Updated plotly method and examples

vlang · Nov 5, 2023 · f709b7d · f709b7d
1 parent 5359bb0
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diff --git a/examples/deriv_example/main.v b/examples/deriv_example/main.v
@@ -9,21 +9,23 @@ fn pow(x f64, _ []f64) f64 {
 	return math.pow(x, 1.5)
 }
 
-f := func.Fn.new(f: pow)
-println('f(x) = x^(3/2)')
+fn main() {
+	f := func.Fn.new(f: pow)
+	println('f(x) = x^(3/2)')
 
-mut expected := 1.5 * math.sqrt(2.0)
-mut result, mut abserr := deriv.central(f, 2.0, 1e-8)
-println('x = 2.0')
-println("f'(x) = ${result} +/- ${abserr}")
-println('exact = ${expected}')
+	mut expected := 1.5 * math.sqrt(2.0)
+	mut result, mut abserr := deriv.central(f, 2.0, 1e-8)
+	println('x = 2.0')
+	println("f'(x) = ${result} +/- ${abserr}")
+	println('exact = ${expected}')
 
-assert float64.soclose(result, expected, abserr)
+	assert float64.soclose(result, expected, abserr)
 
-expected = 0.0
-result, abserr = deriv.forward(f, 0.0, 1e-8)
-println('x = 0.0')
-println("f'(x) = ${result} +/- ${abserr}")
-println('exact = ${expected}')
+	expected = 0.0
+	result, abserr = deriv.forward(f, 0.0, 1e-8)
+	println('x = 0.0')
+	println("f'(x) = ${result} +/- ${abserr}")
+	println('exact = ${expected}')
 
-assert float64.soclose(result, expected, abserr)
+	assert float64.soclose(result, expected, abserr)
+}
diff --git a/examples/diff_example/main.v b/examples/diff_example/main.v
@@ -9,21 +9,23 @@ fn pow(x f64, _ []f64) f64 {
 	return math.pow(x, 1.5)
 }
 
-f := func.Fn.new(f: pow)
-println('f(x) = x^(3/2)')
+fn main() {
+	f := func.Fn.new(f: pow)
+	println('f(x) = x^(3/2)')
 
-mut expected := 1.5 * math.sqrt(2.0)
-mut result, mut abserr := diff.central(f, 2.0)
-println('x = 2.0')
-println("f'(x) = ${result} +/- ${abserr}")
-println('exact = ${expected}')
+	mut expected := 1.5 * math.sqrt(2.0)
+	mut result, mut abserr := diff.central(f, 2.0)
+	println('x = 2.0')
+	println("f'(x) = ${result} +/- ${abserr}")
+	println('exact = ${expected}')
 
-assert float64.soclose(result, expected, abserr)
+	assert float64.soclose(result, expected, abserr)
 
-expected = 0.0
-result, abserr = diff.forward(f, 0.0)
-println('x = 0.0')
-println("f'(x) = ${result} +/- ${abserr}")
-println('exact = ${expected}')
+	expected = 0.0
+	result, abserr = diff.forward(f, 0.0)
+	println('x = 0.0')
+	println("f'(x) = ${result} +/- ${abserr}")
+	println('exact = ${expected}')
 
-assert float64.soclose(result, expected, abserr)
+	assert float64.soclose(result, expected, abserr)
+}
diff --git a/examples/dist_histogram/main.v b/examples/dist_histogram/main.v
@@ -2,43 +2,45 @@ module main
 
 import vsl.dist
 
-lims := [0.0, 1, 2, 3, 4, 5]
+fn main() {
+	lims := [0.0, 1, 2, 3, 4, 5]
 
-mut hist := dist.Histogram.new(lims)
+	mut hist := dist.Histogram.new(lims)
 
-values := [
-	0.0,
-	0.1,
-	0.2,
-	0.3,
-	0.9,
-	1,
-	1,
-	1,
-	1.2,
-	1.3,
-	1.4,
-	1.5,
-	1.99,
-	2,
-	2.5,
-	3,
-	3.5,
-	4.1,
-	4.5,
-	4.9,
-	-3,
-	-2,
-	-1,
-	5,
-	6,
-	7,
-	8,
-]
+	values := [
+		0.0,
+		0.1,
+		0.2,
+		0.3,
+		0.9,
+		1,
+		1,
+		1,
+		1.2,
+		1.3,
+		1.4,
+		1.5,
+		1.99,
+		2,
+		2.5,
+		3,
+		3.5,
+		4.1,
+		4.5,
+		4.9,
+		-3,
+		-2,
+		-1,
+		5,
+		6,
+		7,
+		8,
+	]
 
-hist.count(values, true)!
-labels := hist.gen_labels('%g')!
+	hist.count(values, true)!
+	labels := hist.gen_labels('%g')!
 
-hist_text := dist.text_hist(labels, hist.counts, 0)!
+	hist_text := dist.text_hist(labels, hist.counts, 0)!
 
-println(hist_text)
+	println(hist_text)
+}
diff --git a/examples/io_h5_dataset/main.v b/examples/io_h5_dataset/main.v
@@ -2,13 +2,15 @@ import vsl.inout.h5
 import math.stats
 import rand
 
-linedata := []f64{len: 21, init: (0 * index) + rand.f64()}
-mut meanv := 0.0
-hdffile := 'hdffile.h5'
+fn main() {
+	linedata := []f64{len: 21, init: (0 * index) + rand.f64()}
+	mut meanv := 0.0
+	hdffile := 'hdffile.h5'
 
-meanv = stats.mean(linedata)
+	meanv = stats.mean(linedata)
 
-f := h5.Hdf5File.new(hdffile)!
-f.write_dataset1d('/randdata', linedata)!
-f.write_attribute('/randdata', 'mean', meanv)!
-f.close()
+	f := h5.Hdf5File.new(hdffile)!
+	f.write_dataset1d('/randdata', linedata)!
+	f.write_attribute('/randdata', 'mean', meanv)!
+	f.close()
+}
diff --git a/examples/io_h5_relax/main.v b/examples/io_h5_relax/main.v
@@ -1,32 +1,34 @@
 import vsl.inout.h5
 import math
 
-// A simple 1d relaxation problem.  Write the results
-// and two attributes of the calculation to an HDF5 file.
-// To see the results, use `h5dump ex1_hdffile.h5`
-mut linedata := []f64{len: 21}
-mut newv := 0.0
-hdffile := 'ex1_hdffile.h5'
-mut rounds := i32(0)
+fn main() {
+	// A simple 1d relaxation problem.  Write the results
+	// and two attributes of the calculation to an HDF5 file.
+	// To see the results, use `h5dump ex1_hdffile.h5`
+	mut linedata := []f64{len: 21}
+	mut newv := 0.0
+	hdffile := 'ex1_hdffile.h5'
+	mut rounds := i32(0)
 
-linedata[0] = -2.0
-linedata[20] = 3.0
-mut maxdiff := 0.0
+	linedata[0] = -2.0
+	linedata[20] = 3.0
+	mut maxdiff := 0.0
 
-for loop in 0 .. 1000 {
-	maxdiff = -math.max_f64
-	for i in 1 .. linedata.len - 1 {
-		newv = (linedata[i - 1] + linedata[i] + linedata[i + 1]) / 3.00
-		maxdiff = math.max(maxdiff, math.abs(newv - linedata[i]))
-		linedata[i] = newv
-	}
-	rounds = loop
-	if maxdiff < 0.0001 {
-		break
+	for loop in 0 .. 1000 {
+		maxdiff = -math.max_f64
+		for i in 1 .. linedata.len - 1 {
+			newv = (linedata[i - 1] + linedata[i] + linedata[i + 1]) / 3.00
+			maxdiff = math.max(maxdiff, math.abs(newv - linedata[i]))
+			linedata[i] = newv
+		}
+		rounds = loop
+		if maxdiff < 0.0001 {
+			break
+		}
 	}
+	f := h5.Hdf5File.new(hdffile)!
+	f.write_dataset1d('linedata', linedata)!
+	f.write_attribute('linedata', 'rounds', rounds)!
+	f.write_attribute('linedata', 'maxdiff', maxdiff)!
+	f.close()
 }
-f := h5.Hdf5File.new(hdffile)!
-f.write_dataset1d('linedata', linedata)!
-f.write_attribute('linedata', 'rounds', rounds)!
-f.write_attribute('linedata', 'maxdiff', maxdiff)!
-f.close()
diff --git a/examples/iter_lazy_generation/main.v b/examples/iter_lazy_generation/main.v
@@ -2,9 +2,17 @@ module main
 
 import vsl.iter
 
-data := [1.0, 2.0, 3.0]
-r := 3
-mut combs := iter.CombinationsIter.new(data, r)
-for comb in combs {
-	print(comb)
+fn main() {
+	data := [1.0, 2.0, 3.0]
+	r := 3
+	mut count := 0
+
+	mut combs := iter.CombinationsIter.new(data, r)
+
+	for comb in combs {
+		print(comb)
+		count += 1
+	}
+
+	assert count == 3
 }
diff --git a/examples/la_triplet01/main.v b/examples/la_triplet01/main.v
@@ -2,22 +2,24 @@ module main
 
 import vsl.la
 
-mut a := la.Triplet.new[f64](4, 4, 6)
+fn main() {
+	mut a := la.Triplet.new[f64](4, 4, 6)
 
-a.put(1, 0, 1.0)!
-a.put(0, 1, 2.0)!
-a.put(3, 1, 3.0)!
-a.put(1, 2, 4.0)!
-a.put(2, 3, 5.0)!
-a.put(3, 3, 6.0)!
+	a.put(1, 0, 1.0)!
+	a.put(0, 1, 2.0)!
+	a.put(3, 1, 3.0)!
+	a.put(1, 2, 4.0)!
+	a.put(2, 3, 5.0)!
+	a.put(3, 3, 6.0)!
 
-mut expected_matrix := la.Matrix.deep2([
-	[0.0, 2, 0, 0],
-	[1.0, 0, 4, 0],
-	[0.0, 0, 0, 5],
-	[0.0, 3, 0, 6],
-])
+	mut expected_matrix := la.Matrix.deep2([
+		[0.0, 2, 0, 0],
+		[1.0, 0, 4, 0],
+		[0.0, 0, 0, 5],
+		[0.0, 3, 0, 6],
+	])
 
-m := a.to_dense()
+	m := a.to_dense()
 
-assert expected_matrix.equals(m)
+	assert expected_matrix.equals(m)
+}
diff --git a/examples/ml_kmeans/main.v b/examples/ml_kmeans/main.v
@@ -2,49 +2,51 @@ module main
 
 import vsl.ml
 
-// data
-mut data := ml.Data.from_raw_x([
-	[0.1, 0.7],
-	[0.3, 0.7],
-	[0.1, 0.9],
-	[0.3, 0.9],
-	[0.7, 0.1],
-	[0.9, 0.1],
-	[0.7, 0.3],
-	[0.9, 0.3],
-])!
+fn main() {
+	// data
+	mut data := ml.Data.from_raw_x([
+		[0.1, 0.7],
+		[0.3, 0.7],
+		[0.1, 0.9],
+		[0.3, 0.9],
+		[0.7, 0.1],
+		[0.9, 0.1],
+		[0.7, 0.3],
+		[0.9, 0.3],
+	])!
 
-// model
-nb_classes := 2
-mut model := ml.Kmeans.new(mut data, nb_classes, 'kmeans')
-model.set_centroids([
-	// class 0
-	[0.4, 0.6],
-	// class 1
-	[0.6, 0.4],
-])
+	// model
+	nb_classes := 2
+	mut model := ml.Kmeans.new(mut data, nb_classes, 'kmeans')
+	model.set_centroids([
+		// class 0
+		[0.4, 0.6],
+		// class 1
+		[0.6, 0.4],
+	])
 
-// initial classes
-model.find_closest_centroids()
+	// initial classes
+	model.find_closest_centroids()
 
-// initial computation of centroids
-model.compute_centroids()
+	// initial computation of centroids
+	model.compute_centroids()
 
-// train
-model.train(epochs: 6)
+	// train
+	model.train(epochs: 6)
 
-// test
-expected_classes := [
-	0,
-	0,
-	0,
-	0,
-	1,
-	1,
-	1,
-	1,
-]
-for i, c in model.classes {
-	assert c == expected_classes[i]
-	println('class ${i}: ${c}')
+	// test
+	expected_classes := [
+		0,
+		0,
+		0,
+		0,
+		1,
+		1,
+		1,
+		1,
+	]
+	for i, c in model.classes {
+		assert c == expected_classes[i]
+		println('class ${i}: ${c}')
+	}
 }