iteraa.plot

Functions

`plotRadarDatapoints`(AA, X[, sampIDs, archSpaceIDs, ...])
`createSimplexAx`(AA[, archIDs, gridOn, showLabel, ...])	# groupColor = None, color = None, marker = None, size = None
`mapAlfaToSimplex`(alfa, AA)	alfa: 2D-array (nArchetypes x nData)
`plotTSNE`(X[, figNamePrefix, figSize, numComponents, ...])	Conduct t-stochastic neighbour embedding and visualise the results.

Module Contents

iteraa.plot.plotRadarDatapoints(AA, X, sampIDs=[0], archSpaceIDs=[0, 1], sepSamps=False, showLabel=True, labelAll=False, showLegend=False, figSize=(6, 6), dpi=DPI, title=None, figNamePrefix='')[source]

iteraa.plot.createSimplexAx(AA, archIDs=[0, 1, 2], gridOn=True, showLabel=True, labelAll=False, figSize=(3, 3), gridLineWidth=0.5, gridcolor='k', bordercolor='k', fontcolor='k')[source]: # groupColor = None, color = None, marker = None, size = None groupColor:

Dimension: nData x 1

Description: Contains the category of data point.

iteraa.plot.mapAlfaToSimplex(alfa, AA)[source]: alfa: 2D-array (nArchetypes x nData)

iteraa.plot.plotTSNE(X, figNamePrefix='', figSize=(3, 3), numComponents=2, markIdxs=[], markerSize=1, colourInstances=False, perplexity=30.0, earlyExaggeration=12.0, learningRate='auto', nIter=1000, angle=0.5, metric='euclidean', init='pca', method='barnes_hut', minGradNorm=1e-07, nIterWithoutProgress=300, nJobs=NUM_JOBS, randomState=RANDOM_STATE)[source]

Conduct t-stochastic neighbour embedding and visualise the results.

Parameters:: X (numpy.ndarray) – Whole data set.
Return type:: None