oxits 0.1.0

Time series classification and transformation library for Rust
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50

"""Generate golden test data for multivariate module."""

import numpy as np
from pyts.multivariate.image import JointRecurrencePlot
from pyts.multivariate.transformation import MultivariateTransformer
from pyts.image import RecurrencePlot


def generate():
    fixtures = []
    rng = np.random.RandomState(42)

    # Multivariate data: (n_samples, n_features, n_timestamps)
    X = rng.randn(3, 2, 10)

    # JointRecurrencePlot (default)
    jrp = JointRecurrencePlot()
    result = jrp.transform(X)
    fixtures.append({
        "test_name": "jrp_basic",
        "params": {},
        "input": {"X": X.tolist()},
        "expected": {"output_3d": result.tolist()},
        "tolerance": 1e-10,
    })

    # JointRecurrencePlot with threshold
    jrp = JointRecurrencePlot(threshold=1.0)
    result = jrp.transform(X)
    fixtures.append({
        "test_name": "jrp_threshold",
        "params": {"threshold": 1.0},
        "input": {"X": X.tolist()},
        "expected": {"output_3d": result.tolist()},
        "tolerance": 1e-10,
    })

    # MultivariateTransformer with RecurrencePlot
    mt = MultivariateTransformer(RecurrencePlot(), flatten=False)
    mt.fit(X)
    result = mt.transform(X)
    fixtures.append({
        "test_name": "multivariate_rp",
        "params": {"estimator": "RecurrencePlot"},
        "input": {"X": X.tolist()},
        "expected": {"output_4d": result.tolist()},
        "tolerance": 1e-10,
    })

    return fixtures