code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
# Simulation of BLER in RBF channel
```
import numpy as np
import pickle
from itertools import cycle, product
import dill
import matplotlib.pyplot as plt
from scipy.spatial.distance import cdist
```
Simulation Configuration
```
blkSize = 8
chDim = 4
# Input
inVecDim = 2 ** blkSize # 1-hot vector lengt... | github_jupyter |
```
# Copyright 2021 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 |
# Exploratory Analysis
## 1) Reading the data
```
import types
import pandas as pd
df_claim = pd.read_csv('https://raw.githubusercontent.com/IBMDeveloperUK/Machine-Learning-Models-with-AUTO-AI/master/Data/insurance.csv')
df_claim.head()
df_data = pd.read_csv('https://raw.githubusercontent.com/IBMDeveloperUK/Machine... | github_jupyter |
```
from pathlib import Path
import awkward as ak
import matplotlib.colors as colors
import matplotlib.pyplot as plt
import numpy as np
import tqdm.notebook as tqdm
import uproot
run_name = "run_050016_10192021_21h49min_Ascii_build"
# run_name = "build"
raw_path = Path("data/raw") / f"{run_name}.root"
img_path = Path... | github_jupyter |
<a href="https://colab.research.google.com/github/tvml/ml2122/blob/master/codici/backprop.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Rete neurale per riconoscere caratteri. Backpropagation implementata.
```
from IPython.display import Image... | github_jupyter |
<table>
<tr><td align="right" style="background-color:#ffffff;">
<img src="../images/logo.jpg" width="20%" align="right">
</td></tr>
<tr><td align="right" style="color:#777777;background-color:#ffffff;font-size:12px;">
Abuzer Yakaryilmaz | April 15, 2019 (updated)
</td></tr>
<tr><td... | github_jupyter |
```
from sklearn.neural_network import MLPRegressor
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import classification_re... | github_jupyter |
__GRIP at The Sparks Foundation Internship Task #1__
__Author :- Harshada Jadhav__
## **Linear Regression with Python Scikit Learn**
In this section we will see how the Python Scikit-Learn library for machine learning can be used to implement regression functions. We will start with simple linear regression involving... | github_jupyter |
# Support Vector Machines (SVM) with Sklearn
This notebook creates and measures an [LinearSVC with Sklearn](http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC). This has more flexibility in the choice of penalties and loss functions and should scale better to large number... | github_jupyter |
## 1. Requirements
```
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision.datasets import MNIST
from torchvision import datasets, transforms
from advertorch.attacks import JacobianSaliencyMapAttack as JSMA
import numpy as np
import matplotlib.pyplot as pl... | github_jupyter |
```
import pandas as pd
import numpy as np
import os
import json
import altair as alt
JSON_FILE = "../results/BDNF/Recombinants/BDNF_codons_RDP_recombinationFree.fas.FEL.json"
pvalueThreshold = 0.1
def getFELData(json_file):
with open(json_file, "r") as in_d:
json_data = json.load(in_d)
return json_data... | github_jupyter |
# enable user scoped libraries
```
import site
site.addsitedir(site.USER_SITE)
```
# import basic packages
```
import math
import numpy as np
import torch
```
# params
```
BATCH_SIZE=128
EPOCHS = 30
VALIDATION_RATIO = 0.2
RANDOM_SEED = 1
```
# function to preprocess datasets
```
from torchvision import transform... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Preamble" data-toc-modified-id="Preamble-1"><span class="toc-item-num">1 </span>Preamble</a></span><ul class="toc-item"><li><span><a href="#General-imports" data-toc-modified-id="General-imports-... | github_jupyter |
```
import pandas as pd
import requests
from fantasy import fantasy_points
from creds import nfl_api_key
schedule_url = 'https://profootballapi.com/schedule'
game_url = 'https://profootballapi.com/game'
all_games = requests.post(schedule_url, params={'api_key': nfl_api_key, 'season_type': 'REG'}).json()
```
## Gather... | github_jupyter |
# Mixup / Label smoothing
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
#export
from exp.nb_10 import *
path = datasets.untar_data(datasets.URLs.IMAGENETTE_160)
tfms = [make_rgb, ResizeFixed(128), to_byte_tensor, to_float_tensor]
bs = 64
il = ImageList.from_files(path, tfms=tfms)
sd = SplitData.split_by_... | github_jupyter |
```
import os
import django
from django.db import transaction
import random
from django_efilling.models import Instrument, InstrumentQuestion, InstrumentQuestionChoice
from django_efilling.models import (ESSAY, SINGLE_CHOICE, MULTIPLE_CHOICE, IMAGE_CHOICE, Respondent)
os.environ["DJANGO_ALLOW_ASYNC_UNSAFE"] = "true"
dj... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
hs_d = pd.read_csv('Housing_data.csv')
hs_d.head()
```
# Exploratory Data Analysis
```
hs_d.isnull().sum()
hs_d.nunique()
```
# The Cateforical features for the Dataset
```
for i in hs_d.columns:
if hs_d[i].nunique(... | github_jupyter |
<a href="https://colab.research.google.com/github/csd-oss/vc-investmemt/blob/master/VC_Investment.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# General preparation and GDrive conection
```
import pandas as pd
import matplotlib.pyplot as plt
``... | github_jupyter |
```
mylist = [1,2,3,4]
for n in range(5):
print(n);
for n in range(3,15):
print(n);
for n in range(2,15,3):
print(n);
range(7,21,6) #is a generator
list(range(7,21,6))
index_count = 0;
# for letter in 'abcde':
# print(f'At index {index_count} the letter is {letter}')
# index_count += index_count+1
f... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
# Install pypcd from this repository
import notebook_helper
!{notebook_helper.get_install_cmd(quiet=True)}
import pypcd
print(pypcd.__version__)
# Intentionally pasting the example point cloud into this cell
# so the reader can inspect the ascii file format
#
# ... | github_jupyter |
## Review Calculus using by Python
Consider a sequence of n numbers $x_0, x_1, \cdots x_{n-1}$. We will start our index at 0, to remain in accordance with Python/Numpy's index system. $x_0$ is the first number in the sequence, $x_1$ is the second number in the sequence, and so forth, so $x_j$ is the general $j+1$ numb... | github_jupyter |
# Introduction
## A quick overview of batch learning
If you've already delved into machine learning, then you shouldn't have any difficulty in getting to use incremental learning. If you are somewhat new to machine learning, then do not worry! The point of this notebook in particular is to introduce simple notions. W... | github_jupyter |
# Gaussian Mixture Model
This is tutorial demonstrates how to marginalize out discrete latent variables in Pyro through the motivating example of a mixture model. We'll focus on the mechanics of parallel enumeration, keeping the model simple by training a trivial 1-D Gaussian model on a tiny 5-point dataset. See also ... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import display, HTML, IFrame
from ipywidgets import interact,fixed
from mpl_toolkits import mplot3d
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from matplotlib.patches import Rectangle
from numpy.linalg import norm
from numpy impor... | github_jupyter |
## Recommender System With Pyspark
### User Ratings Using Alternative Least Square
Import libraries
```
from pyspark.sql import SparkSession
from pyspark.ml.recommendation import ALS
from pyspark.ml.evaluation import RegressionEvaluator
from pyspark.ml.tuning import TrainValidationSplit, ParamGridBuilder
#import fin... | github_jupyter |
# Graph optimization with QAOA
One application area where near-term quantum hardware is expected to shine is in graph optimization. Graph-based problems are interesting to explore because they have both strong links to practical use-cases (such as logistics and social networks) and are also often hard to solve.
![gra... | github_jupyter |
# Practical Session 3: Ensemble Learning Techniques
*Notebook by Ekaterina Kochmar*
This practical will address the use of ensemble-based learning techniques. You will be working with the otherwise familiar settings of classification and regression tasks. In this practical, you will use the familiar datasets and will... | github_jupyter |
```
%matplotlib inline
```
# Cross-validation on diabetes Dataset Exercise
A tutorial exercise which uses cross-validation with linear models.
This exercise is used in the `cv_estimators_tut` part of the
`model_selection_tut` section of the `stat_learn_tut_index`.
```
from __future__ import print_function
print(_... | github_jupyter |
```
# training dataset
training_data = [
['Yes', 'No','No','Yes','Some','$$$','No','Yes','French','0-10','Yes'],
['Yes', 'No', 'No', 'Yes', 'Full', '$', 'No', 'No', 'Thai', '30-60', 'No'],
['No', 'Yes', 'No', 'No', 'Some', '$', 'No', 'No', 'Burger', '0-10', 'Yes'],
['Yes', 'No', 'Yes', 'Yes', 'Full', '$... | github_jupyter |
```
import plaidml.keras
plaidml.keras.install_backend()
import os
os.environ["KERAS_BACKEND"] = "plaidml.keras.backend"
# Importing useful libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layer... | github_jupyter |
# Optimization Methods
Until now, you've always used Gradient Descent to update the parameters and minimize the cost. In this notebook, you will learn more advanced optimization methods that can speed up learning and perhaps even get you to a better final value for the cost function. Having a good optimization algorit... | github_jupyter |
```
from torchvision.models import *
import wandb
from sklearn.model_selection import train_test_split
import os,cv2
import numpy as np
import matplotlib.pyplot as plt
from torch.nn import *
import torch,torchvision
from tqdm import tqdm
device = 'cuda'
PROJECT_NAME = 'Intel-Image-Classification-TL'
def load_data():
... | github_jupyter |
# Beacon Time Series, across the transition
Edit selector= below
Look at the beacons with the largest normalized spread.
( Steal plotMultiBeacons() from here.)
```
import math
import numpy as np
import pandas as pd
import BQhelper as bq
%matplotlib nbagg
import matplotlib.pyplot as plt
bq.project = "mlab-sandbox"... | github_jupyter |
<a href="https://colab.research.google.com/github/shakasom/MapsDataScience/blob/master/Chapter4.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Making sense of humongous location datasets
## Installations
The geospatial libraries are not pre ins... | github_jupyter |
```
from sklearn.datasets import load_files
import os
PATH = '/home/mikhail/Documents/ML/лекции/mlcourse_open-master/data/imdb_reviews'
!du -hs $PATH
%%time
train_reviews = load_files(os.path.join(PATH, 'train'))
%%time
test_reviews = load_files(os.path.join(PATH, 'train'))
len(train_reviews.data)
len(test_reviews.data... | github_jupyter |
Lambda School Data Science
*Unit 2, Sprint 1, Module 3*
---
# Ridge Regression
## Assignment
We're going back to our other **New York City** real estate dataset. Instead of predicting apartment rents, you'll predict property sales prices.
But not just for condos in Tribeca...
- [ ] Use a subset of the data where... | 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 |
# Load MXNet model
In this tutorial, you learn how to load an existing MXNet model and use it to run a prediction task.
## Preparation
This tutorial requires the installation of Java Kernel. For more information on installing the Java Kernel, see the [README](https://github.com/awslabs/djl/blob/master/jupyter/READM... | github_jupyter |
# 3장. 사이킷런을 타고 떠나는 머신 러닝 분류 모델 투어
**아래 링크를 통해 이 노트북을 주피터 노트북 뷰어(nbviewer.jupyter.org)로 보거나 구글 코랩(colab.research.google.com)에서 실행할 수 있습니다.**
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://nbviewer.jupyter.org/github/rickiepark/python-machine-learning-book-2nd-edition/blob... | github_jupyter |
## Importing Necessary Libraries and Functions
The first thing we need to do is import the necessary functions and libraries that we will be working with throughout the topic. We should also go ahead and upload all the of the necessary data sets here instead of loading them as we go. We will be using energy production... | github_jupyter |
```
from tqdm.notebook import tqdm
import math
import gym
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from collections import deque
from active_rl.networks.dqn_atari import DQN
from active_rl.utils.memory import ReplayMemory
from active_rl.utils.optimization import stand... | github_jupyter |
## Initial setup
```
from google.colab import drive
drive.mount('/content/drive')
import tensorflow as tf
print(tf.__version__)
# tensorflow version used is 2.8.0
import torch
print(torch.__version__)
# torch version used is 1.10+cu111
!nvidia-smi
# Other imports
! pip install tensorflow_addons
! pip install tensorflo... | github_jupyter |
## 1. Importi
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime
import csv
import os.path
import mplcyberpunk
plt.style.use("cyberpunk")
```
## 2. Branje podatkov
```
with open('../data/kd2018.csv', 'rt') as csvfile:
reader = csv.reader(csvfile, delimiter='... | github_jupyter |
```
import numpy as np
import json
import re
from collections import defaultdict
import spacy
import matplotlib.pyplot as plt
%matplotlib inline
annotation_file = '../vqa-dataset/Annotations/mscoco_%s_annotations.json'
annotation_sets = ['train2014', 'val2014']
question_file = '../vqa-dataset/Questions/OpenEnded_mscoco... | github_jupyter |
```
%matplotlib inline
```
# Spectral clustering for image segmentation
In this example, an image with connected circles is generated and
spectral clustering is used to separate the circles.
In these settings, the `spectral_clustering` approach solves the problem
know as 'normalized graph cuts': the image is seen ... | github_jupyter |
```
%matplotlib inline
from image_registration import chi2_shift
from matplotlib import pyplot as plt
from matplotlib import rcParams
import seaborn as sns
import numpy as np
import cv2
all_imgs = !ls 210226_Bladder_TMA1_reg35/1_shading_correction/*.tif | grep DAPI
for i,z in enumerate(all_imgs):
print(i, z)
... | github_jupyter |
<center>
<img src="img/scikit-learn-logo.png" width="40%" />
<br />
<h1>Robust and calibrated estimators with Scikit-Learn</h1>
<br /><br />
Gilles Louppe (<a href="https://twitter.com/glouppe">@glouppe</a>)
<br /><br />
New York University
</center>
```
# Global imports and settings
# Mat... | github_jupyter |
```
import tensorflow as tf
import matplotlib.pyplot as plt
import random
import time
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
learning_rate = 0.001
training_epochs = 15
batch_size = 100
input_x = tf.placeholder(tf.float32, [None,784])
# 이... | github_jupyter |
## Benchmarking Scipy Signal vs cuSignal Time to Create Windows in Greenflow
The windows examples were taken from the example [cusignal windows notebook](https://github.com/rapidsai/cusignal/blob/branch-21.08/notebooks/api_guide/windows_examples.ipynb).
### General Parameters
```
import cupy.testing as cptest
from g... | github_jupyter |
```
import importlib
import pathlib
import os
import sys
from datetime import datetime, timedelta
import pandas as pd
module_path = os.path.abspath(os.path.join('../..'))
if module_path not in sys.path:
sys.path.append(module_path)
datetime.now()
ticker="GME"
report_name=f"{ticker}_{datetime.now().strftime('%Y%m%d_... | github_jupyter |
```
# Get imports
import pickle
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib qt
# import helper functions
import camera_calibrator
import distortion_correction
import image_binary_gradient
import perspective_transform
import detect_lane_pixels
im... | github_jupyter |
# Setting up
```
# Dependencies
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns
from scipy.stats import sem
plt.style.use('seaborn')
# Hide warning messages in notebook
# import warnings
# warnings.filterwarnings('ignore')
```
# Importing 4 csv files a... | github_jupyter |
# Content personalization
## Without context
This example takes inspiration from Vowpal Wabbit's [excellent tutorial](https://vowpalwabbit.org/tutorials/cb_simulation.html).
Content personalization is about taking into account user preferences. It's a special case of recommender systems. Ideally, side-information sh... | github_jupyter |
# Simple dynamic seq2seq with TensorFlow
This tutorial covers building seq2seq using dynamic unrolling with TensorFlow.
I wasn't able to find any existing implementation of dynamic seq2seq with TF (as of 01.01.2017), so I decided to learn how to write my own, and document what I learn in the process.
I deliberately... | github_jupyter |
# Segmented deformable mirrors
We will use segmented deformable mirrors and simulate the PSFs that result from segment pistons and tilts. We will compare this functionality against Poppy, another optical propagation package.
First we'll import all packages.
```
import os
import numpy as np
import matplotlib.pyplot a... | github_jupyter |
### Contexto
Base de Dados de Churn
<br>
[IBM Sample Data Sets]
### Conteúdo
Cada linha representa um cliente.
<br>
Cada coluna contém os atributos do cliente descritos na coluna Metadados.
O conjunto de dados inclui informações sobre:
<br>
Clientes que saíram no último mês - a coluna é chamada de rotatividade
<br>... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.read_csv('austin_weather.csv')
df.head()
df.info()
```
<h2>Visualisasi Scatter Plot Perbandingan Kuantitatif</h2>
Pada tugas kali ini kita akan mengamati nilai DewPointAvg (F) dengan mengamati nilai HumidityAvg (%), TempAvg (F), dan ... | github_jupyter |
# Preliminaries: imports, start H2O, load data
```
import sklearn
import pandas as pd
import numpy as np
import shap
import h2o
from h2o.automl import H2OAutoML
df = pd.read_csv('C:/Users/Karti/NEU/data/insurance.csv')
df.head()
h2o.init()
data_path = 'C:/Users/Karti/NEU/data/insurance.csv'
h2o_df = h2o.import_file(da... | github_jupyter |
# Convolutional Neural Networks: Step by Step
Welcome to Course 4's first assignment! In this assignment, you will implement convolutional (CONV) and pooling (POOL) layers in numpy, including both forward propagation and (optionally) backward propagation.
**Notation**:
- Superscript $[l]$ denotes an object of the $l... | github_jupyter |
# Mapboxgl Python Library for location data visualizaiton
https://github.com/mapbox/mapboxgl-jupyter
### Requirements
These examples require the installation of the following python modules
```
pip install mapboxgl
pip install pandas
```
```
import pandas as pd
import os
from mapboxgl.utils import *
from mapboxgl.... | github_jupyter |
# ARC Tools
## Coordinates conversions
Below, `xyz` and `zmat` refer to Cartesian and internal coordinates, respectively
```
from arc.species.converter import (zmat_to_xyz,
xyz_to_str,
zmat_from_xyz,
zmat_to_str,
... | github_jupyter |
## Learning Pandas and Matplotlib
Pandas is pythons library that enables broad possibilities for data analysis.
By using Pandas it is very easy to upload, manage and analyse data from different tables by using SQL-like commands. Moreover, in connection with the libraries Matplotlib and Seaborn, Pandas gives broad oppo... | github_jupyter |
# Fisheries competition 大自然保护渔业监测
In this notebook we're going to investigate a range of different architectures for the [Kaggle fisheries competition](https://www.kaggle.com/c/the-nature-conservancy-fisheries-monitoring/). The video states that vgg.py and ``vgg_ft()`` from utils.py have been updated to include VGG w... | github_jupyter |
```
println("Hello World") // make sure we're in a spark kernel
```
# Scala for Spark - Assignment
Learning Scala the hard way.
## Part 1: The Basics
```
/*
Try the REPL
Scala has a tool called the REPL (Read-Eval-Print Loop) that is analogous to
commandline interpreters in many other languages. You may type... | github_jupyter |
# How Debuggers Work
Interactive _debuggers_ are tools that allow you to selectively observe the program state during an execution. In this chapter, you will learn how such debuggers work – by building your own debugger.
```
from bookutils import YouTubeVideo
YouTubeVideo("4aZ0t7CWSjA")
```
**Prerequisites**
* You... | github_jupyter |
```
import gpt_2_simple as gpt2
import os
import requests
!pip install nltk
import nltk
nltk.download('averaged_perceptron_tagger')
from nltk.tag import pos_tag
!nvidia-smi
# gpt2.download_gpt2(model_name="117M")
sess = gpt2.start_tf_sess()
gpt2.load_gpt2(sess, run_name='run3')
gpt2.generate(sess, run_name='run3')
file... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
from datetime import timedelta
subject = 'EGD-0125'
csv_file = 'clinical_data.csv'
xnat_path = 'https://bigr-rad-xnat.erasmusmc.nl'
user = ''
project = 'EGD'
## Difference in dates between sources, due to anonimization
num_days_anon = -1
res = pd.read_csv(csv_fi... | github_jupyter |
# Course Outline
* Step 0: 載入套件並下載語料
* Step 1: 將語料讀進來
* Step 2: Contingency table 和 keyness 計算公式
* Step 3: 計算詞頻
* Step 4: 計算 keyness
* Step 5: 找出 PTT 兩板的 keywords
* Step 6: 視覺化
# Step 0: 載入套件並下載語料
```
import re # 待會會使用 regular expression
import math # 用來計算 log
import pandas as pd # 用來製作表格
import matplot... | github_jupyter |
```
# HIDDEN
from datascience import *
from prob140 import *
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
%matplotlib inline
# HIDDEN
def joint_probability(x, y):
if x == 1 & y == 1:
return 2/8
elif abs(x - y) < 2:
return 1/8
else:
return 0
... | github_jupyter |
# AI Hub Open API 서비스
https://aiopen.etri.re.kr/service_list.php
## 오픈 AI API·DATA 서비스
- ETRI에서 과학기술정보통신부 R&D 과제를 통해 개발된 최첨단 인공지능 기술들을 오픈 API 형태로 개발
- 중소·벤처 기업, 학교, 개인 개발자 등의 다양한 사용자들에게 제공
> API(Application Programming Interface):
컴퓨터나 컴퓨터 프로그램 사이의 연결을 할 수 있도록 제공
# 위키백과 QA API 란?
> 자연어로 기술된 질문의 의미를 분석하여,... | github_jupyter |
<div>
<img src="https://drive.google.com/uc?export=view&id=1vK33e_EqaHgBHcbRV_m38hx6IkG0blK_" width="350"/>
</div>
#**Artificial Intelligence - MSc**
This notebook is designed specially for the module
ET5003 - MACHINE LEARNING APPLICATIONS
Instructor: Enrique Naredo
###ET5003_GaussianProcesses
© All rights reserv... | github_jupyter |
The most common analytical task is to take a bunch of numbers in dataset and summarise it with fewer numbers, preferably a single number. Enter the 'average', sum all the numbers and divide by the count of the numbers. In mathematical terms this is known as the 'arithmetic mean', and doesn't always summarise a dataset ... | github_jupyter |
## Get the data
```
import os
import tarfile
import urllib.request
DOWNLOAD_ROOT = "http://spamassassin.apache.org/old/publiccorpus/"
HAM_URL = DOWNLOAD_ROOT + "20030228_easy_ham.tar.bz2"
SPAM_URL = DOWNLOAD_ROOT + "20030228_spam.tar.bz2"
SPAM_PATH = os.path.join("datasets", "spam")
def fetch_spam_data(ham_url=HAM_U... | github_jupyter |
```
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import model
from datetime import datetime
from datetime import timedelta
sns.set()
df = pd.read_csv('/home/husein/space/Stock-Prediction-Comparison/dat... | github_jupyter |
# 11 ODE Applications (Projectile motion) – Part 1
Let's apply our ODE solvers to some problems involving balls and projectiles.
The `integrators.py` file from [Lesson 10](http://asu-compmethodsphysics-phy494.github.io/ASU-PHY494//2018/02/20/10_ODEs/) is used here (and named [`ode.py`](https://github.com/ASU-CompMetho... | github_jupyter |
```
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import numpy as np
from shapely.geometry import Point
from sklearn.neighbors import KNeighborsRegressor
import rasterio as rst
from rasterstats import zonal_stats
%matplotlib inline
path = r"[CHANGE THIS PATH]\Wales\\"
data = pd.read_csv(p... | github_jupyter |
```
import numpy as np
import pandas as pd
import mlflow
import mlflow.sklearn
from gensim.utils import simple_preprocess
from sklearn.model_selection import train_test_split
from gensim.models.doc2vec import Doc2Vec, TaggedDocument
import gensim
from gensim import corpora
import nltk.stem
nltk.download('rslp')
from ge... | github_jupyter |
<a href="https://colab.research.google.com/github/escheytt/tensorflow/blob/master/FHLD_Class_1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import pandas as pd
from google.colab import files
uploaded = files.upload()
for fn in uploaded.keys... | github_jupyter |
# Basics of Signal Processing
**Authors**: Anmol Parande, Hoang Nguyen, Jordan Grelling
```
import numpy as np
import scipy
import matplotlib.pyplot as plt
from scipy.io import wavfile
import IPython.display as ipd
import scipy.signal as signal
import time
```
Throughout this notebook, we will be working with a clip ... | github_jupyter |
```
import json
import os
import tqdm
import pandas as pd
```
## I. convert emails text (both training and testing) into appropriate jsonl file format
### 6088 entries in training set ( 2000+ machine generated, the rest are human-written)
#### 4000+ are from email corpus, 2000+ are from gtp-2 generated and the ENRON ... | github_jupyter |
# Merge & Concat
En muchas ocasiones nos podemos encontrar con que los conjuntos de datos no se encuentran agregados en una única tabla. Cuando esto sucede, existen dos formas para unir la información de distintas tablas: **merge** y **concat**.
## Concat
La función `concat()` realiza todo el trabajo pesado de real... | github_jupyter |
# Principi AI
L'intelligenza artificiale dalla sua definizione significa avere la capacità di apprendere e di eseguire compiti in maniera simile a quella umana, è presente però la necessità di usare la programmazione per fare in modo che un calcolatore esibisca queste caratteristiche.
## Differenze tra programmazione... | github_jupyter |
```
import pandas as pd
from math import pow, sqrt
import time
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import CountVectorizer
ratings = pd.read_csv('./ml-latest-small/ratings.csv')
movies = pd.read_csv('./ml-latest-small/movies.csv')
movies
# users... | github_jupyter |
# for Mac OS
```
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
import math
import random
import gym
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.distributions import Categorical
from IPython.display import clear_output
import matpl... | github_jupyter |
# Clase 1: Introducción al curso
- IE0417: Diseño de Software para Ingeniería
## Introducción Profesor
- Esteban Zamora Alvarado
- [LinkedIn](https://www.linkedin.com/in/esteban-zamora-a54484102/)
#### Bach Ing. Eléctrica UCR (2014-2018)
- Énfasis en Compus y Redes
- PRIS-Lab: High Performance Computing (HPC)
#### ... | github_jupyter |
# Overview
This Jupyter Notebook takes in data from a Google Sheet that contains line change details and their associated high level categories and outputs a JSON file for the MyBus tool.
The output file is used by the MyBus tool's results page and contains the Line-level changes that are displayed there.
Run all ce... | github_jupyter |
# Region Based Data Analysis
The following notebook will go through prediction analysis for region based Multiple Particle Tracking (MPT) using OGD severity datasets for non-treated (NT) hippocampus, ganglia, thalamus, cortex, and striatum.
## Table of Contents
[1. Load Data](#1.-load-data)<br />
[2. Analys... | github_jupyter |
# DataFrames
DataFrames are the workhorse of pandas and are directly inspired by the R programming language. We can think of a DataFrame as a bunch of Series objects put together to share the same index. Let's use pandas to explore this topic!
```
import pandas as pd
import numpy as np
from numpy.random import randn
... | github_jupyter |
## Tutorial on how to implement periodic boundaries
This tutorial will show how to implement Periodic boundary conditions (where particles that leave the domain on one side enter again on the other side) can be implemented in Parcels
The idea in Parcels is to do two things:
1) Extend the fieldset with a small 'halo'
... | github_jupyter |
# Lab 3: Tables
Welcome to lab 3! This week, we'll learn about *tables*, which let us work with multiple arrays of data about the same things. Tables are described in [Chapter 6](https://www.inferentialthinking.com/chapters/06/Tables) of the text.
First, set up the tests and imports by running the cell below.
```
... | github_jupyter |
```
%matplotlib inline
import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc as pm
import scipy as sp
import seaborn as sns
sns.set(context='notebook', font_scale=1.2, rc={'figure.figsize': (12, 5)})
plt.style.use(['seaborn-colorblind', 'seaborn-darkgrid'])
RANDOM_SEED... | github_jupyter |
```
"""
===================================================
Faces recognition example using eigenfaces and SVMs
===================================================
The dataset used in this example is a preprocessed excerpt of the
"Labeled Faces in the Wild", aka LFW_:
http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz ... | github_jupyter |
<a href="https://colab.research.google.com/github/ash12hub/DS-Unit-2-Tree-Ensembles/blob/master/Ashwin_Raghav_Swamy_Decision_Tree_Classifier_CC.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Decision Tree Classifier: Coding Challenge
Decision tr... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
# TIC TAC TOE GAMES
```
import random as rd
from IPython.display import clear_output
from tabulate import tabulate
player = [1,2]
O = 'O'
X = 'X'
K = ' '
```
## Function
```
def gantian(iPLayer):
if iPLayer == 1:
iPLayer = 2
elif iPLayer == 2:
iPLayer = 1
return iPLayer
def isi(valcon,iPl... | github_jupyter |
```
import warnings
warnings.filterwarnings('ignore')
%matplotlib notebook
import pandas as pd
import numpy as np
from util import *
from sklearn.model_selection import train_test_split
from sklearn import metrics
from skater.core.global_interpretation.interpretable_models.brlc import BRLC
from skater.core.global_inte... | github_jupyter |
# Using QAOA to solve a UD-MIS problem
```
import numpy as np
import igraph
from itertools import combinations
import matplotlib.pyplot as plt
from pulser import Pulse, Sequence, Register
from pulser.simulation import Simulation
from pulser.devices import Chadoq2
from scipy.optimize import minimize
```
## 1. Intro... | github_jupyter |
```
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | github_jupyter |
```
from torch import nn
from collections import OrderedDict
import torch.nn.functional as F
import torch
from torch.utils.data import DataLoader
import torchvision
import random
from torch.utils.data import Subset
from matplotlib import pyplot as plt
from torchsummary import summary
from torchvision import transforms
... | github_jupyter |
# Node Embeddings and Skip Gram Examples
**Purpose:** - to explore the node embedding methods used for methods such as Word2Vec.
**Introduction-** one of the key methods used in node classification actually draws inspiration from natural language processing. This based in the fact that one approach for natural langua... | github_jupyter |
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