## demonstrating a simple expert system with cascading effects (Clojure version)

We originally implemented this demonstration in CLIPS (see this post) but decided Clojure would provide a better platform due to advantages discussed below. “An expert system is a program capable of pairing up a set of facts with a set of rules to those facts, and execute some actions based on the matching rules. [1]” […]

## encoding fashion rules into mathematical data structures (part one)

As we build our fashion recommendation engine, we seek rules to populate it with. With few exceptions (e.g. [1]), we find these rules encoded in prose or infographic form, rather than a semantic web form suitable for computation. For example, [2] provides written advice on dressing fabulously for a “rectangular” women’s body type. The writers […]

## demonstrating a simple expert system with cascading effects (CLIPS version)

We demonstrate a more modern implementation of this system in Clojure here. “An expert system is a program capable of pairing up a set of facts with a set of rules to those facts, and execute some actions based on the matching rules. [1]” At 180 centimeters, Emily Williams stands tall. She carries a “rectangular” […]

## summary of our FOREX experiments and next steps

We started by building a support vector machine model based on features used in harmonic trading, with the idea that ideal “harmonic” ratios can be learned rather than explicitly specified. This worked on testing sets but not when we started trading with it. We abandoned the model before we realized that we need to manage […]

## artificial intelligence in fashion (part two: a first step)

In my last post, “artificial intelligence in fashion (part one: brainstorming)“, I produced a list of big ideas on how machine learning and artificial intelligence may be applied to the fashion industry. I addressed sizing, marketing, and design activities when brainstorming this list. This post doesn’t specifically cover an artificial intelligence solution, but it lays […]

## artificial intelligence in fashion (part one: brainstorming)

Brainstorming as usual: Fashion dictums involve many IF-THEN-ELSE rules. One can convert this into a decision engine (inference engine). User specifies their body shape, and a recommendation engine selects suitable clothing for them, taking into account the user’s tastes. Upload an image of a dress you want to buy, and specify the dress’s given size. […]

## rapidly identifying potential CRISPR/Cas9 off-target sites (part one)

Before we can score segments in the genome having a small number of mismatches to a CRISPR for their off-target risk, we must first find these segments. Searching for every possible mismatch permutation proves computationally expensive, so we apply the following heuristic: We only search for mismatches in the top positions relevant to CRISPR efficiency. […]

## pseudo-harmonic FOREX prediction with machine learning (part one)

“Harmonic” trading methods seek patterns in the relationships between neighboring peaks and valleys in the time series. Particularly, harmonic traders seek pre-specified ratios in the price differences among a series of peaks and valleys. For example, a trader might observe the following pattern: Let A, B, C, D, and E be the points in the […]

## picking stocks by graph database (part 2: machine learning)

In our last post, we demonstrated a graph database created to enable study of the stock market, particularly the study of causality relationships. So how to proceed from there? At this stage we want to pick winning stocks, not write an academic paper, so our focus turns toward practical machine learning. Source Data We start […]

## picking stocks by graph database (part one)

Historical stock price data comes readily available at daily resolution. So we calculated the Granger causality for each pair of stocks we hold data for, at one and two day lags (testing the question “does daily percent change in volume for stock X Granger cause daily percent change in adjusted close price for stock Y?”). […]