Results for approximate-time

This gem contains command-line utility for solving 0/1 knapsack problem using branch-and-bound method, dynamic programming, simple heuristic (weight/price) and fully polynomial time approximation scheme. It can measure CPU and wall-clock time spent by solving a problem, compute relative error of the result and generate graphs from those values.

Hanny is a Hash-based Approximate Nearest Neighbor (ANN) search library in Ruby. Hash-based ANN converts vector data into binary codes and builds a hash table by using the binary codes as hash keys. To build the hash table, Hanny uses Locality Sensitive Hashing (LSH) of approximating cosine similarity. It is known that if the code length is sufficiently long (ex. greater than 128-bit), LSH can obtain high search performance. In the experiment, Hanny achieved about twenty times faster search speed than the brute-force search by Euclidean distance.

This gem provides a casually secure (you will never beat the pros) implementation of the One-Time Pad encryption technique, using multiple substitution cyphers suitable for educational and practical use. Knowing the code, you still have approximately 4.3 billion randon seeds to choose from to decode an unknown message.

With the happy proliferation of TDD, test suites are getting massive, and developer efficiency is dwindling as we wait for our tests to pass. There is a big tradeoff between making unit test more integrationish (and therefore more reliable) vs. making them very mocky, unity and fast. Mockumentary is a library for the later. It inspects the ActiveRecord universe and makes a series of AR mockeries that approximate model without hitting the database, or making any assertions. The assertions, they are still part of the developers job. Mocumentary has two types of AR mockeries: One is used within the Rails universe. It uses introspection to derive association and field information. The second is a static copy built from the first. This static version can be used outside the Rails test universe in a suite faster than the speed of Rails environment load time. Mocking isn't for everyone, so test-drive responsibly.

Noisy sensor data, approximations in the equations that describe the system evolution, and external factors that are not accounted for all place limits on how well it is possible to determine the system's state. The Kalman filter deals effectively with the uncertainty due to noisy sensor data and to some extent also with random external factors. The Kalman filter produces an estimate of the state of the system as an average of the system's predicted state and of the new measurement using a weighted average. The purpose of the weights is that values with better (i.e., smaller) estimated uncertainty are "trusted" more. The weights are calculated from the covariance, a measure of the estimated uncertainty of the prediction of the system's state. The result of the weighted average is a new state estimate that lies between the predicted and measured state, and has a better estimated uncertainty than either alone. This process is repeated at every time step, with the new estimate and its covariance informing the prediction used in the following iteration. This means that the Kalman filter works recursively and requires only the last "best guess", rather than the entire history, of a system's state to calculate a new state.

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