code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
# The Theta Model
The Theta model of Assimakopoulos & Nikolopoulos (2000) is a simple method for forecasting the involves fitting two $\theta$-lines, forecasting the lines using a Simple Exponential Smoother, and then combining the forecasts from the two lines to produce the final forecast. The model is implemented i... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
%matplotlib inline
data_label = pd.read_csv("data(with_label).csv")
```
### 30 day death age
```
fig = plt.figure(figsize=(12,6))
sns.set_style('darkgrid')
ax = sns.violinplot(x="thirty_days", hue="gender", y="age",data=d... | github_jupyter |
# Logic: `logic.py`; Chapters 6-8
This notebook describes the [logic.py](https://github.com/aimacode/aima-python/blob/master/logic.py) module, which covers Chapters 6 (Logical Agents), 7 (First-Order Logic) and 8 (Inference in First-Order Logic) of *[Artificial Intelligence: A Modern Approach](http://aima.cs.berkele... | 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 |
# Classification algorithms
In the context of record linkage, classification refers to the process of dividing record pairs into matches and non-matches (distinct pairs). There are dozens of classification algorithms for record linkage. Roughly speaking, classification algorithms fall into two groups:
- **supervised... | github_jupyter |
# Deep Matrix Factorisation
Matrix factorization with deep layers
```
import sys
sys.path.append("../")
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import pandas as pd
from IPython.display import SVG, display
import matplotlib.pyplot as plt
import seaborn as sns
from reco.preprocess import ... | github_jupyter |
# Convolutional Layer
In this notebook, we visualize four filtered outputs (a.k.a. activation maps) of a convolutional layer.
In this example, *we* are defining four filters that are applied to an input image by initializing the **weights** of a convolutional layer, but a trained CNN will learn the values of these w... | github_jupyter |
# AdaDelta compared to AdaGrad
Presented during ML reading group, 2019-11-12.
Author: Ivan Bogdan-Daniel, ibogdanidaniel@gmail.com
```
#%matplotlib notebook
%matplotlib inline
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
print(f'Numpy version: ... | github_jupyter |
```
#!/usr/bin/env python
# coding: utf-8
# Imports
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.metrics import f1_score, accuracy_score
from sklearn.metrics import roc_curve, confusion_matrix
import torch
import torch.nn as nn # All neural network mod... | github_jupyter |
<a href="https://colab.research.google.com/github/r5racker/012_RahilBhensdadia/blob/main/Lab_05_1_linear_regression_scratch.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
# Import Numpy & PyTorch
import numpy as np
```
A tensor is a number, ve... | github_jupyter |
# Exploratory Data Analysis of AllenSDK
```
# Only for Colab
#!python -m pip install --upgrade pip
#!pip install allensdk
```
## References
- [[AllenNB1]](https://allensdk.readthedocs.io/en/latest/_static/examples/nb/visual_behavior_ophys_data_access.html) Download data using the AllenSDK or directly from our Amazon... | github_jupyter |
```
#
# libraries
#
import pandas as pd
from sklearn.linear_model import LinearRegression as OLS
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns
#
#
# utility function for plotting histograms in a grid
#
def plot_h... | github_jupyter |
# Consumption Equivalent Variation (CEV)
1. Use the model in the **ConsumptionSaving.pdf** slides and solve it using **egm**
2. This notebooks estimates the *cost of income risk* through the Consumption Equivalent Variation (CEV)
We will here focus on the cost of income risk, but the CEV can be used to estimate the ... | github_jupyter |
# Facial Expression Recognizer
```
#The OS module in Python provides a way of using operating system dependent functionality.
#import os
# For array manipulation
import numpy as np
#For importing data from csv and other manipulation
import pandas as pd
#For displaying images
import matplotlib.pyplot as plt
import m... | github_jupyter |
2017
Machine Learning Practical
University of Edinburgh
Georgios Pligoropoulos - s1687568
Coursework 4 (part 7)
### Imports, Inits, and helper functions
```
jupyterNotebookEnabled = True
plotting = True
coursework, part = 4, 7
saving = True
if jupyterNotebookEnabled:
#%load_ext autoreload
%reload_ext aut... | github_jupyter |
# Exercises 06 - Strings and Dictionaries
## 0. Length of Strings
Let's start with a string lightning round to warm up. What are the lengths of the strings below?
For each of the five strings below, predict what `len()` would return when passed that string. Use the variable `length` to record your answer.
```
a = "... | github_jupyter |
<a href="https://colab.research.google.com/github/nationalarchives/TechneTraining/blob/main/Code/Techne_ML_workbook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Set up variables and install useful library code
```
import sys
data_source = "Git... | github_jupyter |
```
import re
import json
import pandas as pd
import numpy as np
from collections import deque
```
## Process dataset
```
base_folder = "../movies-dataset/"
movies_metadata_fn = "movies_metadata.csv"
credits_fn = "credits.csv"
links_fn = "links.csv"
```
## Process movies_metadata data structure/schema
```
metadat... | github_jupyter |
```
import json
import os
import time
import requests
import psycopg2
import os
import json
database_dict = {
"database": os.environ.get("POSTGRES_DB"),
"user": os.environ.get("POSTGRES_USERNAME"),
"password": os.environ.get("POSTGRES_PASSWORD"),
"host": os.environ.get("POSTGRES_WRITER"... | github_jupyter |
```
import sympy as sp
import numpy as np
x = sp.symbols('x')
p = sp.Function('p')
l = sp.Function('l')
poly = sp.Function('poly')
p3 = sp.Function('p3')
p4 = sp.Function('p4')
```
# Introduction
Last time we have used Lagrange basis to interpolate polynomial. However, it is not efficient to update the interpolating ... | github_jupyter |
```
import ml
reload(ml)
from ml import *
import timeit
import scipy
import operator
import collections
import numpy as np
import pandas as pd
from scipy import stats
import seaborn as sns
from collections import Counter
import matplotlib.pyplot as plt
from __future__ import division
from matplotlib.colors import Liste... | github_jupyter |
# Spark DataFrames Project Exercise
Let's get some quick practice with your new Spark DataFrame skills, you will be asked some basic questions about some stock market data, in this case Walmart Stock from the years 2012-2017. This exercise will just ask a bunch of questions, unlike the future machine learning exercise... | github_jupyter |
<img width="10%" alt="Naas" src="https://landen.imgix.net/jtci2pxwjczr/assets/5ice39g4.png?w=160"/>
# HubSpot - Get closed deals weekly
<a href="https://app.naas.ai/user-redirect/naas/downloader?url=https://raw.githubusercontent.com/jupyter-naas/awesome-notebooks/master/HubSpot/HubSpot_Get_closed_deals_weekly.ipynb" t... | github_jupyter |
```
import numpy as np, pandas as pd, matplotlib.pyplot as plt
import os
import seaborn as sns
sns.set()
root_path = r'C:\Users\54638\Desktop\Cannelle\Excel handling'
input_path = os.path.join(root_path, "input")
output_path = os.path.join(root_path, "output")
%%time
# this line magic function should always be put on ... | github_jupyter |
# fuzzy_pandas examples
These are almost all from [Max Harlow](https://twitter.com/maxharlow)'s [awesome NICAR2019 presentation](https://docs.google.com/presentation/d/1djKgqFbkYDM8fdczFhnEJLwapzmt4RLuEjXkJZpKves/) where he demonstrated [csvmatch](https://github.com/maxharlow/csvmatch), which fuzzy_pandas is based on.... | github_jupyter |
<h1>Demand forecasting with BigQuery and TensorFlow</h1>
In this notebook, we will develop a machine learning model to predict the demand for taxi cabs in New York.
To develop the model, we will need to get historical data of taxicab usage. This data exists in BigQuery. Let's start by looking at the schema.
```
impo... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from numpy.linalg import norm
import pandas as pd
plt.style.use('ggplot')
deaths = pd.read_csv('deaths.txt')
pumps = pd.read_csv('pumps.txt')
print deaths.head()
print pumps.head()
plt.plot(deaths['X'], deaths['Y'], 'o', lw=0, mew=1, mec='0.9', m... | github_jupyter |
# Concise Implementation of Linear Regression
With the development of deep learning frameworks, it has become increasingly easy to develop deep learning applications. In practice, we can usually implement the same model, but much more concisely than how we introduce it in the previous section. In this section, we will... | github_jupyter |
```
from airsenal.framework.utils import *
from airsenal.framework.bpl_interface import get_fitted_team_model
from airsenal.framework.season import get_current_season, CURRENT_TEAMS
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
%matplotlib inline
model_team = get_fitted_team_model(get_cur... | github_jupyter |
# TV Script Generation
In this project, you'll generate your own [Seinfeld](https://en.wikipedia.org/wiki/Seinfeld) TV scripts using RNNs. You'll be using part of the [Seinfeld dataset](https://www.kaggle.com/thec03u5/seinfeld-chronicles#scripts.csv) of scripts from 9 seasons. The Neural Network you'll build will ge... | github_jupyter |
# Deterministic point jet
```
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import matplotlib.pylab as pl
```
\begin{equation}
\partial_t \zeta = \frac{\zeta_{jet}}{\tau} - \mu \zeta + \nu_\alpha \nabla^{2\alpha} - \beta \partial_x \psi - J(\psi, \zeta) \zeta
\end{equation}
Here $\zeta_... | github_jupyter |
# Building Autonomous Trader using mt5se
## How to setup and use mt5se
### 1. Install Metatrader 5 (https://www.metatrader5.com/)
### 2. Install python package Metatrader5 using pip
#### Use: pip install MetaTrader5 ... or Use sys package
### 3. Install python package mt5se using pip
#### Use: pip install mt5se ... | github_jupyter |
# Introduction to Deep Learning with PyTorch
In this notebook, you'll get introduced to [PyTorch](http://pytorch.org/), a framework for building and training neural networks. PyTorch in a lot of ways behaves like the arrays you love from Numpy. These Numpy arrays, after all, are just tensors. PyTorch takes these tenso... | github_jupyter |
<a href="https://colab.research.google.com/github/Lambda-School-Labs/bridges-to-prosperity-ds-d/blob/SMOTE_model_building%2Ftrevor/notebooks/Modeling_off_original_data_smote_gridsearchcv.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
This notebook ... | github_jupyter |
##### Copyright 2019 The TensorFlow Hub Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
... | github_jupyter |
<a href="https://cognitiveclass.ai"><img src = "https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png" width = 400> </a>
<h1 align=center><font size = 5>Waffle Charts, Word Clouds, and Regression Plots</font></h1>
## Introduction
In this lab, we will learn how to create word clouds and waffle charts... | github_jupyter |
<h2>Factorization Machines - Movie Recommendation Model</h2>
Input Features: [userId, moveId] <br>
Target: rating <br>
```
import numpy as np
import pandas as pd
# Define IAM role
import boto3
import re
import sagemaker
from sagemaker import get_execution_role
# SageMaker SDK Documentation: http://sagemaker.readthed... | github_jupyter |
# The overview of the basic approaches to solving the Uplift Modeling problem
<br>
<center>
<a href="https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg">
</a>
<br>
<b><a hr... | github_jupyter |
```
s = 'abc'
s.upper()
# L E G B
# local
# enclosing
# global
# builtins
globals()
globals()['s']
s.upper()
dir(s)
s.title()
x = 'this is a bunch of words to show to people'
x.title()
for attrname in dir(s):
print attrname, s.attrname
for attrname in dir(s):
print attrname, getattr(s, attrname)
s.upper
getattr... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
**IMPORTANT NOTE:** This notebook is designed to run as a Colab. Click the button on top that says, `Open in Colab`, to run this notebook as a Colab. Running the notebook on your local machine might result in some of the code blocks throwing errors.
```
#@title Licensed un... | github_jupyter |
```
# ###############################################
# ########## Default Parameters #################
# ###############################################
start = '2016-06-16 22:00:00'
end = '2016-06-18 00:00:00'
pv_nominal_kw = 5000 # There are 3 PV locations hardcoded at node 7, 8, 9
inverter_sizing = 1.05
inverter_q... | github_jupyter |
# Basic Examples with Different Protocols
## Prerequisites
* A kubernetes cluster with kubectl configured
* curl
* grpcurl
* pygmentize
## Setup Seldon Core
Use the setup notebook to [Setup Cluster](seldon_core_setup.ipynb) to setup Seldon Core with an ingress - either Ambassador or Istio.
Then port-forward ... | github_jupyter |
# Welcome to Python reference
this notebook contains pretty much every thing, if you want to refresh your knowledge or what so ever
it will help you ;D
<ul>
<li>Data types<ul>
<li>Numbers</li>
<li>Strings</li>
<li>Lists</li>
<li>Dictionairies</li>
<li>Booleans</li>
... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import re
import time
import warnings
import sqlite3
from sqlalchemy import create_engine # database connection
import csv
import os
warnings.filterwarnings("ignore")
import datetime as dt
import numpy as np
from nltk.corpus import stopwords
from sklearn.decomposi... | github_jupyter |
# 01. Tabular Q Learning
Tabular Q Learning을 실습해봅니다.
- 모든 state의 value function을 table에 저장하고 테이블의 각 요소를 Q Learning으로 업데이트 하는 것으로 학습합니다.
## Colab 용 package 설치 코드
```
!pip install gym
import tensorflow as tf
import numpy as np
import random
import gym
# from gym.wrappers import Monitor
np.random.seed(777)
tf.set_rand... | github_jupyter |
## **Accessing Elements in ndarays:**
Elements can be accessed using indices inside square brackets, [ ]. NumPy allows you to use both positive and negative indices to access elements in the ndarray. Positive indices are used to access elements from the beginning of the array, while negative indices are used to access ... | github_jupyter |
```
(ql:quickload :delta-vega)
```
# Single-View Plots
## Bar Charts
### Simple Bar Chart
A bar chart encodes quantitative values as the extent of rectangular bars.
```
(jupyter:vega-lite
(delta-vega:make-top-view :description "A simple bar chart with embedded data."
:data (delta-vega:ma... | github_jupyter |
# Performing Large Numbers of Calculations with Thermo in Parallel
A common request is to obtain a large number of properties from Thermo at once. Thermo is not NumPy - it cannot just automatically do all of the calculations in parallel.
If you have a specific property that does not require phase equilibrium calcula... | github_jupyter |
```
import os
import numpy as np
import pandas as pd
import random
from transformers import (AdamW, get_linear_schedule_with_warmup, logging,
ElectraConfig, ElectraTokenizer, ElectraForSequenceClassification,
ElectraPreTrainedModel, ElectraModel)
import torch
im... | github_jupyter |
```
## plot plasma density
%pylab inline
import numpy as np
from matplotlib import pyplot as plt
from ReadBinary import *
filename = "../data/Wp2-x.data"
arrayInfo = GetArrayInfo(filename)
print("typeCode: ", arrayInfo["typeCode"])
print("typeSize: ", arrayInfo["typeSize"])
print("shape: ", arrayInfo["shape"])
prin... | github_jupyter |
# Creating images using shapes and simple simulation with attenuation
This exercise shows how to create images via geometric shapes. It then uses forward projection without
and with attenuation.
It is recommended you complete the [Introductory](../Introductory) notebooks first (or alternatively the [display_and_projec... | github_jupyter |
Dictionary and its default functions.
```
# Creating a Dictionary
# with Integer Keys
Dict = {1: 'Geeks', 2: 'For', 3: 'Geeks'}
print("\nDictionary with the use of Integer Keys: ")
print(Dict)
# Creating a Dictionary
# with Mixed keys
Dict = {'Name': 'Geeks', 1: [1, 2, 3, 4]}
print("\nDictionary with the use ... | github_jupyter |
<a href="https://colab.research.google.com/github/ymoslem/OpenNMT-Tutorial/blob/main/2-NMT-Training.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
# Install OpenNMT-py 2.x
!pip3 install OpenNMT-py
```
# Prepare Your Datasets
Please make sure y... | github_jupyter |
## Distributed Training with Chainer and ChainerMN
Chainer can train in two modes: single-machine, and distributed. Unlike the single-machine notebook example that trains an image classification model on the CIFAR-10 dataset, we will write a Chainer script that uses `chainermn` to distribute training to multiple insta... | github_jupyter |
```
try:
from openmdao.utils.notebook_utils import notebook_mode
except ImportError:
!python -m pip install openmdao[notebooks]
```
# NonlinearBlockGS
NonlinearBlockGS applies Block Gauss-Seidel (also known as fixed-point iteration) to the
components and subsystems in the system. This is mainly used to solve ... | github_jupyter |
# AVL Tree
```
from Module.classCollection import Queue
class AVLNode:
def __init__(self, data):
self.data = data
self.leftChild = None
self.rightChild = None
self.height = 1
def preorderTraversal(rootNode):
if not rootNode:
return
print(rootNode.data)
... | github_jupyter |
# DA320 Assignment 7: Mongo Charts
Jon Kaimmer
DA320
Winter2022
### Introduction
Lets import our chirp data and then chart it.
```
#IMPORTS
import pymongo
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import json as json
import plotly.express as px
# import war... | github_jupyter |
```
#remove cell visibility
from IPython.display import HTML
tag = HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide()
} else {
$('div.input').show()
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
Promijeni vidljivost ... | github_jupyter |
# Classification Metrics Spark Example
Classification metrics are used to calculate the performance of binary predictors based on a binary target. They are used extensively in other Iguanas modules. This example shows how they can be applied in Spark and how to create your own.
## Requirements
To run, you'll need th... | github_jupyter |
# Template Matching
### Full Image
```
import cv2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
full = cv2.imread('../images/diver_enhanced.jpg')
full = cv2.cvtColor(full, cv2.COLOR_BGR2RGB)
plt.imshow(full)
```
### Template Image
```
face= cv2.imread('../images/dolphin_template.jpg')
face =... | github_jupyter |
Many features of TensorFlow including their computational graphs lend themselves naturally to being computed in parallel. Computational graphs can be split over different processors as well as in processing different batches. This recipe demonstrates how to access different processors on the same machine.
## Getting r... | github_jupyter |
```
import sys
sys.path.append("../scripts/")
from ideal_robot import *
from scipy.stats import expon, norm, uniform
class Robot(IdealRobot):
def __init__(
self,
pose,
agent=None,
sensor=None,
color="black",
noise_per_meter=5,
noise_std=math.pi / 60,
... | github_jupyter |
## Search algorithms within Optuna
In this notebook, I will demo how to select the search algorithm with Optuna. We will compare the use of:
- Grid Search
- Randomized search
- Tree-structured Parzen Estimators
- CMA-ES
We can select the search algorithm from the [optuna.study.create_study()](https://optuna.readth... | github_jupyter |
## Duplicated features
```
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
```
## Read Data
```
data = pd.read_csv('../UNSW_Train.csv')
data.shape
# check the presence of missing data.
# (there are no missing data in this dataset)
[col for col in data.columns if data[col].... | github_jupyter |
# Run Experiments
You can use the Azure Machine Learning SDK to run code experiments that log metrics and generate outputs. This is at the core of most machine learning operations in Azure Machine Learning.
## Connect to your workspace
All experiments and associated resources are managed within your Azure Machine Le... | github_jupyter |
## Install Lib API
```
! pip install https://dnaink.jfrog.io/artifactory/dna-ink-pypi/model-fkeywords/0.1.0/model_fkeywords-0.1.0-py3-none-any.whl
! python -m spacy download pt_core_news_sm
```
## Import libs
```
import pandas as pd
import spacy
import nltk
nltk.download('stopwords')
from api_model import nlextract
... | github_jupyter |
```
import pandas as pd
import os
import numpy as np
from datetime import timedelta
from sklearn.ensemble import RandomForestRegressor
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
in_dir = 'D:\\Toppan\\2017-11-20 全データ\\処理済(機械ごと)\\vectorized'
out_dir = in_dir
holiday_path = 'D:\\Toppan\\201... | github_jupyter |
# Python Basics with Numpy (optional assignment)
Welcome to your first assignment. This exercise gives you a brief introduction to Python. Even if you've used Python before, this will help familiarize you with functions we'll need.
**Instructions:**
- You will be using Python 3.
- Avoid using for-loops and while-lo... | github_jupyter |
This notebook was prepared by [Donne Martin](https://github.com/donnemartin). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).
# Solution Notebook
## Problem: Generate a list of primes.
* [Constraints](#Constraints)
* [Test Cases](#Test-Cases)
* [Algorithm](#Algor... | github_jupyter |
```
import matplotlib
matplotlib.use('nbagg')
import matplotlib.animation as anm
import matplotlib.pyplot as plt
import math
import matplotlib.patches as patches
import numpy as np
%matplotlib widget
class World:
def __init__(self, time_span, time_interval, debug=False):
self.objects = []
self.deb... | github_jupyter |
## Overview/To-Do
This one tries a different, more realistic detecor layouout.
```
%matplotlib inline
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from BurstCube.LocSim.GRB import *
from BurstCube.LocSim.Detector import *
from BurstCube.LocSim.Spacecraft import *
from BurstCube.LocSim.Stat... | github_jupyter |
```
from __future__ import print_function
import matplotlib.pyplot as plt
%matplotlib inline
import SimpleITK as sitk
print(sitk.Version())
from myshow import myshow
# Download data to work on
%run update_path_to_download_script
from downloaddata import fetch_data as fdata
OUTPUT_DIR = "Output"
```
This section of t... | github_jupyter |
# BUSINESS ANALYTICS
You are the business owner of the retail firm and want to see how your company is performing. You are interested in finding out the weak areas where you can work to make more profit. What all business problems you can derive by looking into the data?
```
# Importing certain libraries
import pandas... | github_jupyter |
### LOAD DATA
```
import csv # for csv file import
import numpy as np
import os
import cv2
import math
#from keras import optimizers
from sklearn.utils import shuffle # to shuffle data in generator
from sklearn.model_selection import train_test_split # to split data into Training + Validation
def get_file_data(file_pa... | github_jupyter |
# Transfer Learning Template
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
import os, json, sys, time, random
import numpy as np
import torch
from torch.optim import Adam
from easydict import EasyDict
import matplotlib.pyplot as plt
from steves_models.steves_ptn import Steves_Prototypical_Network
... | github_jupyter |
# Week 4
## Overview
Yay! It's week 4. Today's we'll keep things light.
I've noticed that many of you are struggling a bit to keep up and still working on exercises from the previous week. Thus, this week we only have two components with no lectures and very little reading.
## Informal intro
[![IMAGE ALT TEXT HE... | github_jupyter |
<a href="http://cocl.us/pytorch_link_top">
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN/notebook_images%20/Pytochtop.png" width="750" alt="IBM Product " />
</a>
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN... | github_jupyter |
## Introduction
In real world, there exists many huge graphs that can not be loaded in one machine,
such as social networks and citation networks.
To deal with such graphs, PGL develops a Distributed Graph Engine Framework to
support graph sampling on large scale graph networks for distributed GNN training.
In thi... | github_jupyter |
# "[ML] What's the difference between a metric and a loss?"
- toc:true
- branch: master
- badges: false
- comments: true
- author: Peiyi Hung
- categories: [learning, machine learning]
In machine learning, we usually use two values to evaluate our model: a metric and a loss. For instance, if we are doing a binary cla... | github_jupyter |
# Convolutional Neural Networks
A CNN is made up of basic building blocks defined as tensor, neurons, layers and kernel weights and biases. In this lab, we use PyTorch to build a image classifier using CNN. The objective is to learn CNN using PyTorch framework.
Please refer to the link below for know more about CNN
htt... | github_jupyter |
# Canonical correlation analysis in python
In this notebook, we will walk through the solution to the basic algrithm of canonical correlation analysis and compare that to the output of implementations in existing python libraries `statsmodels` and `scikit-learn`.
```
import numpy as np
from scipy.linalg import sqrtm
... | github_jupyter |
```
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.model_selection ... | github_jupyter |
# Testing Various Machine Learning Models
I would like to create model that can identify individuals at the greatest risk of injury three months prior to when it occurs. In order to do this, I will first complete feature selection using a step-forward approach to optimize recall. Then I will complete some basic EDA. H... | github_jupyter |
```
#!pip install gretel-synthetics --upgrade
#!pip install matplotlib
#!pip install smart_open
# load source training set
import logging
import os
import sys
import pandas as pd
from smart_open import open
source_file = "https://gretel-public-website.s3-us-west-2.amazonaws.com/datasets/uci-heart-disease/train.csv"
an... | github_jupyter |
# Methods of Approximating Ambient Light Level Using Camera Output
## 1. Helper Functions
These helper functions can largely be ignored, but make sure to run each cell before using the algorithm section (Section 2).
### 1.1 Get Camera Capture
Get capture from camera.
```
import matplotlib.pyplot as plt
import cv2
... | github_jupyter |
```
%matplotlib inline
from matplotlib import style
style.use('fivethirtyeight')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import datetime as dt
```
# Reflect Tables into SQLAlchemy ORM
```
# Python SQL toolkit and Object Relational Mapper
import sqlalchemy
from sqlalchemy.ext.automap imp... | github_jupyter |
## Homework 3 and 4 - Applications Using MRJob
```
# general imports
import os
import re
import sys
import time
import random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# tell matplotlib not to open a new window
%matplotlib inline
# automatically reload modules
%reload_ext autoreload
%au... | github_jupyter |
# Mega-Meta Functional Connectivity Pipeline
_________
```
CHANGE LOG
08/14 . -MJ changed "dur = rel_events.loc[o,'durTR']" to "dur = rel_events.loc[i,'durTR'] -> 0 to i
05/22/2019 - JMP initial commit
05/28/2019 - JMP added 'rest' TR extration
```
#### Description
extracts signal from Power ROI spheres (264) for a g... | github_jupyter |
# Debugging Numba problems
## Common problems
Numba is a compiler, if there's a problem, it could well be a "compilery" problem, the dynamic interpretation that comes with the Python interpreter is gone! As with any compiler toolchain there's a bit of a learning curve but once the basics are understood it becomes eas... | github_jupyter |
# Introduction to Strings
---
This notebook covers the topic of strings and their importance in the world of programming. You will learn various methods that will help you manipulate these strings and make useful inferences with them. This notebook assumes that you have already completed the "Introduction to Data Scie... | github_jupyter |
# Find UniProt IDs in ChEMBL targets
Ultimately, we are interested in getting [activity data from ChEMBl](/chembl-27/query_local_chembl-27.ipynb) we need to account for three components:
* The compound being measured
* The target the compound binds to
* The assay where this measurement took place
So, to find all act... | github_jupyter |
```
%matplotlib inline
from pathlib import Path
from pandas import DataFrame,Series
from pandas.plotting import scatter_matrix
from sklearn.model_selection import train_test_split
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
import pandas as pd
from... | 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 |
<img src="https://nlp.johnsnowlabs.com/assets/images/logo.png" width="180" height="50" style="float: left;">
## Deep Learning NER
In the following example, we walk-through a LSTM NER model training and prediction. This annotator is implemented on top of TensorFlow.
This annotator will take a series of word embedding... | github_jupyter |
# Naive Bayes Classifier (Self Made)
### 1. Importing Libraries
```
import numpy as np
import matplotlib.pyplot as plt
import os
import pandas as pd
from sklearn.metrics import r2_score
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from... | github_jupyter |
```
import numpy as np
import pandas as pd
import pickle
import time
import itertools
import matplotlib
matplotlib.rcParams.update({'font.size': 17.5})
import matplotlib.pyplot as plt
%matplotlib inline
import sys
import os.path
sys.path.append( os.path.abspath(os.path.join( os.path.dirname('..') , os.path.pardir ))... | github_jupyter |
```
from keras.applications.vgg19 import VGG19
from keras.models import Model
from keras.applications.vgg19 import preprocess_input
from keras.preprocessing import image
import numpy as np
import pickle
import os
val = []
# Leemos el archivo annotation.txt y guardamos los datos en un vector (por palabras)
with open('/h... | github_jupyter |
```
import arrow as arw
import matplotlib.pyplot as plt
import netCDF4 as nc
import numpy as np
import pandas as pd
import xarray as xr
from salishsea_tools import places, teos_tools
%matplotlib inline
hindcast_dataset = xr.open_dataset(
'https://salishsea.eos.ubc.ca/erddap/griddap/ubcSSg3DTracerFields1hV17-0... | github_jupyter |
# Dense Sentiment Classifier
In this notebook, we build a dense neural net to classify IMDB movie reviews by their sentiment.
```
#load watermark
%load_ext watermark
%watermark -a 'Gopala KR' -u -d -v -p watermark,numpy,pandas,matplotlib,nltk,sklearn,tensorflow,theano,mxnet,chainer,seaborn,keras,tflearn,bokeh,gensim
... | github_jupyter |
```
import time
import warnings
import logging
import tensorflow as tf
```
### Decorate functions with tf.function
Functions can be faster than eager code, especially for graphs with many small ops. But for graphs with a few expensive ops (like convolutions), you may not see much speedup.
```
@tf.function
def add(a,... | github_jupyter |
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