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| <span id="projectnumber">1.11</span> |
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| <div id="projectbrief">User Documentation for MADlib</div> |
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| <div class="textblock">Here is a list of all modules:</div><div class="directory"> |
| <div class="levels">[detail level <span onclick="javascript:toggleLevel(1);">1</span><span onclick="javascript:toggleLevel(2);">2</span><span onclick="javascript:toggleLevel(3);">3</span><span onclick="javascript:toggleLevel(4);">4</span>]</div><table class="directory"> |
| <tr id="row_0_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_0_" class="arrow" onclick="toggleFolder('0_')">▼</span><a class="el" href="group__grp__datatrans.html" target="_self">Data Types and Transformations</a></td><td class="desc"></td></tr> |
| <tr id="row_0_0_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_0_0_" class="arrow" onclick="toggleFolder('0_0_')">▼</span><a class="el" href="group__grp__arraysmatrix.html" target="_self">Arrays and Matrices</a></td><td class="desc">Mathematical operations for arrays and matrices </td></tr> |
| <tr id="row_0_0_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__array.html" target="_self">Array Operations</a></td><td class="desc">Provides fast array operations supporting other MADlib modules </td></tr> |
| <tr id="row_0_0_1_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__matrix.html" target="_self">Matrix Operations</a></td><td class="desc">Provides fast matrix operations supporting other MADlib modules </td></tr> |
| <tr id="row_0_0_2_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_0_0_2_" class="arrow" onclick="toggleFolder('0_0_2_')">▼</span><a class="el" href="group__grp__matrix__factorization.html" target="_self">Matrix Factorization</a></td><td class="desc">Matrix Factorization methods including Singular Value Decomposition and Low-rank Matrix Factorization </td></tr> |
| <tr id="row_0_0_2_0_"><td class="entry"><span style="width:64px;display:inline-block;"> </span><a class="el" href="group__grp__lmf.html" target="_self">Low-rank Matrix Factorization</a></td><td class="desc">Performs low-rank matrix factorization for an incomplete matrix </td></tr> |
| <tr id="row_0_0_2_1_" class="even"><td class="entry"><span style="width:64px;display:inline-block;"> </span><a class="el" href="group__grp__svd.html" target="_self">Singular Value Decomposition</a></td><td class="desc">Performs factorization of dense and sparse matrices </td></tr> |
| <tr id="row_0_0_3_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__linalg.html" target="_self">Norms and Distance functions</a></td><td class="desc">Provides utility functions for basic linear algebra operations </td></tr> |
| <tr id="row_0_0_4_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__svec.html" target="_self">Sparse Vectors</a></td><td class="desc">Implements a sparse vector data type that provides compressed storage of vectors that may have many duplicate elements </td></tr> |
| <tr id="row_0_1_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_0_1_" class="arrow" onclick="toggleFolder('0_1_')">▼</span><a class="el" href="group__grp__pca.html" target="_self">Dimensionality Reduction</a></td><td class="desc">A collection of methods for dimensionality reduction </td></tr> |
| <tr id="row_0_1_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__pca__train.html" target="_self">Principal Component Analysis</a></td><td class="desc">Produces a model that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components </td></tr> |
| <tr id="row_0_1_1_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__pca__project.html" target="_self">Principal Component Projection</a></td><td class="desc">Projects a higher dimensional data point to a lower dimensional subspace spanned by principal components learned through the PCA training procedure </td></tr> |
| <tr id="row_0_2_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__encode__categorical.html" target="_self">Encoding Categorical Variables</a></td><td class="desc">Provides functions to encode categorical variables </td></tr> |
| <tr id="row_0_3_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__pivot.html" target="_self">Pivot</a></td><td class="desc">Provides pivoting functions helpful for data preparation before modeling </td></tr> |
| <tr id="row_0_4_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__stemmer.html" target="_self">Stemming</a></td><td class="desc">Provides porter stemmer operations supporting other MADlib modules </td></tr> |
| <tr id="row_1_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_1_" class="arrow" onclick="toggleFolder('1_')">▼</span><a class="el" href="group__grp__graph.html" target="_self">Graph</a></td><td class="desc"></td></tr> |
| <tr id="row_1_0_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__pagerank.html" target="_self">PageRank</a></td><td class="desc">Find the PageRank of all vertices in a directed graph </td></tr> |
| <tr id="row_1_1_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__sssp.html" target="_self">Single Source Shortest Path</a></td><td class="desc">Finds the shortest path from a single source vertex to every other vertex in a given graph </td></tr> |
| <tr id="row_2_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_2_" class="arrow" onclick="toggleFolder('2_')">▼</span><a class="el" href="group__grp__mdl.html" target="_self">Model Evaluation</a></td><td class="desc"></td></tr> |
| <tr id="row_2_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__validation.html" target="_self">Cross Validation</a></td><td class="desc">Estimates the fit of a predictive model given a data set and specifications for the training, prediction, and error estimation functions </td></tr> |
| <tr id="row_2_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__pred.html" target="_self">Prediction Metrics</a></td><td class="desc">Provides various prediction accuracy metrics </td></tr> |
| <tr id="row_3_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_3_" class="arrow" onclick="toggleFolder('3_')">▼</span><a class="el" href="group__grp__stats.html" target="_self">Statistics</a></td><td class="desc"></td></tr> |
| <tr id="row_3_0_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_3_0_" class="arrow" onclick="toggleFolder('3_0_')">▼</span><a class="el" href="group__grp__desc__stats.html" target="_self">Descriptive Statistics</a></td><td class="desc"></td></tr> |
| <tr id="row_3_0_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__correlation.html" target="_self">Pearson's Correlation</a></td><td class="desc">Generates a cross-correlation matrix for all pairs of numeric columns in a table </td></tr> |
| <tr id="row_3_0_1_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__summary.html" target="_self">Summary</a></td><td class="desc">Calculates general descriptive statistics for any data table </td></tr> |
| <tr id="row_3_1_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_3_1_" class="arrow" onclick="toggleFolder('3_1_')">▼</span><a class="el" href="group__grp__inf__stats.html" target="_self">Inferential Statistics</a></td><td class="desc"></td></tr> |
| <tr id="row_3_1_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__stats__tests.html" target="_self">Hypothesis Tests</a></td><td class="desc">Provides functions to perform statistical hypothesis tests </td></tr> |
| <tr id="row_3_2_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__prob.html" target="_self">Probability Functions</a></td><td class="desc">Provides cumulative distribution, density/mass, and quantile functions for a wide range of probability distributions </td></tr> |
| <tr id="row_4_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_4_" class="arrow" onclick="toggleFolder('4_')">▼</span><a class="el" href="group__grp__super.html" target="_self">Supervised Learning</a></td><td class="desc"></td></tr> |
| <tr id="row_4_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__crf.html" target="_self">Conditional Random Field</a></td><td class="desc">Constructs a Conditional Random Fields (CRF) model for labeling sequential data </td></tr> |
| <tr id="row_4_1_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_4_1_" class="arrow" onclick="toggleFolder('4_1_')">▼</span><a class="el" href="group__grp__regml.html" target="_self">Regression Models</a></td><td class="desc"></td></tr> |
| <tr id="row_4_1_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__clustered__errors.html" target="_self">Clustered Variance</a></td><td class="desc">Calculates clustered variance for linear, logistic, and multinomial logistic regression models, and Cox proportional hazards models </td></tr> |
| <tr id="row_4_1_1_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__cox__prop__hazards.html" target="_self">Cox-Proportional Hazards Regression</a></td><td class="desc">Models the relationship between one or more independent predictor variables and the amount of time before an event occurs </td></tr> |
| <tr id="row_4_1_2_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__elasticnet.html" target="_self">Elastic Net Regularization</a></td><td class="desc">Generates a regularized regression model for variable selection in linear and logistic regression problems, combining the L1 and L2 penalties of the lasso and ridge methods </td></tr> |
| <tr id="row_4_1_3_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__glm.html" target="_self">Generalized Linear Models</a></td><td class="desc">Estimate generalized linear model (GLM). GLM is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value </td></tr> |
| <tr id="row_4_1_4_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__linreg.html" target="_self">Linear Regression</a></td><td class="desc">Also called Ordinary Least Squares Regression, models linear relationship between a dependent variable and one or more independent variables </td></tr> |
| <tr id="row_4_1_5_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__logreg.html" target="_self">Logistic Regression</a></td><td class="desc">Models the relationship between one or more predictor variables and a binary categorical dependent variable by predicting the probability of the dependent variable using a logistic function </td></tr> |
| <tr id="row_4_1_6_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__marginal.html" target="_self">Marginal Effects</a></td><td class="desc">Calculates marginal effects for the coefficients in regression problems </td></tr> |
| <tr id="row_4_1_7_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__multinom.html" target="_self">Multinomial Regression</a></td><td class="desc">Multinomial regression is to model the conditional distribution of the multinomial response variable using a linear combination of predictors </td></tr> |
| <tr id="row_4_1_8_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__ordinal.html" target="_self">Ordinal Regression</a></td><td class="desc">Regression to model data with ordinal response variable </td></tr> |
| <tr id="row_4_1_9_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__robust.html" target="_self">Robust Variance</a></td><td class="desc">Calculates Huber-White variance estimates for linear, logistic, and multinomial regression models, and for Cox proportional hazards models </td></tr> |
| <tr id="row_4_2_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__svm.html" target="_self">Support Vector Machines</a></td><td class="desc">Solves classification and regression problems by separating data with a hyperplane or other nonlinear decision boundary </td></tr> |
| <tr id="row_4_3_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_4_3_" class="arrow" onclick="toggleFolder('4_3_')">▼</span><a class="el" href="group__grp__tree.html" target="_self">Tree Methods</a></td><td class="desc"></td></tr> |
| <tr id="row_4_3_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__decision__tree.html" target="_self">Decision Tree</a></td><td class="desc">Decision Trees. Decision trees use a tree-based model to predict the value of a target variable based on several input variables </td></tr> |
| <tr id="row_4_3_1_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__random__forest.html" target="_self">Random Forest</a></td><td class="desc">Random forests are an ensemble learning method for classification and regression that construct a multitude of decision trees at training time, then produce the class that is the mode of the classes of the individual trees </td></tr> |
| <tr id="row_5_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_5_" class="arrow" onclick="toggleFolder('5_')">▼</span><a class="el" href="group__grp__tsa.html" target="_self">Time Series Analysis</a></td><td class="desc"></td></tr> |
| <tr id="row_5_0_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__arima.html" target="_self">ARIMA</a></td><td class="desc">Generates a model with autoregressive, moving average, and integrated components for a time series dataset </td></tr> |
| <tr id="row_6_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_6_" class="arrow" onclick="toggleFolder('6_')">▼</span><a class="el" href="group__grp__unsupervised.html" target="_self">Unsupervised Learning</a></td><td class="desc"></td></tr> |
| <tr id="row_6_0_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_6_0_" class="arrow" onclick="toggleFolder('6_0_')">▼</span><a class="el" href="group__grp__association__rules.html" target="_self">Association Rules</a></td><td class="desc"></td></tr> |
| <tr id="row_6_0_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__assoc__rules.html" target="_self">Apriori Algorithm</a></td><td class="desc">Computes association rules for a given set of data </td></tr> |
| <tr id="row_6_1_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_6_1_" class="arrow" onclick="toggleFolder('6_1_')">▼</span><a class="el" href="group__grp__clustering.html" target="_self">Clustering</a></td><td class="desc"></td></tr> |
| <tr id="row_6_1_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__kmeans.html" target="_self">k-Means Clustering</a></td><td class="desc">Partitions a set of observations into clusters by finding centroids that minimize the sum of observations' distances from their closest centroid </td></tr> |
| <tr id="row_6_2_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_6_2_" class="arrow" onclick="toggleFolder('6_2_')">▼</span><a class="el" href="group__grp__topic__modelling.html" target="_self">Topic Modelling</a></td><td class="desc"></td></tr> |
| <tr id="row_6_2_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__lda.html" target="_self">Latent Dirichlet Allocation</a></td><td class="desc">Generates a Latent Dirichlet Allocation predictive model for a collection of documents </td></tr> |
| <tr id="row_7_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_7_" class="arrow" onclick="toggleFolder('7_')">▼</span><a class="el" href="group__grp__utility__functions.html" target="_self">Utility Functions</a></td><td class="desc"></td></tr> |
| <tr id="row_7_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__utilities.html" target="_self">Developer Database Functions</a></td><td class="desc">Provides a collection of user-defined functions for performing common tasks in the database </td></tr> |
| <tr id="row_7_1_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_7_1_" class="arrow" onclick="toggleFolder('7_1_')">▼</span><a class="el" href="group__grp__linear__solver.html" target="_self">Linear Solvers</a></td><td class="desc"></td></tr> |
| <tr id="row_7_1_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__dense__linear__solver.html" target="_self">Dense Linear Systems</a></td><td class="desc">Implements solution methods for large dense linear systems. Currently, restricted to problems that fit in memory </td></tr> |
| <tr id="row_7_1_1_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__sparse__linear__solver.html" target="_self">Sparse Linear Systems</a></td><td class="desc">Implements solution methods for linear systems with sparse matrix input. Currently, restricted to problems that fit in memory </td></tr> |
| <tr id="row_7_2_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__path.html" target="_self">Path</a></td><td class="desc">Path Functions </td></tr> |
| <tr id="row_7_3_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__pmml.html" target="_self">PMML Export</a></td><td class="desc">Implements the PMML XML standard to describe and exchange models produced by data mining and machine learning algorithms </td></tr> |
| <tr id="row_7_4_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__sessionize.html" target="_self">Sessionize</a></td><td class="desc">Sessionize </td></tr> |
| <tr id="row_7_5_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_7_5_" class="arrow" onclick="toggleFolder('7_5_')">▼</span><a class="el" href="group__grp__text__analysis.html" target="_self">Text Analysis</a></td><td class="desc"></td></tr> |
| <tr id="row_7_5_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__text__utilities.html" target="_self">Term Frequency</a></td><td class="desc">Provides a collection of user-defined functions for performing common tasks related to text </td></tr> |
| <tr id="row_8_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_8_" class="arrow" onclick="toggleFolder('8_')">▼</span><a class="el" href="group__grp__early__stage.html" target="_self">Early Stage Development</a></td><td class="desc">A collection of implementations which are in early stage of development. There may be some issues that will be addressed in a future version. Interface and implementation are subject to change </td></tr> |
| <tr id="row_8_0_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_8_0_" class="arrow" onclick="toggleFolder('8_0_')">▼</span><a class="el" href="group__grp__sketches.html" target="_self">Cardinality Estimators</a></td><td class="desc">A collection of methods to estimate the number of unique values contained in the data </td></tr> |
| <tr id="row_8_0_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__countmin.html" target="_self">CountMin (Cormode-Muthukrishnan)</a></td><td class="desc">Implements Cormode-Mathukrishnan <em>CountMin</em> sketches on integer values as a user-defined aggregate </td></tr> |
| <tr id="row_8_0_1_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__fmsketch.html" target="_self">FM (Flajolet-Martin)</a></td><td class="desc">Implements Flajolet-Martin's distinct count estimation as a user-defined aggregate </td></tr> |
| <tr id="row_8_0_2_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__mfvsketch.html" target="_self">MFV (Most Frequent Values)</a></td><td class="desc">Implements the most frequent values variant of the CountMin sketch as a user-defined aggregate </td></tr> |
| <tr id="row_8_1_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__cg.html" target="_self">Conjugate Gradient</a></td><td class="desc">Finds the solution to the function <img class="formulaInl" alt="$ \boldsymbol Ax = \boldsymbol b $" src="form_41.png"/>, where <img class="formulaInl" alt="$A$" src="form_42.png"/> is a symmetric, positive-definite matrix and <img class="formulaInl" alt="$x$" src="form_43.png"/> and <img class="formulaInl" alt="$ \boldsymbol b $" src="form_44.png"/> are vectors </td></tr> |
| <tr id="row_8_2_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__bayes.html" target="_self">Naive Bayes Classification</a></td><td class="desc">Constructs a classification model from a dataset where each attribute independently contributes to the probability that a data point belongs to a category </td></tr> |
| <tr id="row_8_3_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__sample.html" target="_self">Random Sampling</a></td><td class="desc">Provides utility functions for sampling operations </td></tr> |
| <tr id="row_8_4_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_8_4_" class="arrow" onclick="toggleFolder('8_4_')">▼</span><a class="el" href="group__grp__nene.html" target="_self">Nearest Neighbors</a></td><td class="desc"></td></tr> |
| <tr id="row_8_4_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__knn.html" target="_self">k-Nearest Neighbors</a></td><td class="desc">Finds k nearest data points to the given data point and outputs majority vote value of output classes for classification, and average value of target values for regression </td></tr> |
| <tr id="row_9_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_9_" class="arrow" onclick="toggleFolder('9_')">▼</span><a class="el" href="group__grp__deprecated.html" target="_self">Deprecated Modules</a></td><td class="desc">A collection of deprecated modules. These functions will be removed in the next major version (2.0) </td></tr> |
| <tr id="row_9_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__indicator.html" target="_self">Create Indicator Variables</a></td><td class="desc">Provides utility functions helpful for data preparation before modeling </td></tr> |
| <tr id="row_9_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__mlogreg.html" target="_self">Multinomial Logistic Regression</a></td><td class="desc">Also called as softmax regression, models the relationship between one or more independent variables and a categorical dependent variable </td></tr> |
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