text stringlengths 2.5k 6.39M | kind stringclasses 3
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```
from lightgbm import LGBMRegressor
from sklearn.compose import make_column_transformer
from sklearn.linear_model import TheilSenRegressor
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import FunctionTransformer
import pandas as pd
import ... | github_jupyter |
# VSB Power Grid Fault Detection
```
from IPython.display import Image
Image(url='https://upload.wikimedia.org/wikipedia/commons/thumb/e/e0/Three_Phase_Electric_Power_Transmission.jpg/1200px-Three_Phase_Electric_Power_Transmission.jpg')
```
Data Source: https://www.kaggle.com/c/vsb-power-line-fault-detection
Useful ... | github_jupyter |
### ***1. CTR数据中的类别数据处理，编码方式有哪些，区别是什么?***
对于 CTR 数据中的类别数据，比如用户所在的省份，城市；所使用的移动设备的型号，操作系统版本等。可采用的编码方法是通常有：
1. LabelEncoding - 将不同的类别的按照不同的整型数字编码。
2. OneHotEncoding - 用一个0和1组成的向量来表示来表示每一个类别，并确保在OneHot编码中体现类别的唯一性。
### ***2. 对于时间类型数据，处理方法有哪些?***
机器学习中对数据处理可以认为是广义的特征工程，它包括了如下几个部分：
 as fin:
... | github_jupyter |
# Convolutional Neural Networks: Application
Welcome to Course 4's second assignment! In this notebook, you will:
- Implement helper functions that you will use when implementing a TensorFlow model
- Implement a fully functioning ConvNet using TensorFlow
**After this assignment you will be able to:**
- Build and t... | github_jupyter |
```
import sys
sys.path.insert(1, '/media/galia-lab/Data1/users/gidonl/connectome_embed/cepy')
import numpy as np
import cepy as ce
import os
import time
```
## Learn embeddings of The Enhanced Nathan Kline Institute Rockland Sample:
The purpose of this notebook is to create CEs of a large group of subjects from The... | github_jupyter |
# Sharpe ratio (Solution)
## Install packages
```
import sys
!{sys.executable} -m pip install -r requirements.txt
import cvxpy as cvx
import numpy as np
import pandas as pd
import time
import os
import quiz_helper
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')
plt.rcParams['figure.figsize'... | github_jupyter |
# SETUP: Train and optimize a pet detector using Azure ML
This notebook will setup the Azure ML workspace and resources for the pet detector project. The Azure ML resources that we will be using in the pet detector project are:
1. Workspace
1. Experiment
1. Azure Compute Cluster
1. Datastore
1. HyperDrive
1. Azure C... | github_jupyter |
```
!nvidia-smi
import io
import os
import sys
import gc
import pickle
import random
import termcolor
import warnings
import shutil
import math
from functools import partial
from datetime import datetime
from dataclasses import dataclass
from pathlib import Path
from typing import List
import pandas as pd
import numpy... | github_jupyter |
```
%matplotlib inline
import numpy as np
from matplotlib import pyplot as plt
```
# How computers Make Predictions
Consider the 1D advection equation:
\begin{equation}
\frac{\partial u}{\partial t} + a \frac{\partial u}{\partial x} = 0
\end{equation}
Here $u(x, t)$ is a function of space and time that satisfies t... | github_jupyter |
# Exercices
## Questions
Une boucle est une
{bl}`multiplication|>répétition|réparation|condition` d'un
{bl}`>bloc|truc|itérateur|argument`.
```{question}
:multi:
Une boucle **for**
- {v}`itère sur une séquence`
- {f}`attend une condition`
- {v}`parcourt un ensemble`
- {f}`se termine quand un test est faux`
```
##... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
!pip install transformers
!pip install imblearn
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from scipy.stats import spearmanr
from imblearn.over_sampling import SMOTE
import pandas as pd
... | github_jupyter |
# Introduction to Tensors and Variables
**Learning Objectives**
1. Understand Basic and Advanced Tensor Concepts
2. Understand Single-Axis and Multi-Axis Indexing
3. Create Tensors and Variables
## Introduction
In this notebook, we look at tensors, which are multi-dimensional arrays with a uniform type (called ... | github_jupyter |
# Ground state solvers
## Introduction
<img src="aux_files/H2_gs.png" width="200">
In this tutorial we are going to discuss the ground state calculation interface of Qiskit Nature. The goal is to compute the ground state of a molecular Hamiltonian. This Hamiltonian can be electronic or vibrational. To know more abou... | github_jupyter |
# Training
We will train the network and save the model to disk. We will use this model in the `predict.ipynb` notebook.
**Note**:
We are currently using tensorflow 2.0 which is currently in beta state. So it is expected and ok that there are warnings!
## import libraries and set constants
```
from __future__ impo... | github_jupyter |
```
# Copyright 2020 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 writi... | github_jupyter |
```
import datetime
import json
import vk
import operator
import matplotlib.pyplot as plt
import numpy as np
import os
import gensim
import gensim.corpora as corpora
import pyLDAvis
import nltk
from nltk.corpus import stopwords
import pyLDAvis.gensim
import pymorphy2
from wordcloud import WordCloud
import numpy as np
i... | github_jupyter |
# Bioinformatics Modeling
## Synthetic Gene Sequence Data Builder - Tutorial 1
## Pre-requisites:
- Access to an IBM Cloud Object Storage instance
- The e2eai_credentials.json file (included in the repo clone) in your local directory updated with credentials to the cloud object storage instance
- A copy of ICOS.p... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import seaborn as sns
from scipy import stats
runs =[
"SA", "SA_CA",
"SA_SR","SA_CM", "SA_CTRD", "MD",
"CG", "CG_MD"
]
def get_ref_secondary():
"""returns a DataFrame containing all the reference structure... | github_jupyter |
# John Conway's Game of Life
The cellular automata game *Life*, invented by the mathematician [John Conway](https://en.wikipedia.org/wiki/John_Horton_Conway), makes a fun programming exercise. Let's review the [rules](http://en.wikipedia.org/wiki/Conway%27s_Game_of_Life):
The *world* of the Game of Life is an infini... | github_jupyter |
# Simple CNN for MNIST
Using the MNIST dataset (70 000 pictures of hand-written digits) we will train a simple CNN, which is able to predict a digit given a picture of a hand-written digit with 99% accuracy.
```
import numpy as np
np.random.seed(1337) # for reproducibility
from keras.datasets import mnist
from kera... | github_jupyter |
# *Imports and Dataset*
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_se... | github_jupyter |
# Stationarity and Correlation Analysis
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
```
# What is Stationarity?
#### A stationary time series is one whose properties do not depend on the time at which the series is observed.
Hyndman, R.J., & Athanasopoulos, G. (2018) Forecasting: prin... | github_jupyter |
# T81-558: Applications of Deep Neural Networks
**Module 3: Introduction to TensorFlow**
* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)
* For more information visit the [cla... | github_jupyter |
```
from __future__ import absolute_import, division, print_function
import torch
import numpy as np
from torch.utils.data import DataLoader
from torch import optim
from torch import nn
from torch.autograd import Variable
import os
from tqdm.notebook import tqdm
import math
import matplotlib.pyplot as plt
# file para... | github_jupyter |
```
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv1D, MaxPool1D, Flatten, Dense, Dropout, BatchNormalization
from tensorflow.keras.optimizers import Adam
print(tf.__version__)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot ... | github_jupyter |
# Supply Network Design 2
## Objective and Prerequisites
Take your supply chain network design skills to the next level in this example. We’ll show you how – given a set of factories, depots, and customers – you can use mathematical optimization to determine which depots to open or close in order to minimize overall ... | github_jupyter |
<a name="building-language-model"></a>
# Building the language model
<a name="count-matrix"></a>
### Count matrix
To calculate the n-gram probability, you will need to count frequencies of n-grams and n-gram prefixes in the training dataset. In some of the code assignment exercises, you will store the n-gram frequenc... | github_jupyter |
# Purpose
The purpose of this notebook is to analyze item similarities learned by training a factorization machine model. This consists of the following steps:
1. Load in movielens data
2. preprocess the data, and train a model.
3. Extract the item embedding vectors, and compute cosine similarities
4. Generate visual ... | github_jupyter |
<a href="https://colab.research.google.com/github/Tessellate-Imaging/monk_v1/blob/master/study_roadmaps/4_image_classification_zoo/Classifier%20-%20Food%20101%20Dataset.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Table of contents
## Install... | github_jupyter |
```
from matplotlib import pyplot as plt
import networkx as nx
import numpy as np
```
# Erdos Renyi
```
N = 500
k = 0.8
def gen_erdos(N, k):
G = nx.erdos_renyi_graph(N, k / N)
options = {
"node_color": "red",
'node_size': 5,
}
nx.draw(G, pos=nx.circular_layout(G), alpha=0.5, **option... | github_jupyter |
# Tradução
Uma das forças motrizes que possibilitaram o desenvolvimento da civilização humana é a capacidade de se comunicar uns com os outros. Na maioria das atividades humanas, a comunicação é a chave.
A inteligência artificial (IA) pode ajudar a simplificar a... | github_jupyter |
# Verification Example Notebook
This Notebook will show how to use the verification script by providing a few examples in order of increasing difficulty. Before you start I highly reccomend reading [README.md](https://github.com/OpenPrecincts/verification/blob/master/README.md)
The following cells will show how to us... | github_jupyter |
# Load and prepare data
**Objective**: Load news and tweets data from raw data files into sqlite3 db.
Last modified: 2017-10-15
# Roadmap
1. Copy ~~Meng~~ original data folder to DATA_DIR, unzip and check format.
2. Create db. Build tables for news and tweets.
3. Bulk load news and tweets data into db.
4. Check bas... | github_jupyter |
Strings
====
The process of cleaning data for analysis often requires working with text, for example, to correct typos, convert to standard nomenclature and resolve ambiguous labels. In some statistical fields that deal with (say) processing electronic medical records, information science or recommendations based on u... | github_jupyter |
<small><i>This notebook was prepared by [Donne Martin](http://donnemartin.com). Source and license info is on [GitHub](https://github.com/donnemartin/data-science-ipython-notebooks).</i></small>
# Functions
* Functions as Objects
* Lambda Functions
* Closures
* \*args, \*\*kwargs
* Currying
* Generators
* Generator E... | github_jupyter |
# Sensors
```
from pcg_gazebo.simulation import create_object, SimulationModel
from pcg_gazebo.task_manager import get_rostopic_list
# If there is a Gazebo instance running, you can spawn the box
# into the simulation
from pcg_gazebo.task_manager import Server
# First create a simulation server
server = Server()
# Cr... | github_jupyter |
# Conociendo Python
<img style="float: right; margin: 0px 0px 15px 15px;" src="https://upload.wikimedia.org/wikipedia/commons/c/c3/Python-logo-notext.svg" width="200px" height="200px" />
> Introducción a la materia, guía de instalación y descripción de las herramientas computacionales que se van a utilizar a lo largo... | github_jupyter |
```
# header files
import torch
import torch.nn as nn
import torchvision
import numpy as np
from google.colab import drive
drive.mount('/content/drive')
np.random.seed(1234)
torch.manual_seed(1234)
torch.cuda.manual_seed(1234)
# define transforms
train_transforms = torchvision.transforms.Compose([torchvision.transforms... | github_jupyter |
```
#Importing all required libraries
import os
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import Model
from os import getcwd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.optimizers import RMSprop
import shutil
from shutil import rmt... | github_jupyter |
# Test of Resampling Methods
## Utility Functions
```
import time
import pandas as pd
import numpy as np
import neurokit2 as nk
%matplotlib inline
def generate_signal(length=1000, end=20):
signal = np.cos(np.linspace(start=0, stop=end, num=length))
return(signal)
def resample(signal, method="interpolation")... | github_jupyter |
## 📍 Map initialization
There are several ways to draw background maps with Python. For a complete review, visit the [map section](https://www.python-graph-gallery.com/map) of the gallery
This example uses the `Basemap` library. Let's initialize a map of the world as explained in [this post](https://www.python-graph... | github_jupyter |
```
%matplotlib inline
```
# Blend transparency with color in 2-D images
Blend transparency with color to highlight parts of data with imshow.
A common use for :func:`matplotlib.pyplot.imshow` is to plot a 2-D statistical
map. The function makes it easy to visualize a 2-D matrix as an image and add
transparency to... | 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 |
# Exploratory Data Analysis
## Objectives
* Determine the shape of the annotation data.
* Identify class balance.
* Examine image metadata.
* Visualize images.
* Visualize bounding boxes.
* Get the mean pixel intensity of all images, to set as a parameter for Mask R-CNN.
## Data
* Radiological Society of North Ame... | github_jupyter |
# Hyper-parameter tuning
**Learning Objectives**
1. Understand various approaches to hyperparameter tuning
2. Automate hyperparameter tuning using CMLE HyperTune
## Introduction
In the previous notebook we achieved an RMSE of **4.13**. Let's see if we can improve upon that by tuning our hyperparameters.
Hyperparame... | github_jupyter |
```
import cv2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
class ColorTracking:
def rgb_to_hsv(self, r, g, b):
ma, mi = max(r, g, b), min(r, g, b)
h, s, v = 0, ma - mi, ma
if mi == b:
h = 60 * (g-r) / (ma-mi) + 60
elif mi == r:
h = 60... | github_jupyter |
# Building an Intake-esm catalog from CESM2 History Files
As mentioned in a couple of ESDS posts ([intake-esm and Dask](https://ncar.github.io/esds/posts/intake_esm_dask/), [debugging intake-esm](https://ncar.github.io/esds/posts/intake_cmip6_debug/)), [intake-esm](https://intake-esm.readthedocs.io/en/latest/) can be ... | github_jupyter |
```
%pylab inline
from sklearn import svm, datasets
iris = datasets.load_iris()
iris.data[0:2]
X = iris.data[:,:2]
y = iris.target
X[0:2]
y[0:2]
C = 1.0
# linear, poly, rbf
svc = svm.SVC(kernel='linear', C=1, gamma='auto').fit(X, y)
type(X)
X[0:10]
X[:,0]
# create a mesh to plot in
x_min, x_max = X[:, 0].min() - 1, X[:... | github_jupyter |
# os.path
This module implements some useful functions on pathnames. To read or write see `open()`, and for accessing the filesystem see the os module. The path paramenters can be passed as either strings or bytes. Applications are encouraged to represent file names as (unicode) character strings. Unfortunetly some fil... | github_jupyter |
<a target="_blank" href="https://colab.research.google.com/github/ati-ozgur/kurs-neural-networks-deep-learning/blob/master/notebooks/keras-fchollet-using-word-embeddings-pretrained.ipynb">Run in Google Colab
</a>
Example is taken from François Chollet book: Deep Learning with Python Original code can be found in ... | github_jupyter |
## Robbins-Monroe Algorithm
Assume we wish to find the root of an unknown function $g(x)$. If $g(x)$ were known and continuously differentiable we could apply Newton's method
$$x_{n+1} = x_n - \frac{g(x_n)}{g_x(x_n)}$$
where $g_x(\cdot)$ is the derivative of $g(\cdot)$ with respect to $x$. An alternative approach wo... | github_jupyter |
```
import h5py, json, spacy
import numpy as np
import cPickle as pickle
%matplotlib inline
import matplotlib.pyplot as plt
from model import LSTMModel
from utils import prepare_ques_batch, prepare_im_batch, get_batches_idx
embeddings = spacy.en.English()
word_dim = 300
nb_classes = 1000
h5_img_file_train = h5py.Fil... | github_jupyter |
# Multiclass Voting Classifier to Predict Wine Quality Score
## Wine Data
Data from http://archive.ics.uci.edu/ml/datasets/Wine+Quality
### Citations
P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.
Modeling wine preferences by data mining from physicochemical properties.
In Decision Support Systems, Elsevi... | github_jupyter |
```
# importing prerequisites
import sys
import requests
import cv2
import random
import tarfile
import json
import numpy as np
import pdf2image
from os import path
from PIL import Image
from PIL import ImageFont, ImageDraw
from glob import glob
from matplotlib import pyplot as plt
from pdf2image import convert_from_pa... | github_jupyter |
# How to Use Jupyter Notebook
To edit a cell in Jupyter notebook, run the cell by pressing Shift + Enter. This will allow changes you made to be available to other cells.
For more Keyboard Shortcuts go to navigation bar -- "Help" -- "Keyboard Shortcuts"
### Code cells
Re-running will execute any statements you have w... | github_jupyter |
```
import cv2
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import glob
import copy
class LaneFinding():
def __init__(self):
self.ym_per_pix = 30/720
self.xm_per_pix = 3.7/700
self.obj_points , self.img_points = self.camera_calibrate()
#self.sr... | github_jupyter |
## Ludwig, 2020 Code to Calculate Xray Luminosity for Binary Stripped Star - Neutron Star Source.
## Calculation by Ylva Gotberg and Katelyn Breivik
### Dependencies :
https://pypi.org/project/tabula-py/
and latex (for plots)
pip install tabula-py (not tabula!)
## Luminosity Calculation:
### Assumptions:
##... | github_jupyter |
```
# 그래프, 수학 기능 추가
# Add graph and math features
import pylab as py
import numpy as np
import numpy.linalg as nl
# 기호 연산 기능 추가
# Add symbolic operation capability
import sympy as sy
```
# 파이썬에서의 선형대수 : 사이파이 계열의 넘파이<br>Linear Algebra in Python: NumPy of SciPy Stack
파이썬 프로그래밍 언어의 기본 기능만으로도 선형 대수 문제를 해결하는 것이 가능은 하나, 보다... | github_jupyter |
# Exploring the Lorenz System of Differential Equations
In this Notebook we explore the Lorenz system of differential equations:
$$
\begin{aligned}
\dot{x} & = \sigma(y-x) \\
\dot{y} & = \rho x - y - xz \\
\dot{z} & = -\beta z + xy
\end{aligned}
$$
This is one of the classic systems in non-linear differential equati... | github_jupyter |
```
import pandas as pd
import numpy as np
import cPickle
from nltk.corpus import stopwords
from gensim.models import word2vec
import nltk.data
import re
import logging
from nltk.stem.snowball import *
import itertools
# Python 2.x:
import HTMLParser
html_parser = HTMLParser.HTMLParser()
import multiprocessing
loggi... | github_jupyter |
# SUTD 2021 50.007 Machine Learning HMM Project Part 4
> Group 4:
> - Ma Yuchen (1004519)
> - Chung Wah Kit (1004103)
> - James Raphael Tiovalen (1004555)
## Initial Workspace Setup
```
# Setup and install dependencies
# !pip3 install numpy
# !pip3 install torch
# Import libraries
import os
import numpy as np
from ... | github_jupyter |
# Pore Scale Imaging and Modeling Section I
In this project, we have selected a comprehensive paper related to [pore scale imaging and modeling](https://www.sciencedirect.com/science/article/pii/S0309170812000528). The goal of this example is to investigate the permeability of different rock samples. As there are diff... | github_jupyter |
# Gridded data, NetCDF
## Xarray
When working with higher dimensional data (3D or more), we can't rely on Pandas. Here comes [Xarray](http://xarray.pydata.org/en/stable/index.html) to the rescue.
* It has Pandas like syntax, so if you know Pandas you will find yourself at home with Xarray.
* format agnostic: It can re... | github_jupyter |
# PRÁCTICA 02.
# Perceptrón Simple.
### OBJETIVO:
Que el alumno implemente el perceptrón simple y lo aplique en distintos conjuntos de datos aplicando variaciones en la forma de aprendizaje del perceptrón.
```
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from mpl_toolkits.mplot3d im... | github_jupyter |
```
import importlib
import json
import os
import sys
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
import torchvision.models as models
from PIL import Image
from torchvision import transforms
from torch.nn import functional as F
sys.path.append("../src")
import dataloader
import... | github_jupyter |
# Louvain Community Detection
In this notebook, we will use cuGraph to identify the cluster in a test graph using the Louvain algorithm
Notebook Credits
* Original Authors: Bradley Rees and James Wyles
* Created: 08/01/2019
* Last Edit: 10/16/2019
RAPIDS Versions: 0.10.0
Test Hardware
* GV100 32G, CUDA 10.0
... | github_jupyter |
Notebook is a useful tool for data scientists: It allows us to use coding and presentation tools together.
```
# we can code
print("Welcome to DATA601!")
```
__we can create headers of different sizes__
# Header
## Header
### Header
#### Header
__we can create bullet points__
- Item
- item
__we can order item... | github_jupyter |
```
%pylab inline
import pandas as pd
human_orthology = pd.read_table('/home/cmb-panasas2/skchoudh/genomes/ensemble_orthology/human-mouse/human_query.tsv').set_index('ensembl_gene_id')
mouse_orthology = pd.read_table('/home/cmb-panasas2/skchoudh/genomes/ensemble_orthology/human-mouse/mouse_query.tsv').set_index('ensemb... | github_jupyter |
# Determines bounding boxes for each sulcus
This notebook determines bounding box around a sulcus. It uses a supervised database, in which each sulcus has been manually labelled.
# Imports
```
import sys
import os
import json
```
The following line permits to import deep_folding even if this notebook is executed fr... | github_jupyter |
# Table of Contents
<p><div class="lev1 toc-item"><a href="#Defining-a-gene-compactifier-for-easy-printing" data-toc-modified-id="Defining-a-gene-compactifier-for-easy-printing-1"><span class="toc-item-num">1 </span>Defining a gene compactifier for easy printing</a></div><div class="lev1 toc-item"><a href="... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import geopandas as gpd
import geoplot
import pickle
import geoplot.crs as gcrs
import statsmodels.api as sm
mount_path = "/mnt/c/Users/jason/Dropbox (MIT)/"
#mount_path = "/Users/shenhaowang/Dropbox (MIT)/project_media_lab_South_Australia/"
age... | github_jupyter |
# Matplotlib图鉴——进阶饼图
## 公众号：可视化图鉴
```
import matplotlib
print(matplotlib.__version__) #查看Matplotlib版本
import pandas as pd
print(pd.__version__) #查看pandas版本
import numpy as np
print(np.__version__) #查看numpy版本
import matplotlib.pyplot as plt
import matplotlib as mpl
plt.rcParams['font.sans-serif'] = ['STHeiti'] #设置中文... | github_jupyter |
```
from __future__ import absolute_import, division, print_function
from matplotlib.font_manager import _rebuild; _rebuild()
#Helper libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import scipy.io as spio
import keras
from keras.models import Sequential
from keras.layers import Dense
f... | github_jupyter |
```
from pyspark.sql.functions import col, when
data_lake_account_name = '' # Synapse Workspace ADLS
file_system_name = 'relmeshadlsfs'
sf_accounts_path = f'abfss://{file_system_name}@{data_lake_account_name}.dfs.core.windows.net/salesforcedata/account/accounts.csv'
mapping_file = f'abfss://{file_system_name}@{data_lak... | github_jupyter |
# Data frames manipulation with pandas
[pandas](https://pandas.pydata.org/) - fast, powerful, flexible and easy to use open source data analysis and manipulation tool,
built on top of the Python programming language.
----------------
```
## Load libraries
import pandas as pd
import matplotlib.pyplot as plt
```
## ... | github_jupyter |
```
import numpy as np
import pandas as pd
from sklearn import preprocessing
data = np.loadtxt("data.csv",delimiter=',')
#len(data[0]),len(data[1])
M = len(data[0])
x = data[:,0:(M-1)]
y = data[:,M-1:]
z = np.ones((len(data),1))
new_d = np.hstack((x,z))
new_d = np.hstack((new_d,y))
points = np.loadtxt("test_boston_x_t... | github_jupyter |
```
from collections import namedtuple
```
## Problem
- you want to sort a list of comments represented as tuples, namedtuples, dicts, objects by either the number of likes or comment.content length.
```
comment_str = 'Python3 is awesome'
comment_tuple = ('Junior', 'Python3 is awesome', 5, (8, 5, 2019))
comment_dic... | github_jupyter |
```
# !pip install smart_open
import sagemaker
import boto3
from sagemaker import image_uris
from sagemaker.session import Session
from sagemaker.inputs import TrainingInput
## data preprocessing libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from smart_open import open as s_open
imp... | github_jupyter |
## ColorScale
The colors for the `ColorScale` can be defined one of two ways:
- Manually, by setting the scale's `colors` attribute to a list of css colors. They can be either:
- html colors (link) `'white'`
- hex `'#000000'`
- rgb `'rgb(0, 0, 0)'`.
```python
col_sc = ColorScale(colors=['yellow', 'red'])
```... | github_jupyter |
<div class="alert alert-block alert-info" style="margin-top: 20px">
<a href="https://cocl.us/NotebooksPython101">
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Ad/TopAd.png" width="750" align="center">
</a>
</div>
<a href="https://cognitiveclass.ai... | github_jupyter |
# Train and visualize a model in Tensorflow - Part 4: Inspecting the model
Neural networks have been widely critized because of the lack of interpretation of their internal parameters. In this notebook we will present some techniques to log and visualize the model behaviour during training.
The lack of interpretabili... | github_jupyter |
```
"""
This set of functions help the users resize
"""
import os
import json
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import detectron2
import detectron2.data.transforms as T
from detectron2.structures import BoxMode
from detectron2.engine.defaults import DefaultPredictor
import labelme
... | github_jupyter |
<a href="https://colab.research.google.com/github/tensorflow/privacy/blob/master/tutorials/Classification_Privacy.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
##### Copyright 2019 The TensorFlow Authors.
```
#@title Licensed under the Apache Lic... | github_jupyter |
##### Copyright 2020 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
```
from sklearn import model_selection, metrics, utils
import tensorflow as tf
import keras.callbacks as callbacks
import numpy as np
import pandas as pd
from amp.models.discriminators import veltri_amp_classifier
import amp.data_utils.data_loader as data_loader
import amp.utils.classifier_utils as cu
import matplotli... | github_jupyter |
# Final Project
# Title: California Housing Price Analysis
## Team members
### Names: Shuibenyang Yuan, Bolin Yang
### PIDs: A14031016, A92111272
<img src="housing.jpg" width="50%">
## Research Questions & Reasons for choosing them
The reason we want to analyze the housing price in California is that housing prices... | github_jupyter |
# Modules
If you quit from the Python interpreter and enter it again, the definitions you have made (functions and variables) are lost. Therefore, if you want to write a somewhat longer program, you are better off using a text editor to prepare the input for the interpreter and running it with that file as input inste... | github_jupyter |
# Introduction to Spark
Spark is a fast and general engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Spark applications can be written in Python, Java, Scala in R. It integrates well with IPython and the entire Python Stack (e.g. Numpy).
The company Dat... | github_jupyter |
```
import os
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import pkg_resources
import debvader
```
### Download and format data
Download dataset that will be used for training. It has been generated using the code in https://github.com/BastienArcelin/dc2_img_generation and the stamps a... | github_jupyter |
# Data Mining: Homework 7
### Elaheh Toulabi Nejad | 9631232
## 1. Logistic Regression
### a)
```
import pandas as pd
from sklearn.datasets import load_breast_cancer
cancer_data = load_breast_cancer()
data = pd.DataFrame(cancer_data.data,columns=cancer_data.feature_names)
data.head(3)
```
<hr style = "border-top... | github_jupyter |
# Detailed execution time for cadCAD models
*Danilo Lessa Bernardineli*
---
This notebook shows how you can get detailed info about the execution time by using Python decorators on the minimal P&P model. The strategy is to make use of the decorator as defined in the next block, and use in the most convenient way.
T... | github_jupyter |
# Keras for Text Classification
**Learning Objectives**
1. Learn how to tokenize and integerize a corpus of text for training in Keras
1. Learn how to do one-hot-encodings in Keras
1. Learn how to use embedding layers to represent words in Keras
1. Learn about the bag-of-word representation for sentences
1. Learn how ... | github_jupyter |
<br>
**References and Additional Resources**
<br>
> [TensorFlow Guides: Keras Sequential Model](https://www.tensorflow.org/guide/keras/sequential_model)<br>
> [Keras: The Sequential Model Guide](https://keras.io/guides/seq... | github_jupyter |
```
# 3가지 타입(def read_type, art_type, view_type)으로 나뉘고 각각 실행해야함
# 4/30 ~ 4/1 1달간 총 14393개 *50개(3분)
## 입력값(이것만 입력할 것) ##
num = [0, 5827] # 수행할 구간, 매일경제은 range(0, n+1) 번 설정해야함, 내가 처음에 range(0,50)까지했으면 다음에는 range(50,이후숫자)
loot = 'C:/Users/###/###/###/' # 저장할 위치, 파일이 산만해지니 프로젝트가아닌폴더에서 관리할 것 # loot = './/' 현재위치에 저장하는 변수
... | github_jupyter |
# Amazon SageMaker Multi-Model Endpoints using XGBoost
With [Amazon SageMaker multi-model endpoints](https://docs.aws.amazon.com/sagemaker/latest/dg/multi-model-endpoints.html), customers can create an endpoint that seamlessly hosts up to thousands of models. These endpoints are well suited to use cases where any one o... | github_jupyter |
d
# Distributed Inference with mapInPandas
Train sklearn model and log with MLflow.
```
ls /dbfs/databricks-datasets/learning-spark-v2/sf-airbnb
import mlflow.sklearn
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
fr... | github_jupyter |
```
import tensorflow as tf
import datetime, os
#hide tf logs
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # or any {'0', '1', '2'},
#0 (default) shows all, 1 to filter out INFO logs, 2 to additionally filter out WARNING logs, and 3 to additionally filter out ERROR logs
import scipy.optimize
import scipy.io
import numpy a... | github_jupyter |
```
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
```
# Dataloaders for ShapeNetCore and R2N2
This tutorial shows how to:
- Load models from ShapeNetCore and R2N2 using PyTorch3D's data loaders.
- Pass the loaded datasets to `torch.utils.data.DataLoader`.
- Render ShapeNetCore models with PyT... | github_jupyter |
# Fetch data and prepare sample
* The complete training dataset has 25k images, 12.5k from cats and 12.5k from dogs.
* We use 1000 images each for training and 500 for validation / test.
## Fetch training data
```
import os
import requests
from tqdm import tqdm_notebook as tqdm
from pathlib import Path
root = Path... | github_jupyter |
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