Build Effective Algorithms with MapReduce
files that ship with MATLAB® illustrate different programming
techniques. You can use these examples as a starting point to quickly
The associated files for these examples are all in the
|Example Link||Primary File||Description||Notable Programming Techniques|
|Find Maximum Value with MapReduce||Find maximum arrival delay|
One intermediate key and minimal computation.
|Compute Mean Value with MapReduce||Find mean arrival delay|
One intermediate key with intermediate state (accumulating intermediate sum and count).
|Create Histograms Using MapReduce||Visualize data using histograms|
Low-volume summaries of data, sufficient to generate a graphic and gain preliminary insights.
|Compute Mean by Group Using MapReduce||Compute mean arrival delay for each day of the week|
Perform simple computations on subgroups of input data using several intermediate keys.
|Compute Maximum Average HSV of Images with MapReduce||Determine average maximum hue, saturation, and brightness in an image collection|
Analyzes an image datastore using three intermediate keys. The outputs are filenames, which can be used to view the images.
|Simple Data Subsetting Using MapReduce||Create single table from subset of large data set|
Extraction of subset of large data set to look for patterns. The procedure is generalized using a parameterized map function to pass in the subsetting criteria.
|Using MapReduce to Compute Covariance and Related Quantities||Compute covariance and related quantities|
Calculate several intermediate values and store them with the same key. Use covariance to obtain a correlation matrix and regression coefficients, and to perform principal components analysis.
|Compute Summary Statistics by Group Using MapReduce||Compute summary statistics organized by group|
Use an anonymous function to pass an extra grouping parameter to a parameterized map function. This parameterization allows you to quickly recalculate statistics using different grouping variables.
|Using MapReduce to Fit a Logistic Regression Model||Fit simple logistic regression model|
|Tall Skinny QR (TSQR) Matrix Factorization Using MapReduce||Tall skinny QR decomposition|