Moved Pearson correlation coefficient to combine.py

1 files changed, 0 insertions, 80 deletions

diff --git a/pyecsca/sca/stacked_traces/correlate.py b/pyecsca/sca/stacked_traces/correlate.py
deleted file mode 100644
index c5277d9..0000000
--- a/pyecsca/sca/stacked_traces/correlate.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import numpy as np
-import numpy.typing as npt
-from numba import cuda
-from numba.cuda.cudadrv.devicearray import DeviceNDArray
-from math import sqrt
-from typing import List, Optional, Union
-from .combine import InputType, GPUTraceManager
-from .stacked_traces import StackedTraces
-from ..trace.trace import CombinedTrace
-
-
-def gpu_pearson_corr(intermediate_values: npt.NDArray[np.number],
-                     stacked_traces: Optional[StackedTraces] = None,
-                     trace_manager: Optional[GPUTraceManager] = None,
-                     **tm_kwargs) -> Union[CombinedTrace, List[CombinedTrace]]:
-    if (stacked_traces is None) == (trace_manager is None):
-        raise ValueError("Either samples or trace manager must be given.")
-
-    if trace_manager is None:
-        assert stacked_traces is not None
-        trace_manager = GPUTraceManager(stacked_traces, **tm_kwargs)
-
-    if (len(intermediate_values.shape) != 1
-        or (intermediate_values.shape[0]
-            != trace_manager.traces_shape[0])):
-        raise ValueError("Intermediate values have to be a vector "
-                         "as long as trace_count")
-
-    intermed_sum: np.number = np.sum(intermediate_values)
-    intermed_sq_sum: np.number = np.sum(np.square(intermediate_values))
-    inputs: List[InputType] = [intermediate_values,
-                               np.array([intermed_sum]),
-                               np.array([intermed_sq_sum])]
-
-    return trace_manager.run(
-        _gpu_pearson_corr,
-        inputs
-    )
-
-
-@cuda.jit(cache=True)
-def _gpu_pearson_corr(samples: DeviceNDArray,
-                      intermediate_values: DeviceNDArray,
-                      intermed_sum: DeviceNDArray,
-                      intermed_sq_sum: DeviceNDArray,
-                      result: DeviceNDArray):
-    """
-    Calculates the Pearson correlation coefficient between the given samples and intermediate values using GPU acceleration.
-
-    :param samples: A 2D array of shape (n, m) containing the samples.
-    :type samples: npt.NDArray[np.number]
-    :param intermediate_values: A 1D array of shape (n,) containing the intermediate values.
-    :type intermediate_values: npt.NDArray[np.number]
-    :param intermed_sum: A 1D array of shape (1,) containing the precomputed sum of the intermediate values.
-    :type intermed_sum: npt.NDArray[np.number]
-    :param intermed_sq_sum: A 1D array of shape (1,) containing the precomputed sum of the squares of the intermediate values.
-    :param result: A 1D array of shape (m,) to store the resulting correlation coefficients.
-    :type result: cuda.devicearray.DeviceNDArray
-    """
-    col: int = cuda.grid(1)  # type: ignore
-
-    if col >= samples.shape[1]:  # type: ignore
-        return
-
-    n = samples.shape[0]
-    samples_sum = 0.
-    samples_sq_sum = 0.
-    product_sum = 0.
-
-    for row in range(n):
-        samples_sum += samples[row, col]
-        samples_sq_sum += samples[row, col] ** 2
-        product_sum += samples[row, col] * intermediate_values[row]
-
-    numerator = float(n) * product_sum - samples_sum * intermed_sum[0]
-    denom_samp = sqrt(float(n) * samples_sq_sum - samples_sum ** 2)
-    denom_int = sqrt(float(n) * intermed_sq_sum[0] - intermed_sum[0] ** 2)
-    denominator = denom_samp * denom_int
-
-    result[col] = numerator / denominator