate model for predicting the band gap of a material from its composition. This surrogate model was used to replace expensive qunatum mecahnical... | github_jupyter |
# Exercise 13. Nonparametric tests, goodness-of-fit tests
## Michal Béreš, Martina Litschmannová, Adéla Vrtková
# Conformance distribution probability testing of discrete NV(finite number of values) - good agreement test
- we test whether the measured data(their relative frequencies) agree with any specific distrib... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
import os
import tensorflow as tf
import tensorflow_datasets as tfds
print("TensorFlow version:", tf.__version__)
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
(x_train_... | github_jupyter |
```
repo_directory = '/Users/iaincarmichael/Dropbox/Research/law/law-net/'
data_dir = '/Users/iaincarmichael/Documents/courtlistener/data/'
import numpy as np
import sys
import matplotlib.pyplot as plt
from scipy.stats import rankdata
from collections import Counter
# graph package
import igraph as ig
# our code
sy... | github_jupyter |
# Distance Based Statistical Method for Planar Point Patterns
**Authors: Serge Rey <sjsrey@gmail.com> and Wei Kang <weikang9009@gmail.com>**
## Introduction
Distance based methods for point patterns are of three types:
* [Mean Nearest Neighbor Distance Statistics](#Mean-Nearest-Neighbor-Distance-Statistics)
* [Near... | github_jupyter |
<a href="https://colab.research.google.com/github/Amro-source/Deep-Learning/blob/main/imageprocessing1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import numpy as np
def im2double(im):
min_val = np.min(im.ravel())
max_val = np.max(i... | github_jupyter |
```
import pandas as pd
from datetime import datetime
from _lib.data_preparation import remove_substandard_trips, df_calc_basic, df_join_generic_with_gps, read_gpx, calc_context
from _lib.data_preparation import get_df_detail_final, get_df_generic_final
from _lib.helper import val2year, val2zip, val2utf8, get_filepath... | github_jupyter |
<a href="https://www.bigdatauniversity.com"><img src="https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png" width="400" align="center"></a>
<h1 align="center"><font size="5">Classification with Python</font></h1>
In this notebook we try to practice all the classification algorithms that we learned i... | github_jupyter |
```
# 사용할 데이터 import
import pandas as pd
df = pd.read_csv('https://archive.ics.uci.edu/ml/'
'machine-learning-databases'
'/breast-cancer-wisconsin/wdbc.data', header=None)
from sklearn.preprocessing import LabelEncoder
X = df.loc[:, 2:].values
y = df.loc[:, 1].values
le = LabelEnco... | github_jupyter |
```
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.3
tf.Session(config=config)
import keras
from keras.models import *
from keras.layers import *
from keras import optimizers
from keras.applications.resnet50 import ResNet5... | github_jupyter |
```
import ase
import numpy as np
import cPickle as pck
from ase.visualize import view
import quippy as qp
def qp2ase(qpatoms):
from ase import Atoms as aseAtoms
positions = qpatoms.get_positions()
cell = qpatoms.get_cell()
numbers = qpatoms.get_atomic_numbers()
pbc = qpatoms.get_pbc()
atoms = a... | github_jupyter |
```
%matplotlib inline
import csv
import random
import numpy as np
from sklearn.feature_extraction.text import *
import pickle
import tensorflow as tf
import nn_model
from sklearn.metrics import label_ranking_loss
from collections import Counter, defaultdict
import matplotlib.pyplot as plt
from operator import itemgett... | github_jupyter |
# Loss and Regularization
```
%load_ext autoreload
%autoreload 2
import numpy as np
from numpy import linalg as nplin
from cs771 import plotData as pd
from cs771 import optLib as opt
from sklearn import linear_model
from matplotlib import pyplot as plt
from matplotlib.ticker import MaxNLocator
import random
```
**Loa... | github_jupyter |
# Import statements
```
from google.colab import drive
drive.mount('/content/drive')
from my_ml_lib import MetricTools, PlotTools
import os
import numpy as np
import matplotlib.pyplot as plt
import pickle
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import json
import dat... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from utils import get_ts
from warnings import simplefilter
simplefilter("ignore")
df = get_ts(coin="nexo",days=500)
df.head(3)
# Set Matplotlib defaults
plt.style.use("seaborn-whitegrid")
plt.rc("figure", autolayout=True, f... | github_jupyter |
# Planar data classification with one hidden layer
Welcome to your week 3 programming assignment! It's time to build your first neural network, which will have one hidden layer. Now, you'll notice a big difference between this model and the one you implemented previously using logistic regression.
By the end of this ... | github_jupyter |
#### From Quarks to Cosmos with AI: Tutorial Day 4
---
# Field-level cosmological inference with IMNN + DELFI
by Lucas Makinen [<img src="https://raw.githubusercontent.com/tlmakinen/FieldIMNNs/master/tutorial/plots/Orcid-ID.png" alt="drawing" width="20"/>](https://orcid.org/0000-0002-3795-6933 "") [<img src="https://r... | github_jupyter |
# Task 6: Regularisation
_All credit for this jupyter notebook tutorial goes to the book "Hands-On Machine Learning with Scikit-Learn & TensorFlow" by Aurelien Geron. Modifications were made in preparation for the hands-on sessions._
# Setup
First, let's import a few common modules, ensure MatplotLib plots figures i... | github_jupyter |
```
#hide
from perutils.nbutils import simple_export_all_nb,simple_export_one_nb
```
# Personal Utils (perutils)
> Notebook -> module conversion with #export flags and nothing else
**Purpose:** The purpose and main use of this module is for adhoc projects where a full blown nbdev project is not necessary
**Exampl... | github_jupyter |
```
import pandas as pd
```
# Import Data
```
schiz_pre = pd.read_csv('data/schizophrenia_pre_features_tfidf_256.csv')
schiz_post = pd.read_csv('data/schizophrenia_post_features_tfidf_256.csv')
```
# High-Level Look at Datasets
## Preface
These datasets contain a very large number of features. For this project we ... | github_jupyter |
<a href="https://colab.research.google.com/github/BrianThomasRoss/DS-Unit-2-Linear-Models/blob/master/module3-ridge-regression/Brian_Ross_LS_DS_213_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, Sprin... | github_jupyter |
# Supervised Stylometric Analysis of the Pentateuch
### Table of Contents
1. [Introduction](#intro)
2. [Preprocess Data](#preprocess)
3. [Embedding Experimentation](#embed)
4. [Results](#results)
<a name='intro'></a>
### 1. Introduction
Modern biblical scholarship holds that the Pentateuch, also known as the To... | github_jupyter |
```
import tensorflow as tf
import os
import numpy as np
import ujson as json
from importlib import reload
from scipy import stats
from func import cudnn_gru, native_gru, dot_attention, summ, ptr_net
from prepro import word_tokenize, convert_idx
import inference
# reload(inference.InfModel)
# reload(inference.Inferen... | github_jupyter |
<a href="https://colab.research.google.com/github/gordeli/textanalysis/blob/master/03_Data_Collection_DS3Text.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#Fundamentals of Text Analysis for User Generated Content @ EDHEC, 2021
# Part 3: Data Col... | github_jupyter |
```
import boto3
import botocore
import os
import sagemaker
bucket = sagemaker.Session().default_bucket()
prefix = "sagemaker/ipinsights-tutorial"
execution_role = sagemaker.get_execution_role()
region = boto3.Session().region_name
# check if the bucket exists
try:
boto3.Session().client("s3").head_bucket(Bucket... | github_jupyter |
# Table of Contents
<p><div class="lev1 toc-item"><a href="#ALGO1-:-Introduction-à-l'algorithmique" data-toc-modified-id="ALGO1-:-Introduction-à-l'algorithmique-1"><span class="toc-item-num">1 </span><a href="https://perso.crans.org/besson/teach/info1_algo1_2019/" target="_blank">ALGO1 : Introduction à l'al... | github_jupyter |
```
import pandas as pd
import numpy as np
```
## Load data from csv file
```
names = ['CRIM','ZN','INDUS','CHAS','NOX','RM','AGE','DIS','RAD','TAX','PTRATIO','B','LSTAT','PRICE']
df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data',
header=None, names=name... | github_jupyter |
```
from copy import deepcopy
import json
import pandas as pd
DATA_DIR = 'data'
# Define template payloads
CS_TEMPLATE = {
'resourceType': 'CodeSystem',
'status': 'draft',
'experimental': False,
'hierarchyMeaning': 'is-a',
'compositional': False,
'content': 'fragment',
'concept': []
}
```
... | github_jupyter |
## Caroline's raw material planning
<img align='right' src='https://drive.google.com/uc?export=view&id=1FYTs46ptGHrOaUMEi5BzePH9Gl3YM_2C' width=200>
As we know, BIM produces logic and memory chips using copper, silicon, germanium and plastic.
Each chip has the following consumption of materials:
| chip | copper... | github_jupyter |
## AutoGraph: examples of simple algorithms
This notebook shows how you can use AutoGraph to compile simple algorithms and run them in TensorFlow.
It requires the nightly build of TensorFlow, which is installed below.
```
!pip install -U -q tf-nightly-2.0-preview
import tensorflow as tf
tf = tf.compat.v2
tf.enable_... | github_jupyter |
# T81-558: Applications of Deep Neural Networks
**Module 7: Generative Adversarial Networks**
* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)
* For more information visit the... | github_jupyter |
#### Copyright 2017 Google LLC.
```
# 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 agreed to in writin... | github_jupyter |
```
#@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 agreed to in writing, software
# distributed u... | github_jupyter |
```
!env | grep -i python
! which python
! which pip
! pip install catboost
from catboost import CatBoostClassifier
```
A fork of `catboost-go-5.0-subset.ipynb` where we exclude run ID and study ID from the features
```
!pip install --user catboost ipywidgets
!conda install -y python-graphviz
!jupyter nbextension ena... | github_jupyter |
```
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
import gc
import time
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from sklearn.metrics import roc_auc_score
from nltk.tokenize ... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import utils
matplotlib.rcParams['figure.figsize'] = (0.89 * 12, 6)
matplotlib.rcParams['lines.linewidth'] = 10
matplotlib.rcParams['lines.markersize'] = 20
```
# The Dataset
$$y = x^3 + x^2 - 4x$$
```
x, y, X, transform, sc... | github_jupyter |
```
#######################################################
# Script:
# trainPerf.py
# Usage:
# python trainPerf.py <input_file> <output_file>
# Description:
# Build the prediction model based on training data
# Pass 1: prediction based on hours in a week
# Authors:
# Jasmin Nakic, jnakic@salesforce.com
... | github_jupyter |
# Rotation Transformation
We meta-learn how to rotate images so that we can accurately classify rotated images. We use MNIST.
Import relevant packages
```
from operator import mul
from itertools import cycle
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.backends.cudnn... | github_jupyter |
# Daily Load Profile Timeseries Clustering Evaluation
```
import pandas as pd
import numpy as np
import datetime as dt
import os
from math import ceil, log
import plotly.plotly as py
import plotly.offline as po
import plotly.graph_objs as go
import plotly.figure_factory as ff
import plotly.tools as tools
import color... | github_jupyter |
```
import pandas as pd
import numpy as np
data = {
'color': [ 'blue', 'green', 'yellow', 'red', 'white' ],
'object': ['ball', 'pen', 'pencil', 'paper', 'mug'],
'price': [ 1.2, 1.0, 0.6, 0.9, 1.7 ]
}
frame = pd.DataFrame(data)
frame
frame2 = pd.DataFrame(data, columns=['object', 'price'])
frame2
frame2 = pd... | github_jupyter |
In Ipython Notebook, I can write down the mathmatical expression with latex, which allows me to understand my codes better.
## q_3 word2vec.py
```
import numpy as np
import random
from q1_softmax import softmax
from q2_gradcheck import gradcheck_naive
from q2_sigmoid import sigmoid, sigmoid_grad
def normalizeRows(x)... | github_jupyter |
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-59152712-8"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-59152712-8');
</script>
# Enforce conformal 3-metric $\det{\bar{\gamma}_{ij}}=\det{... | github_jupyter |
<div align="center"><h1>Perspectives on Text</h1>
<h3>_Synthesizing Textual Knowledge through Markup_</h3>
<br/>
<h4>Elli Bleeker, Bram Buitendijk, Ronald Haentjens Dekker, Astrid Kulsdom
<br/>R&D - Dutch Royal Academy of Arts and Science</h4>
<h6>Computational Methods for Literary Historical Textual Schola... | github_jupyter |
Miscilanous plots of spectra.
```
#first get the python modules we need
import numpy as np
import matplotlib.pyplot as plt
import astropy.io.fits as fits
import os
import glob
from astropy.convolution import convolve, Box1DKernel
from astropy.table import Table
import astropy.units as u
from astropy.modeling import mo... | github_jupyter |
# Project description
- Beta Bank customers are leaving: little by little, chipping away every month. The bankers figured out it’s cheaper to save the existing customers rather than to attract new ones.
- We need to predict whether a customer will leave the bank soon. You have the data on clients’ past behavior and t... | github_jupyter |
# Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and... | github_jupyter |
# Lab 1
Second section is kind of exploring the subject. The proper homework is presented in last two sections, showing differences similarities for different parameters (noise and function).
## Simulation preparation
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#import seaborn as sns ... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
import pickle
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
# functions
def rem_outliers(df, col):
''' Remove outl... | github_jupyter |
# Import
```
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
from torch.utils.data import TensorDataset, Dataset, DataLoader, random_split
from torch.nn.utils.rnn import pack_padded_sequence, pack_sequence, pad_packed_sequence, pad_sequ... | github_jupyter |
# NLP - Hotel review sentiment analysis in python
```
#warnings :)
import warnings
warnings.filterwarnings('ignore')
import os
dir_Path = 'D:\\01_DATA_SCIENCE_FINAL\\D-00000-NLP\\NLP-CODES\\AMAN-NLP-CODES\\AMAN_NLP_VIMP-CODE\\Project-6_Sentiment_Analysis_Amn\\'
os.chdir(dir_Path)
```
## Data Facts and Import
```
im... | github_jupyter |
## TrainingPhase and General scheduler
Creates a scheduler that lets you train a model with following different [`TrainingPhase`](/callbacks.general_sched.html#TrainingPhase).
```
from fastai.gen_doc.nbdoc import *
from fastai.callbacks.general_sched import *
from fastai.vision import *
show_doc(TrainingPhase)
```
... | github_jupyter |
## Problem Definition
In the following different ways of loading or implementing an optimization problem in our framework are discussed.
### By Class
A very detailed description of defining a problem through a class is already provided in the [Getting Started Guide](../getting_started.ipynb).
The following definitio... | github_jupyter |
```
import os, platform, pprint, sys
import fastai
import keras
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
import sklearn
# from fastai.tabular.data import TabularDataLoaders
# from fastai.tabular.all import FillMissing, Categorify, N... | github_jupyter |
# Using AWS Lambda and PyWren for Landsat 8 Time Series
This notebook is a simple demonstration of drilling a timeseries of NDVI values from the [Landsat 8 scenes held on AWS](https://landsatonaws.com/)
### Credits
- NDVI PyWren - [Peter Scarth](mailto:p.scarth@uq.edu.au?subject=AWS%20Lambda%20and%20PyWren) (Joint Rem... | github_jupyter |
```
import xgboost as xgb
import pandas as pd
# 読み出し
data = pd.read_pickle('data.pkl')
nomination_onehot = pd.read_pickle('nomination_onehot.pkl')
selected_performers_onehot = pd.read_pickle('selected_performers_onehot.pkl')
selected_directors_onehot = pd.read_pickle('selected_directors_onehot.pkl')
selected_studio_one... | github_jupyter |
```
%tensorflow_version 2.x
import tensorflow as tf
#from tf.keras.models import Sequential
#from tf.keras.layers import Dense
import os
import io
tf.__version__
```
# Download Data
```
# Download the zip file
path_to_zip = tf.keras.utils.get_file("smsspamcollection.zip",
origin="https://archive.ic... | github_jupyter |
```
import os
from skimage.filters.rank import median
import numpy as np
import matplotlib.pyplot as plt
import skimage.data as data
import skimage.segmentation as seg
import skimage.filters as filters
import skimage.draw as draw
import skimage.color as color
from scipy.ndimage.filters import convolve
from skimage.fil... | github_jupyter |
# 3D Partially coherent ODT forward simulation
This forward simulation is based on the SEAGLE paper ([here](https://ieeexplore.ieee.org/abstract/document/8074742)): <br>
```H.-Y. Liu, D. Liu, H. Mansour, P. T. Boufounos, L. Waller, and U. S. Kamilov, "SEAGLE: Sparsity-Driven Image Reconstruction Under Multiple Scatteri... | github_jupyter |
```
#default_exp fastai.dataloader
```
# DataLoader Errors
> Errors and exceptions for any step of the `DataLoader` process
This includes `after_item`, `after_batch`, and collating. Anything in relation to the `Datasets` or anything before the `DataLoader` process can be found in `fastdebug.fastai.dataset`
```
#expo... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(5)
y = x
t = x
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.scatter(x, y, c=t, cmap='viridis')
ax2.scatter(x, y, c=t, cmap='viridis_r')
color = "red"
plt.scatter(x, y, c=color)
sequence_of_colors = ["red", "orange", "yellow", "green", "blue","red", "ora... | github_jupyter |
---
_You are currently looking at **version 1.1** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-machine-learning/resources/bANLa) course resource._
---
## Assignment 4 - ... | github_jupyter |
```
# ==============================================================================
# Copyright 2021 Google LLC. This software is provided as-is, without warranty
# or representation for any use or purpose. Your use of it is subject to your
# agreement with Google.
# ===================================================... | github_jupyter |
# Hertzian conatct 1
## Assumptions
When two objects are brought into contact they intially touch along a line or at a single point. If any load is transmitted throught the contact the point or line grows to an area. The size of this area, the pressure distribtion inside it and the resulting stresses in each solid req... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy
from numpy import genfromtxt
import csv
import pandas as pd
from operator import itemgetter
from datetime import*
from openpyxl import load_workbook,Workbook
from openpyxl.styles import PatternFill, Border, Side, Alignment, Protection, Font
import openpyxl
from win32com ... | github_jupyter |
<center>
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png" width="300" alt="cognitiveclass.ai logo" />
</center>
# Loops in Python
Estimated time needed: **20** minutes
## Objectives
After completing this lab you will be ab... | github_jupyter |
```
import json
import requests
import numpy as np
import pandas as pd
import pandas as pd
import requests
from requests.auth import HTTPBasicAuth
USERNAME = 'damminhtien'
PASSWORD = '**********'
TARGET_USER = 'damminhtien'
authentication = HTTPBasicAuth(USERNAME, PASSWORD)
import uuid
from IPython.display import dis... | github_jupyter |
<a href="https://colab.research.google.com/github/trucabrac/blob_Jan2022/blob/main/Blob_batch_processing.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import numpy as np
import cv2
import os
import glob
from skimage.filters import gaussian
fro... | github_jupyter |
```
import numpy as np
import pandas as pd
import scipy as sp
import sklearn as sl
import seaborn as sns; sns.set()
import matplotlib as mpl
from sklearn.linear_model import LinearRegression
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import axes3d
from matplotlib import cm
%matplotlib inline
```
# ... | github_jupyter |
# Missing Data
Missing values are a common problem within datasets. Data can be missing for a number of reasons, including tool/sensor failure, data vintage, telemetry issues, stick and pull, and omissing by choice.
There are a number of tools we can use to identify missing data, some of these methods include:
- Pa... | github_jupyter |
# Applying Chords to 2D and 3D Images
## Importing packages
```
import time
import porespy as ps
ps.visualization.set_mpl_style()
```
Import the usual packages from the Scipy ecosystem:
```
import scipy as sp
import scipy.ndimage as spim
import matplotlib.pyplot as plt
```
## Demonstration on 2D Image
Start by cre... | github_jupyter |
# Exercise 6
```
# Importing libs
import cv2
import numpy as np
import matplotlib.pyplot as plt
apple = cv2.imread('images/apple.jpg')
apple = cv2.cvtColor(apple, cv2.COLOR_BGR2RGB)
apple = cv2.resize(apple, (512,512))
orange = cv2.imread('images/orange.jpg')
orange = cv2.cvtColor(orange, cv2.COLOR_BGR2RGB)
orange = ... | github_jupyter |
```
from bs4 import BeautifulSoup
import os
import random
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import LabelEncoder
from sklearn.datasets import fetch_20newsgroups
from sklearn.svm import LinearSVC
from sklearn.naive_bayes import ComplementNB
from sklearn.model_selection... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
from IPython.core.debugger import Pdb; pdb = Pdb()
def get_down_centre_last_low(p_list):
zn_num = len(p_list) - 1
available_num = min(9, (zn_num - 6))
index = len(p_list) - 4
for i in range(0, available_num // 2):
if p_list[index - 2... | github_jupyter |
```
# default_exp core
```
# module name here
> API details.
```
#hide
from nbdev.showdoc import *
#export
import pandas as pd
from tqdm import tqdm_notebook as tqdm
import json
import numpy as np
from fastai.vision.all import *
import albumentations as A
import skimage.io as skio
import warnings
warnings.filterwarn... | github_jupyter |
# Machine Learning and Statistics for Physicists
Material for a [UC Irvine](https://uci.edu/) course offered by the [Department of Physics and Astronomy](https://www.physics.uci.edu/).
Content is maintained on [github](github.com/dkirkby/MachineLearningStatistics) and distributed under a [BSD3 license](https://openso... | github_jupyter |
# Offline analysis of a [mindaffectBCI](https://github.com/mindaffect) savefile
So you have successfully run a BCI experiment and want to have a closer look at the data, and try different analysis settings?
Or you have a BCI experiment file from the internet, e.g. MOABB, and want to try it with the mindaffectBCI an... | github_jupyter |
```
import numpy as np
from scipy.integrate import odeint
from TricubicInterpolation import TriCubic
class Fermat(object):
def __init__(self,neTCI=None,frequency = 120e6,type='s',straightLineApprox=True):
'''Fermat principle. type = "s" means arch length is the indepedent variable
type="z" means z ... | github_jupyter |
## Desafio Final
```
# imports de avisos
import sys
import warnings
import matplotlib.cbook
warnings.simplefilter("ignore")
warnings.simplefilter(action='ignore', category=FutureWarning)
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=matplotlib.cbook.mplDeprecation... | github_jupyter |
# Database engineering
In this section we'll create:
+ table schemas using SQLAlchemy ORM
+ create a database in SQLite
+ load the cleaned Hawaii climate data into pandas dataframes
+ upload the data from the pandas dataframes into the SQLite database
```
# Dependencies
import pandas as pd
import sqlite3
from sqlalc... | github_jupyter |
<div class="alert alert-block alert-info" style="margin-top: 20px">
<a href="https://cocl.us/corsera_da0101en_notebook_top">
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/Images/TopAd.png" width="750" align="center">
</a>
</div>
<a href="https://ww... | github_jupyter |
# Aula 01 - Parte 1
## Transformações Lineares
Nesta primeira parte da aula faremos uma breve revisão de transformações lineares. Vamos começar pensando em transformações em 2D.
### Rotação
Crie uma função que recebe um ângulo $\theta$ e devolve uma matriz de rotação representada por um *numpy.array*. Os pontos são ... | github_jupyter |
```
#CREATE CLASS
#CLASS VS INSTANCE
#CREATE CLASS
class SoftwareEngineer:
def __init__(self, name, age, level, salary):
#instance attribute
self.name = name
self.age = age
self.level = level
self.salary = salary
#instance
se1 = SoftwareEngineer("Max", 20, "Junior", 50... | github_jupyter |
```
import cv2
cap = cv2.VideoCapture(0)
car_model=cv2.CascadeClassifier('cars.xml')
```
# TO DETECT CAR ON LIVE VIDEO OR PHOTO.....
```
while True:
ret,frame=cap.read()
cars=car_model.detectMultiScale(frame)
gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
for(x,y,w,h) in cars:
cv2.rectangle(fram... | github_jupyter |
<h1>datetime library</h1>
<li>Time is linear
<li>progresses as a straightline trajectory from the big bag
<li>to now and into the future
<li>日期库官方说明 https://docs.python.org/3.5/library/datetime.html
<h3>Reasoning about time is important in data analysis</h3>
<li>Analyzing financial timeseries data
<li>Looking at comm... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Algorithms/CloudMasking/landsat457_surface_reflectance.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
... | github_jupyter |
```
%%pyspark
df = spark.read.load('abfss://capture@splacceler5lmevhdeon4ym.dfs.core.windows.net/SeattlePublicLibrary/Library_Collection_Inventory.csv', format='csv'
## If header exists uncomment line below
, header=True
)
display(df.limit(10))
%%pyspark
# Show Schema
df.printSchema()
%%pyspark
from pyspark.sql i... | github_jupyter |
# LinearSVR with MinMaxScaler & Power Transformer
This Code template is for the Classification task using Support Vector Regressor (SVR) based on the Support Vector Machine algorithm with Power Transformer as Feature Transformation Technique and MinMaxScaler for Feature Scaling in a pipeline.
### Required Packages
... | github_jupyter |
<img src="../images/26-weeks-of-data-science-banner.jpg"/>
# Getting Started with Python
## About Python
<img src="../images/python-logo.png" alt="Python" style="width: 500px;"/>
Python is a
- general purpose programming language
- interpreted, not compiled
- both **dynamically typed** _and_ **strongly typed**
-... | github_jupyter |
#Instalamos pytorch
```
#pip install torch===1.6.0 torchvision===0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
```
#Clonamos el repositorio para obtener el dataset
```
!git clone https://github.com/joanby/deeplearning-az.git
from google.colab import drive
drive.mount('/content/drive')
```
# Importar l... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df=pd.read_csv("phl_hec_all_confirmed.csv") ;
# df.head()
sns.heatmap(df.isnull(),yticklabels=False,cbar=False)
df.drop(['P. Name KOI'],axis=1,inplace=True)
df.drop(['P. Min Mass (EU)'],axis=1,inplace=True)
df.drop(['P. Ma... | github_jupyter |
```
```
---
title: "Pipes and Filters"
teaching: 25
exercises: 10
questions:
- "How can I combine existing commands to do new things?"
objectives:
- "Redirect a command's output to a file."
- "Process a file instead of keyboard input using redirection."
- "Construct command pipelines with two or more stages."
- "Expl... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import molsysmt as msm
```
# Info
*Printing out summary information of a molecular system*
There is in MolSysMT a method to print out a brief overview of a molecular system and its elements. The output of this method can be a `pandas.DataFrame` or a `string`. Lets load a molecu... | github_jupyter |
# [Detecting the difficulty level of French texts](https://www.kaggle.com/c/detecting-the-difficulty-level-of-french-texts/overview/evaluation)
## Hyper parameters tuning
---
In this notebook, we will use cross-validation to find the best parameters of the models that showed the most promising result in first approach.... | github_jupyter |
#### Основы программирования в Python для социальных наук
## Web-scraping таблиц. Подготовка к самостоятельной
Семинар 7
*Автор: Татьяна Рогович, НИУ ВШЭ*
Этот блокнот поможет вам разобраться, как подходить к самостоятельной работе. Один из пунктов - это скрейпинг таблицы из википедии. Посмотрим на примере, как это... | github_jupyter |
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