mirror of
https://github.com/Zenithsiz/ist-ddrs-lab2
synced 2026-02-03 22:23:55 +00:00
Filipe Rodrigues
483fb34788
Made all matrices have delimiters `[]`. Co-authored-by: Ricardo-Rodrigues <R1cardu7@users.noreply.github.com>
54 lines
1.7 KiB
Typst
54 lines
1.7 KiB
Typst
#import "/typst/util.typ" as util: indent_par, code_figure
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#indent_par[To estimate the transition probabilities, we traverse the data, two points at a time, as shown on figure 6. This allows us to check the transition of each data point and store it in an occurrences matrix, as we traverse.]
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#figure(
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```
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Data: 0 0 1 1 0 0 0 1 0
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Iteration 1: 0 0 Transition: 0 -> 0
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Iteration 2: 0 1 Transition: 0 -> 1
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Iteration 3: 1 1 Transition: 1 -> 1
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Iteration 4: 1 0 Transition: 1 -> 0
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Iteration 5: 0 0 Transition: 0 -> 0
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Iteration 6: 0 0 Transition: 0 -> 0
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Iteration 7: 0 1 Transition: 0 -> 1
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Iteration 8: 1 0 Transition: 1 -> 0
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...
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```,
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kind: image,
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caption: "Data traversal"
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)
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#indent_par[Afterwards, we can divide each row by the number of occurrences in that row to obtain the transition probability matrix. The following code 1 is the code we developed to accomplish this:]
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#code_figure(
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text(size: 0.8em, raw(read("/code/2.R"), lang: "R", block: true)),
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caption: "Developed code",
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)
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#indent_par[The following tables 1 and 2 contain our results:]
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#let occur_matrix = csv("/output/2-occur.csv", delimiter: "\t")
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#let prob_matrix = csv("/output/2-prob.csv", delimiter: "\t")
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#grid(
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columns: (1fr, 1fr),
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figure(
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pad(1em, text(size: 1.8em, math.mat(
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gap: 1em,
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..occur_matrix
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))),
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kind: table,
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caption: "Occurrences matrix"
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),
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figure(
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pad(1em, text(size: 1.8em, math.mat(
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gap: 1em,
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..prob_matrix
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))),
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kind: table,
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caption: "Transition probability matrix"
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)
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)
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#pagebreak()
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