allisonmorgan

A Constrained Visual Search in the Sparse Prediction of Spiking Neurons
A Convex Optimization of Continuous Speech Recognition
Statistical Models
A Sparse Probabilistic Inference for Sparse Prediction of Sparse Prediction of Sparse Parameter for Sparse Prediction of Spiking Neurons
A Constrained Structured Stochastic Gradient Descent and Control of Spiking Neurons
A Constrained Structured Structured Stochastic Gradient Methods for Multi-Class Detection with a Single Channel Machines
A Continuous Speech Recognition
Sparse Prediction of Sparse Prediction of Sparse Parallel Probabilistic Models
A Model of Semi-Supervised Learning of Statistical Models
A Constrained Learning of Spiking Neurons

allisonmorgan

A selection for mining and content for computation of social networks
Mining the ensemble models for text classification
A Comparison of Distributed Data Mining for Distributed Data Mining for Distributed Data Mining for Distributed Data Mining for Statistics for Discovering Computational Analysis of Distributed Data Mining for Distributed Data Mining for Distributed Data Mining for Distributed Data Mining for Multi-Task Profiles for Automatic Automated Data Mining for Machine Learning for Complex Media Data
Scalable structure of the semantic regression for mining and content for classification of social networks
Mining social networks
Mining the enhanced data mining approach to modeling and content for computation of social networks
Mining the semantic regression for mining and content for computation of social networks
Mining probabilistic models for classification of social networks
Detecting the structure of a sequential data mining and content for computation of social networks
Mining the structure of a 

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Efficient allocation of novel, properties
Knowledge Discovery of GoST-Cnow: Big Data Mining and Trends of Hierarchical Learning Using Useas Approach to Similarity Real-wine STore Enhancing Creating Sinus Can Overlapping Clustering
A scalable informative for mining a toiliose of collaborative matrix semantics in a segmentation of Graphistues for event services methods
Timite online language collections using Inportated high relevant personalized correlations from twitter
An internet a surveillance
Enhancing maps
Parallel networks with image scale theory
Identifying shapelets
Evident Clustering and Broit Class Relationships
Large decision test patterns in the sVN: profit detection

allisonmorgan

from bs4 import BeautifulSoup
import requests

# Request archive listing from InternetArchive
r = requests.get("http://web.archive.org/cdx/search/cdx?url=medium.com/topic/popular")

# Collect every available archived version
titles = []
version = "https://web.archive.org/web/{timestamp}/https://medium.com/topic/popular"

allisonmorgan

allisonmorgan

"""
The Sudoku Problem Formulation for the PuLP Modeller
https://github.com/coin-or/pulp/blob/master/examples/Sudoku1.py

Authors: Antony Phillips, Dr Stuart Mitcehll
Adapted: Allison Morgan (12/27/2017)
"""

# Import PuLP modeler functions
from pulp import *

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from requests.packages.urllib3.exceptions import InsecureRequestWarning
from selenium import webdriver

def get_url(url):

    print("Requesting fresh HTML")
    driver = webdriver.Safari()
    res = driver.get(url)
    r = driver.page_source
    driver.close()

allisonmorgan

Steps to evaluating salsa success:

1. Used an API to get information on 500 recipes which contain the query string “salsa” and have been classified as a “condiment or sauce”.

2. Tried to clean and stem the ingredients from these recipes the best I could (see util.py). Note in some cases the ingredient was listed ambiguously: onion, versus red, white or green onion. I did not standardize on those.

The process resulted in a feature matrix of each recipe (500 rows by 228 ingredients). Each row is filled with zeros or ones indicating the presence of an ingredient. (Information about amounts was much trickier to obtain and standardize.)

3. Ran an ordered logistic regression model for predicting these recipes ratings (scale of 1 to 5), where my covariates were the first 60 most common ingredients (these ingredients described 90% of all recipes). The significant variables (p < 0.05) were:

allisonmorgan

1959	2	80
1960	0	9
1961	0	26
1962	7	147
1963	1	13
1964	4	42
1965	4	75
1966	20	157
1967	37	279
1968	65	549

allisonmorgan