How to interpret the output

Understanding GRSBA’s Index Matrix Output

Let’s consider an output similar to the Thermal Comfort example from the “How to Use GRSBA” page:

output = [
[4 2 0 5 1 3]
[5 4 1 2 3 0]
[4 1 2 3 0 5]
[5 1 2 3 4 0]
[4 1 2 3 5 0]
[5 1 2 3 4 0]
]

Analyzing the first line, [4 2 0 5 1 3], we see:

• 4: The first index indicates the fourth most probable value.
• 2: The second index indicates the second most probable value.
• 0: The third index indicates the most probable value.
• 5: The fourth index indicates the least probable value.
• 1: The fifth index indicates the second most probable value (again).
• 3: The sixth index indicates the third most probable value.

Each line in the parent array corresponds to a specific variable:

output = [
[4 2 0 5 1 3] - Index for temperature 
[5 4 1 2 3 0] - Index for humidity
[4 1 2 3 0 5] - Index for precipitation 
[5 1 2 3 4 0] - Index for solar wind
[4 1 2 3 5 0] - Index for solar radiation 
[5 1 2 3 4 0] - Index for pressure
]

How to Interpret the Results

Let’s say your input for temperature is:

temperature = np.array([0.2, 0.4, 0.6, 0.8, 0.7, 1.0])

And the corresponding output line (index for temperature) is:

[4 2 0 5 1 3]

This means that the most probable next temperature value is 0.6 (because the index ‘0’ is in the third position, indicating the most probable value).

Key Points

• GRSBA’s index matrix output ranks the probability of each value in your input array.
• The index position (0, 1, 2, etc.) indicates the rank, with 0 being the most probable.
• Each line in the output matrix corresponds to a different variable in your input.

Let me know if you have any other questions!