Initialized quantile-percentile branch

pytensor/tensor/math.py

@@ -26,6 +26,7 @@
     concatenate,
     constant,
     expand_dims,
+    extract_constant,
     stack,
     switch,
 )
@@ -1569,6 +1570,46 @@ def std(input, axis=None, ddof=0, keepdims=False, corrected=False):
     return ret
 
 
+def quantile(input, quant, axis=None):
+    """
+    Computes the median along the given axis(es) of a tensor `input`.
+    Parameters
+    ----------
+    input: TensorVariable
+        The input tensor.
+    quant: float
+        Probability for the quantiles to compute.
+        Values must be between 0 and 1 inclusive.
+    axis: None or int or (list of int) (see `Sum`)
+        Compute the quantile along this axis of the tensor.
+        None means computing along the flattened tensor.
+    """
+    input = as_tensor_variable(input)
+    input_ndim = input.type.ndim
+    if axis is None:
+        axis = list(range(input_ndim))
+    elif isinstance(axis, int | np.integer):
+        axis = [axis]
+    elif isinstance(axis, np.ndarray) and axis.ndim == 0:
+        axis = [int(axis)]
+    else:
+        axis = [int(a) for a in axis]
+
+    new_axes_order = [i for i in range(input.ndim) if i not in axis] + axis
+    input = input.dimshuffle(new_axes_order)
+
+    remaining_axis_size = shape(input)[: input.ndim - len(axis)]
+    flattened_axis_size = prod(shape(input)[input.ndim - len(axis) :])
+
+    input = input.reshape(concatenate([remaining_axis_size, [flattened_axis_size]]))
+    axis = -1
+
+    sorted_input = input.sort(axis=axis)
+    input_shape = input.shape[axis]
+    k = extract_constant(input_shape) * quant
+    return sorted_input[k]
+
+
 @scalar_elemwise(symbolname="scalar_maximum")
 def maximum(x, y):
     """elemwise maximum. See max for the maximum in one tensor"""