The OSTP’s actual offices sit next door to the White House, in the Eisenhower Executive Office Building. If you look clo

I’ve chosen a few popular datasets available in Scikit-learn for comparison. The train times andpredict times are compared. For easier reading, I’ve explicitly written how many times faster is the faiss-based clustering than Scikit-learn’s. For error comparison I’ve just written how many times lower error the faiss-based clustering achieves (since numbers are large and not very informative).

“The most important thing is that there be a relationship of trust between the president and the science advisor,” he told me, “so that when the science advisor has something to say that the president doesn’t want to hear, the president doesn’t assume that it’s wrong, doesn’t assume that the science advisor made it up to make his life more difficult.”

In the early days of the science advisor — in particular beginning with the Eisenhower administration when the first full-time advisor, James Killian, took office in 1957 — the men who held the position apparently enjoyed easy access to the Oval Office and held the president’s ear on a variety of issues.

As we can see, for K-Means clustering for small datasets (first 4 datasets) faiss-based version is slower for training and has a larger error. For prediction it works universally faster.

In both libraries we have to specify algorithm hyperparameters: number of clusters, number of restarts (each starting with other initial guesses) and maximal number of iterations.

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lity typically starts in childhood. The most common reason is from an insecure attachment being formed with our primary caregivers. If we weren’t provided a secure foundation for our own emotions growing up, we risk feeling uncomfortable around all emotions.

As we can see from the example, the core of the algorithm is searching for nearest neighbors, specifically nearest centroids, both for training and prediction. And that’s where faiss is orders of magnitude faster than Scikit-learn! It leverages great C implementation, concurrency wherever possible and even GPU, if you want.

The final quality of clustering is calculated as a sum of in-cluster distances, where for each cluster we calculate a sum of Euclidean distances between points in that cluster and its centroid. This is also called inertia.

All of those times have been measured with the time.process_time() function, that measures process time instead of wall clock time, for more accurate results. Results are averages of 100 runs, except for MNIST, where it took too long for Scikit-learn and I had to do 5 runs.

A great feature of faiss is that it has both installation and build instructions (installation docs) and an excellent documentation with examples (getting started docs). After the installation, we can write the actual clustering. The code is quite simple, since we just mimic the Scikit-learn API.

This is a repeated refrain from former advisors and observers of the position: that there must be a good, established relationship between the science advisor and advisee in order for the relationship to function. This makes sense, though it can feel somewhat disappointing — if the science being advocated is good and true, who cares who the messenger is? But we’ve seen that ideal, of course, get shot down time and again already when it comes to science and government.

in prediction we perform kNN search with k = 1, returning indices of nearest centroids from self.cluster_centers_ (index [1], since index.search() returns distances and indices)

K-Means is an iterative algorithm, which clusters the data points into k clusters, each represented with a mean / center point (a centroid). Training starts with some initial guesses and then alternates between two steps: assignment and update.

In the assignment phase we assign each point to the nearest cluster (using Euclidean distance between point and centroids) and in the update step we recalculate each centroid, calculating a mean point from all points assigned to that cluster in the current step.

Before 1957, the science advisor and an associated committee was attached to the Office of Defense Mobilization, essentially a major step removed from the White House. After Sputnik’s launch, though, it was moved officially under the White House umbrella. “It had been essentially a long-range, somewhat philosophical planning body, removed one notch from the President,” said Donald F. Hornig, who held the post in the latter part of Lyndon Johnson’s administration, in 1968. After 1957, it transformed from “a remote advisory committee to a group with a job to be done.”