Speed-up Weighted Quantile Calculation (#11)

* Speeds up weighted quantile calculation

Order quantiles monotonically and reuse floor and ceil indices.

quantile_forest/_quantile_forest_fast.pyx

@@ -317,6 +317,12 @@ cpdef vector[double] calc_weighted_quantile(
     if not issorted:
         parallel_qsort_asc(inputs, weights, 0, n_inputs-1)
 
+    # Get monotonic sorting of quantiles for efficient calculation.
+    sorted_quantile_idx = vector[double](n_quantiles)
+    for i in range(<SIZE_t>(sorted_quantile_idx.size())):
+        sorted_quantile_idx[i] = <double>i
+    parallel_qsort_asc(quantiles, sorted_quantile_idx, 0, n_quantiles-1)
+
     cum_weights = vector[double](n_inputs)
 
     # Calculate the empirical cumulative distribution function (ECDF).
@@ -331,16 +337,20 @@ cpdef vector[double] calc_weighted_quantile(
 
     out = vector[double](n_quantiles)
 
+    idx_floor = 0
+    idx_ceil = 1
+
     for i in range(n_quantiles):
         quantile = quantiles[i]
 
+        # Assign the output based on the input quantile ordering.
+        i = <SIZE_t>sorted_quantile_idx[i]
+
         # Calculate the quantile's proportion of total weight.
         p = quantile * f + C
 
         # Find the first index where the proportion of weight exceeds p.
-        idx_floor = 0
-        idx_ceil = 1
-        for j in range(n_inputs):
+        for j in range(idx_floor, n_inputs):
             if p >= cum_weights[j]:
                 if weights[j] > 0:
                     idx_floor = j