Refactor xintegrator

n3fit/src/n3fit/backends/keras_backend/operations.py

@@ -196,18 +196,6 @@ def tensor_ones_like(*args, **kwargs):
     return K.ones_like(*args, **kwargs)
 
 
-@tf.function
-def many_replication(grid, replications, axis=0, **kwargs):
-    """
-    Generates a tensor with one extra dimension:
-        a repetition of "grid" n times along the given axis
-    from keras documentation:
-    If x has shape (s1, s2, s3) and axis is 1, the output will have shape (s1, s2 * rep, s3)
-    see full `docs <https://www.tensorflow.org/api_docs/python/tf/keras/backend/repeat_elements>`_
-    """
-    return K.repeat_elements(grid, rep=replications, axis=axis, **kwargs)
-
-
 # Property operations
 # modify properties of the tensor like the shape or elements it has
 @tf.function

n3fit/src/n3fit/layers/x_operations.py

@@ -56,24 +56,23 @@ def get_config(self):
 
 class xIntegrator(MetaLayer):
     """
-    This layer performs a sum of the input layer/tensor on the first axis
+    This layer performs a sum of the input layer/tensor on the axis corresponding to the x-grid
+    weighted by the weights of the grid.
 
-    Receives as input a rank-n (n > 1) tensor `x` (batch_dims ..., xpoints, flavours)
-    and returns a summation on the `xpoints` index (i.e., index -2)
-    weighted by the weights of the grid
+    The output shape is the input shape with the x-axis removed.
 
     Parameters
     ----------
         grid_weights: np.array
             weights of the grid
+        x_axis: int (default=1)
+            axis of the input tensor that corresponds to the x-grid
     """
 
-    def __init__(self, grid_weights, output_dim=BASIS_SIZE, **kwargs):
-        grid_weights_tensor = op.numpy_to_tensor(grid_weights)
-        # Open up the grid weights
-        self.grid_weights = op.many_replication(grid_weights_tensor, output_dim, axis=1)
+    def __init__(self, grid_weights, x_axis=1, **kwargs):
+        self.x_axis = x_axis
+        self.grid_weights = op.flatten(op.numpy_to_tensor(grid_weights))
         super().__init__(**kwargs)
 
-    def call(self, x):
-        xx = x * self.grid_weights
-        return op.sum(xx, axis=-2)
+    def call(self, pdf):
+        return op.tensor_product(pdf, self.grid_weights, axes=[self.x_axis, 0])

n3fit/src/n3fit/tests/test_xops.py

@@ -3,7 +3,8 @@
 """
 import numpy as np
 
-from n3fit.layers import xDivide
+from n3fit.backends import operations as op
+from n3fit.layers import xDivide, xIntegrator
 
 
 def test_xdivide_default():
@@ -21,7 +22,7 @@ def test_xdivide_default():
 
 
 def test_xdivide_indices():
-    """Check that the default xDivide works as expected"""
+    """Check that xDivide with custom indices works as expected"""
     custom_indices = [0, 1, 7]
     x_div = xDivide(div_list=custom_indices)
     test_input = np.array([1, 2, 3], dtype=np.float32).reshape((1, 3, 1))
@@ -32,3 +33,15 @@ def test_xdivide_indices():
         expected_output[:, :, i] = 1 / test_input[:, :, 0]
 
     np.testing.assert_allclose(test_output, expected_output, rtol=1e-05)
+
+
+def test_xintegrator():
+    np.random.seed(42)
+    weights = np.random.rand(5, 1)
+    pdf = op.numpy_to_tensor(np.random.rand(1, 5, 8))
+    xint = xIntegrator(weights)
+    xint_out = xint(pdf)
+    xint_out_reference = np.array(
+        [[0.405455, 0.878931, 0.937715, 0.906214, 1.984154, 1.147975, 1.642387, 1.549858]]
+    )
+    np.testing.assert_allclose(xint_out.numpy(), xint_out_reference, rtol=1e-05)