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[jasmine-performance] merge fix of optimization mistake in jasmine #342

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Merged
biblicabeebli merged 1 commit into from
Oct 1, 2026
Merged

[jasmine-performance] merge fix of optimization mistake in jasmine #342

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1 change: 1 addition & 0 deletions src/forest/constants.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,6 +61,7 @@ class Frequency(Enum):
FP_TOLERANCE = 1e-6 # a minimum threshold (Jasmine)

SECONDS_IN_DAY = 60 * 60 * 24
SECONDS_IN_WEEK = 60 * 60 * 24 * 7

SECONDS_PER_DAY_TIMES_PI = 86_400 * math.pi ## src: SOGP
SECONDS_PER_WEEK_TIMES_PI = 604_800 * math.pi
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11 changes: 7 additions & 4 deletions src/forest/jasmine/sogp_gps.py
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Expand Up @@ -17,12 +17,15 @@
import numba
import numpy as np

from forest.constants import K0_PARAMS, SECONDS_PER_DAY_TIMES_PI, SECONDS_PER_WEEK_TIMES_PI
from forest.constants import K0_PARAMS, SECONDS_IN_DAY, SECONDS_IN_WEEK


logger = logging.getLogger(__name__)


PI = np.pi


@numba.njit(cache=True, fastmath=True)
def calculate_k0(x1: np.ndarray, x2: np.ndarray, k0_params: K0_PARAMS) -> float:
""" This function calculates the similarity between two points
Expand All @@ -38,10 +41,10 @@ def calculate_k0(x1: np.ndarray, x2: np.ndarray, k0_params: K0_PARAMS) -> float:
float, the similarity between x1 and x2
"""
l1, l2, l3, a1, a2, b1, b2, b3 = k0_params

dt = abs(x1[0] - x2[0])
sin_daily = np.sin(dt / SECONDS_PER_DAY_TIMES_PI) ** 2
sin_weekly = np.sin(dt / SECONDS_PER_WEEK_TIMES_PI) ** 2
sin_daily = np.sin(dt / SECONDS_IN_DAY * PI) ** 2
sin_weekly = np.sin(dt / SECONDS_IN_WEEK * PI) ** 2
k1 = np.exp(-dt / l1 - sin_daily / a1)
k2 = np.exp(-dt / l2 - sin_weekly / a2)
k3 = np.exp(-abs(x1[1] - x2[1]) / l3)
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