Restoring authorship annotation for <serxa@yandex-team.ru>. Commit 1 of 2.

1 files changed, 162 insertions, 162 deletions

diff --git a/library/cpp/lwtrace/mon/analytics/transform.h b/library/cpp/lwtrace/mon/analytics/transform.h
index f7dc9adb5b..bd80d49d35 100644
--- a/library/cpp/lwtrace/mon/analytics/transform.h
+++ b/library/cpp/lwtrace/mon/analytics/transform.h
@@ -1,204 +1,204 @@
-#pragma once
-
+#pragma once 
+ 
 #include "data.h"
-
-namespace NAnalytics {
-
-template <class TSkip, class TX, class TY>
-inline TTable Histogram(const TTable& in, TSkip skip,
+ 
+namespace NAnalytics { 
+ 
+template <class TSkip, class TX, class TY> 
+inline TTable Histogram(const TTable& in, TSkip skip, 
                  const TString& xn_out, TX x_in,
                  const TString& yn_out, TY y_in,
-                 double x1, double x2, double dx)
-{
-    long buckets = (x2 - x1) / dx;
-    TTable out;
+                 double x1, double x2, double dx) 
+{ 
+    long buckets = (x2 - x1) / dx; 
+    TTable out; 
     TString yn_sum = yn_out + "_sum";
     TString yn_share = yn_out + "_share";
-    double ysum = 0.0;
-    out.resize(buckets);
-    for (size_t i = 0; i < out.size(); i++) {
-        double lb = x1 + dx*i;
-        double ub = lb + dx;
-        out[i].Name = "[" + ToString(lb) + ";" + ToString(ub) + (ub==x2? "]": ")");
-        out[i][xn_out] = (lb + ub) / 2;
-        out[i][yn_sum] = 0.0;
-    }
-    for (const auto& row : in) {
-        if (skip(row)) {
-            continue;
-        }
-        double x = x_in(row);
-        long i = (x - x1) / dx;
-        if (x == x2) { // Special hack to include right edge
-            i--;
-        }
-        double y = y_in(row);
-        ysum += y;
-        if (i >= 0 && i < buckets) {
+    double ysum = 0.0; 
+    out.resize(buckets); 
+    for (size_t i = 0; i < out.size(); i++) { 
+        double lb = x1 + dx*i; 
+        double ub = lb + dx; 
+        out[i].Name = "[" + ToString(lb) + ";" + ToString(ub) + (ub==x2? "]": ")"); 
+        out[i][xn_out] = (lb + ub) / 2; 
+        out[i][yn_sum] = 0.0; 
+    } 
+    for (const auto& row : in) { 
+        if (skip(row)) { 
+            continue; 
+        } 
+        double x = x_in(row); 
+        long i = (x - x1) / dx; 
+        if (x == x2) { // Special hack to include right edge 
+            i--; 
+        } 
+        double y = y_in(row); 
+        ysum += y; 
+        if (i >= 0 && i < buckets) { 
             out[i][yn_sum] = y + out[i].GetOrDefault(yn_sum, 0.0);
-        }
-    }
-    for (TRow& row : out) {
-        if (ysum != 0.0) {
+        } 
+    } 
+    for (TRow& row : out) { 
+        if (ysum != 0.0) { 
             row[yn_share] = row.GetOrDefault(yn_sum, 0.0) / ysum;
-        }
-    }
-    return out;
-}
-
+        } 
+    } 
+    return out; 
+} 
+ 
 inline TTable HistogramAll(const TTable& in, const TString& xn, double x1, double x2, double dx)
-{
-    long buckets = (dx == 0.0? 1: (x2 - x1) / dx);
-    TTable out;
+{ 
+    long buckets = (dx == 0.0? 1: (x2 - x1) / dx); 
+    TTable out; 
     THashMap<TString, double> colSum;
-    out.resize(buckets);
-
+    out.resize(buckets); 
+ 
     TSet<TString> cols;
-    for (auto& row : in) {
-        for (auto& kv : row) {
-            cols.insert(kv.first);
-        }
-    }
-    cols.insert("_count");
-    cols.erase(xn);
-
+    for (auto& row : in) { 
+        for (auto& kv : row) { 
+            cols.insert(kv.first); 
+        } 
+    } 
+    cols.insert("_count"); 
+    cols.erase(xn); 
+ 
     for (const TString& col : cols) {
-        colSum[col] = 0.0;
-    }
-
-    for (size_t i = 0; i < out.size(); i++) {
-        double lb = x1 + dx*i;
-        double ub = lb + dx;
-        TRow& row = out[i];
-        row.Name = "[" + ToString(lb) + ";" + ToString(ub) + (ub==x2? "]": ")");
-        row[xn] = (lb + ub) / 2;
+        colSum[col] = 0.0; 
+    } 
+ 
+    for (size_t i = 0; i < out.size(); i++) { 
+        double lb = x1 + dx*i; 
+        double ub = lb + dx; 
+        TRow& row = out[i]; 
+        row.Name = "[" + ToString(lb) + ";" + ToString(ub) + (ub==x2? "]": ")"); 
+        row[xn] = (lb + ub) / 2; 
         for (const TString& col : cols) {
-            row[col + "_sum"] = 0.0;
-        }
-    }
-    for (const TRow& row_in : in) {
-        double x;
-        if (!row_in.Get(xn, x)) {
-            continue;
-        }
-        long i = (dx == 0.0? 0: (x - x1) / dx);
-        if (x == x2 && dx > 0.0) { // Special hack to include right edge
-            i--;
-        }
-        for (const auto& kv : row_in) {
+            row[col + "_sum"] = 0.0; 
+        } 
+    } 
+    for (const TRow& row_in : in) { 
+        double x; 
+        if (!row_in.Get(xn, x)) { 
+            continue; 
+        } 
+        long i = (dx == 0.0? 0: (x - x1) / dx); 
+        if (x == x2 && dx > 0.0) { // Special hack to include right edge 
+            i--; 
+        } 
+        for (const auto& kv : row_in) { 
             const TString& yn = kv.first;
-            if (yn == xn) {
-                continue;
-            }
+            if (yn == xn) { 
+                continue; 
+            } 
             double y;
             if (!row_in.Get(yn, y)) {
                 continue;
             }
-            colSum[yn] += y;
-            if (i >= 0 && i < buckets) {
+            colSum[yn] += y; 
+            if (i >= 0 && i < buckets) { 
                 out[i][yn + "_cnt"] = out[i].GetOrDefault(yn + "_cnt") + 1;
                 out[i][yn + "_sum"] = out[i].GetOrDefault(yn + "_sum") + y;
                 if (out[i].contains(yn + "_min")) {
                     out[i][yn + "_min"] = Min(y, out[i].GetOrDefault(yn + "_min"));
-                } else {
-                    out[i][yn + "_min"] = y;
-                }
+                } else { 
+                    out[i][yn + "_min"] = y; 
+                } 
                 if (out[i].contains(yn + "_max")) {
                     out[i][yn + "_max"] = Max(y, out[i].GetOrDefault(yn + "_max"));
-                } else {
-                    out[i][yn + "_max"] = y;
-                }
-            }
-        }
-        colSum["_count"]++;
-        if (i >= 0 && i < buckets) {
+                } else { 
+                    out[i][yn + "_max"] = y; 
+                } 
+            } 
+        } 
+        colSum["_count"]++; 
+        if (i >= 0 && i < buckets) { 
             out[i]["_count_sum"] = out[i].GetOrDefault("_count_sum") + 1;
-        }
-    }
-    for (TRow& row : out) {
+        } 
+    } 
+    for (TRow& row : out) { 
         for (const TString& col : cols) {
-            double ysum = colSum[col];
-            if (col != "_count") {
+            double ysum = colSum[col]; 
+            if (col != "_count") { 
                 if (row.GetOrDefault(col + "_cnt") != 0.0) {
                     row[col + "_avg"] = row.GetOrDefault(col + "_sum") / row.GetOrDefault(col + "_cnt");
-                }
-            }
-            if (ysum != 0.0) {
+                } 
+            } 
+            if (ysum != 0.0) { 
                 row[col + "_share"] = row.GetOrDefault(col + "_sum") / ysum;
-            }
-        }
-    }
-    return out;
-}
-
-inline TMatrix CovarianceMatrix(const TTable& in)
-{
+            } 
+        } 
+    } 
+    return out; 
+} 
+ 
+inline TMatrix CovarianceMatrix(const TTable& in) 
+{ 
     TSet<TString> cols;
-    for (auto& row : in) {
-        for (auto& kv : row) {
-            cols.insert(kv.first);
-        }
-    }
-
-    struct TAggregate {
-        size_t Idx = 0;
-        double Sum = 0;
-        size_t Count = 0;
-        double Mean = 0;
-    };
-
+    for (auto& row : in) { 
+        for (auto& kv : row) { 
+            cols.insert(kv.first); 
+        } 
+    } 
+ 
+    struct TAggregate { 
+        size_t Idx = 0; 
+        double Sum = 0; 
+        size_t Count = 0; 
+        double Mean = 0; 
+    }; 
+ 
     THashMap<TString, TAggregate> colAggr;
-
-    size_t colCount = 0;
-    for (const TString& col : cols) {
-        TAggregate& aggr = colAggr[col];
-        aggr.Idx = colCount++;
-    }
-
-    for (const TRow& row : in) {
-        for (const auto& kv : row) {
-            const TString& xn = kv.first;
+ 
+    size_t colCount = 0; 
+    for (const TString& col : cols) { 
+        TAggregate& aggr = colAggr[col]; 
+        aggr.Idx = colCount++; 
+    } 
+ 
+    for (const TRow& row : in) { 
+        for (const auto& kv : row) { 
+            const TString& xn = kv.first; 
             double x;
             if (!row.Get(xn, x)) {
                 continue;
             }
-            TAggregate& aggr = colAggr[xn];
-            aggr.Sum += x;
-            aggr.Count++;
-        }
-    }
-
-    for (auto& kv : colAggr) {
-        TAggregate& aggr = kv.second;
-        aggr.Mean = aggr.Sum / aggr.Count;
-    }
-
-    TMatrix covCount(cols.size(), cols.size());
-    TMatrix cov(cols.size(), cols.size());
-    for (const TRow& row : in) {
-        for (const auto& kv1 : row) {
+            TAggregate& aggr = colAggr[xn]; 
+            aggr.Sum += x; 
+            aggr.Count++; 
+        } 
+    } 
+ 
+    for (auto& kv : colAggr) { 
+        TAggregate& aggr = kv.second; 
+        aggr.Mean = aggr.Sum / aggr.Count; 
+    } 
+ 
+    TMatrix covCount(cols.size(), cols.size()); 
+    TMatrix cov(cols.size(), cols.size()); 
+    for (const TRow& row : in) { 
+        for (const auto& kv1 : row) { 
             double x;
             if (!row.Get(kv1.first, x)) {
                 continue;
             }
-            TAggregate& xaggr = colAggr[kv1.first];
-            for (const auto& kv2 : row) {
+            TAggregate& xaggr = colAggr[kv1.first]; 
+            for (const auto& kv2 : row) { 
                 double y;
                 if (!row.Get(kv2.first, y)) {
                     continue;
                 }                
-                TAggregate& yaggr = colAggr[kv2.first];
-                covCount.Cell(xaggr.Idx, yaggr.Idx)++;
-                cov.Cell(xaggr.Idx, yaggr.Idx) += (x - xaggr.Mean) * (y - yaggr.Mean);
-            }
-        }
-    }
-
-    for (size_t idx = 0; idx < cov.size(); idx++) {
-        cov[idx] /= covCount[idx];
-    }
-
-    return cov;
-}
-
-}
+                TAggregate& yaggr = colAggr[kv2.first]; 
+                covCount.Cell(xaggr.Idx, yaggr.Idx)++; 
+                cov.Cell(xaggr.Idx, yaggr.Idx) += (x - xaggr.Mean) * (y - yaggr.Mean); 
+            } 
+        } 
+    } 
+ 
+    for (size_t idx = 0; idx < cov.size(); idx++) { 
+        cov[idx] /= covCount[idx]; 
+    } 
+ 
+    return cov; 
+} 
+ 
+}