Value Members

1.  object AutoMPG_Regression extends App

   The AutoMPG_Regression object performs regression on the UCI AutoMPG dataset.

   The AutoMPG_Regression object performs regression on the UCI AutoMPG dataset.

   See also

   archive.ics.uci.edu/ml/datasets/Auto+MPG > run-main apps.analytics.AutoMPG_Regression

2.  object Census extends App

   The Census object is a sample application that performs basic analytics on census data.

   The Census object is a sample application that performs basic analytics on census data. FIX: must download the following file.

   See also

   archive.ics.uci.edu/ml/machine-learning-databases/census1990-mld/ > run-main apps.analytics.Census

3.  object Pendulum extends App

   The Pendulum object is a simple application that fits data governed by a differential equation (pendulum equations) using Principal Differential Analysis.

   The Pendulum object is a simple application that fits data governed by a differential equation (pendulum equations) using Principal Differential Analysis. FIX - make a Scala DSL for readble system of Ordinary Differential Equations (ODEs) FIX - extend to Differential Algebraic Equations (DAEs)

   See also

   www.math.ucdavis.edu/~tracy/courses/math22B/22BBook.pdf > run-main apps.analytics.Pendulum

4.  object PokerBayes extends App

   The PokerBayes object is a sample application that uses the NaiveBayes and AugNaiveBayes classes.

   The PokerBayes object is a sample application that uses the NaiveBayes and AugNaiveBayes classes. Classify a poker hand consisting of 5 cards. The hand is to be classified as one of the following types of hands:

   0: Nothing in hand; not a recognized poker hand 1: One pair; one pair of equal ranks within five cards 2: Two pairs; two pairs of equal ranks within five cards 3: Three of a kind; three equal ranks within five cards 4: Straight; five cards, sequentially ranked with no gaps 5: Flush; five cards with the same suit 6: Full house; pair + different rank three of a kind 7: Four of a kind; four equal ranks within five cards 8: Straight flush; straight + flush 9: Royal flush; {Ace, King, Queen, Jack, Ten} + flush

   See also

   archive.ics.uci.edu/ml/datasets/Poker+Hand > run-main apps.analytics.PokerBayes

   archive.ics.uci.edu/ml/machine-learning-databases/poker/poker-hand.names

5.  object SolarRadiation extends App

   The SolarRadiation object is a sample application that performs basic analytics on solar radiation and meteorological data.

   The SolarRadiation object is a sample application that performs basic analytics on solar radiation and meteorological data. The data is provided by NSRDB. Hourly Global Horizontal Radiation data of 40 sites from the years 1961 to 1990 is considered. Operations are performed directly on compressed (RLE) vector and matrix. FIX: must download the following file.

   See also

   rredc.nrel.gov/solar/old_data/nsrdb/1961-1990/hourly/compressed/ > run-main apps.analytics.SolarRadiation

   rredc.nrel.gov/solar/old_data/nsrdb/

6.  object TempPerceptron extends App

   The TempPerceptron object trains a perceptron on a small dataset of temperatures from counties in Texas where the variables/factors to consider are Latitude (x1), Elevation (x2) and Longitude (x3).

   The TempPerceptron object trains a perceptron on a small dataset of temperatures from counties in Texas where the variables/factors to consider are Latitude (x1), Elevation (x2) and Longitude (x3). The regression equation is the following:

   y = sigmoid (w dot x) = sigmoid (w0 + w1*x1 + w2*x2 + w3*x3)

   > run-main apps.analytics.TempPerceptron

7.  object TempRegression extends App

   The TempRegression object performs regression on a small dataset of temperatures from counties in Texas where the variables/factors to consider are Latitude (x1), Elevation (x2) and Longitude (x3).

   The TempRegression object performs regression on a small dataset of temperatures from counties in Texas where the variables/factors to consider are Latitude (x1), Elevation (x2) and Longitude (x3). The regression equation is the following:

   y = b dot x = b0 + b1*x1 + b2*x2 + b3*x3

   A fit is created for both the full model and a reduce model created using backward elimination.

   See also

   www.stat.ufl.edu/~winner/cases/txtemp.ppt > run-main apps.analytics.TempRegression

8.  object TennisBayes extends App

   The TennisBayes object is a sample application that uses the NaiveBayes class.

   The TennisBayes object is a sample application that uses the NaiveBayes class. Classify (No/Yes) whether a person will play tennis based on the measured integer-valued features.

   See also

   suanpalm3.kmutnb.ac.th/teacher/FileDL/choochart82255418560.pdf > run-main apps.analytics.TennisBayes