Version 13 of JMP introduces a new function: Get Excel Worksheets.
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Tag Archives: JSL
Pick Directory
This is one of a series of posts highlighting new features available in version 13 of JMP.
Pick Directory is a JSL function that is used to allow the user to select a directory on the computer. Sometimes you don’t know where to look for data, or where to save data, so you ask the user using this function.
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Calendar Box
Version 13 of JMP introduces a new display box: Calendar Box.
Now if you are creating a form to gather input from a user you can use this control to allow the user to easily specify a date or datetime.
Performance Trap
I was recently processing a number of files using pattern matching. During the processing I was storing information in lists which were subsequently used to populate new JMP data tables.
Everything worked fine until I increased the number of files by a factor of 10.
After some time I started hitting ‘escape’ and ‘CTRL-Z’ in a frenetic effort to seize control of my laptop.
Plotting Functions
Validating Modal Windows
A modal window forces a user to respond to a prompt before continuing execution of a script. The JMP user interface rarely uses modal windows and as programmers we should respect this principle and use modal windows sparingly. If a task is important enough to warrant a modal window it’s probably important enough to demand some level of validation of user inputs. Here’s how: (more…)
More Pattern Matching
In my last post I introduced the principles of using the pattern matching functions within JMP. Once you start using pattern matching you will discover that you need to use some additional features, which I discuss here. (more…)
Basic Pattern Matching
Pattern matching is an incredibly powerful technique for interrogating text strings for the purpose of matching and manipulating string patterns. In this post I will illustrate some of the basic principles of pattern matching. This will be followed by more advanced scenarios. (more…)
Visualising Machine Learning Pt. 2
Where is the “learning” in machine learning? The problem with machine learning algorithms is that they yield a final solution without giving you a sense of the learning process. So I’ve implemented an interactive version of a perceptron learning algorithm. To run the algorithm I need lenearly separable data – hence the posts from the previous weeks. Now I can use that as the basis for visualising the machine learning steps. (more…)
Visualising Machine Learning pt.1
My last two posts have been talking creating linearly separable data. Hopefully you found some of the features interesting – for example, the idea of creating interactivity using grab handles. But I never really gave a purpose to what I was doing: I wanted to write some machine learning code – specifically a perceptron learning algorithm – and to test the code I needed to create some data on which it could act. (more…)