(AI Studio Core)

Description

The Generate Data operator generates an ExampleSet with a specified number of numerical attributes which is controlled by the number of attributes parameter. Please note that in addition to the specified number of regular attributes, the label attribute is automatically generated by applying the function selected by the target function parameter. The selected target function is applied on the attributes to generate the label attribute. For example if the number of attributes parameter is set to 3 and the target function is set to 'sum'. Then three regular numerical attributes will be created. In addition to these regular attributes a label attribute will be generated automatically. As the target function is set to 'sum', the label attribute value will be the sum of all three regular attribute values.

The label target functions are calculated as follows (assuming n generated attributes):

• random: The label is randomly generated.
• sum (needs at least 3 attributes): The label is the sum of the arguments: att1 + att2 + ... + att[n]
• polynomial (needs at least 3 attributes): att1^3 + att2^2 + att3
• non linear (needs at least 3 attributes): att1 * att2 * att3 + att1 * att2 + att2 * att2
• one variable non linear (needs 1 attribute): 3 * att1^3 - att1^2 + 1000 / |att1| + 2000 * |att1|
• complicated function (needs at least 3 attributes): att1 * att1 * att2 + att2 * att3 + max(att1,att2) - e^att3
• complicated function2 (needs at least 3 attributes): att1 * att1 * att1 + att2 * att2 + att1 * att2 + att1 / |att3| - 1 / (att3 * att3)
• simple sinus (needs 1 attribute): sin(att1)
• sinus (needs 2 attributes): sin(att1 * att2) + sin(att1 + att2)
• simple superposition (needs 1 attribute): 5 * sin(att1) + sin(30 * att1)
• sinus frequency (needs at least 2 attributes): 10 * sin(3 * att1) + 12 * sin(7 * att1) + 11 * sin( 5 * att2) + 9 * sin(10 * att2) + 10 * sin(8 * (att1 + att2))
• sinus with trend (needs 1 attribute): sin(att1) + 0.1 * att1
• sinc: sin(x) / ||x||, if ||x|| is not 0, else 0.
• triangular function (needs 1 attribute): The label is the fractional part of the argument.
• square pulse function (needs 1 attribute): The label is a square pulse in the attribute.
• random classification: The label is randomly "negative" or "positive".
• one third classification: The label is "positive" if att1 < 0.3333 and "negative" else.
• sum classification: The label is "positive" if the sum of all arguments is positive, else "negative".
• quadratic classification (needs at least 2 attributes): The label is "positive" if att2 > att1^2, else "negative".
• simple non linear classification (needs at least 2 attributes): The label is "positive" if 50 < att1*att2 < 80, else "negative".
• interaction classification (needs at least 3 attributes): The label is "positive" if att1 < 0 or att2 > 0 and att3 < 0, else "negative".
• simple polynomial classification (needs at least 1 attribute): The label is "positive" if att1^4 > 100, else "negative".
• polynomial classification (needs at least 4 attributes): The label is "positive" if att1^3 + att2^2 - att3^2 + att4 > 0, else "negative".
• checkerboard classification (needs 2 attributes): The label is "positive" or "negative", according to a checkerboard pattern, where the size of each tile is 5.
• random dots classification (needs 2 attributes): Some randomly sized and placed positive and negative dots are generated on the 2D field. The label is "positive" if the example is only contained by positive dots, else "negative".
• global and local models classification (needs 2 attributes): The label is "positive" if the sum of both arguments is positive, else "negative". In addition, several local patterns in different sizes are placed in the data space.
• sinus classification (needs at least 2 attributes): The label is "positive" if sin(att1*att2) + sin(att1+att2) > 0, else "negative".
• multi classification: The label is "one" if the sum of all arguments modulo 2 is 0, "two" if the sum modulo 3 is 0, "three" if the sum modulo 5 is 0, else "four".
• two gaussians classification: Generates two Gaussian clusters. The label is either "cluster0" or "cluster1".
• transactions dataset (needs at least 5 attributes): Generates an association function transaction dataset, all attribute values are 0 or 1. The first four attributes are correlated. No label is generated.
• grid function: Generates a uniformly distributed grid in the given dimensions. A label with zero value is generated.
• three ring clusters (needs 2 attributes): Generates three concentric ring clusters. The label values are "core", "first_ring" and "second_ring", accordingly.
• spiral cluster (needs 2 attributes): Generates two interlocking spiral clusters. The label values are "spiral1" and "spiral2", accordingly.
• single gaussian cluster: Generates a Gaussian cluster. A label with zero value is generated.
• gaussian mixture clusters: Generates a mixture of Gaussian clusters. Each attribute doubles the cluster amount, so 2^n clusters are generated. A label with the cluster id is generated.
• driller oscillation timeseries (needs at least 2 attributes): Generates an artificial audio data set (based on real-world data from drilling processes). No label is generated.