Remove ScoreHistoryMatch::Builder

chrome/browser/autocomplete/scored_history_match.cc

Index: chrome/browser/autocomplete/scored_history_match.cc
diff --git a/chrome/browser/autocomplete/scored_history_match.cc b/chrome/browser/autocomplete/scored_history_match.cc
index adb22c62e601262674f439f54c811e762f0f2416..50b9d2b2dba56aeb549da88adca3408464c43d82 100644
--- a/chrome/browser/autocomplete/scored_history_match.cc
+++ b/chrome/browser/autocomplete/scored_history_match.cc
@@ -4,25 +4,299 @@
 
 #include "chrome/browser/autocomplete/scored_history_match.h"
 
+#include <math.h>
+
+#include <algorithm>
+#include <vector>
+
+#include "base/logging.h"
+#include "base/numerics/safe_conversions.h"
+#include "base/strings/string_number_conversions.h"
+#include "base/strings/string_split.h"
+#include "base/strings/string_util.h"
+#include "base/strings/utf_offset_string_conversions.h"
+#include "base/strings/utf_string_conversions.h"
+#include "chrome/browser/autocomplete/history_url_provider.h"
+#include "components/bookmarks/browser/bookmark_utils.h"
+#include "components/omnibox/omnibox_field_trial.h"
+#include "components/omnibox/url_prefix.h"
+#include "content/public/browser/browser_thread.h"
+
+namespace {
+
+// The number of days of recency scores to precompute.
+const int kDaysToPrecomputeRecencyScoresFor = 366;
+
+// The number of raw term score buckets use; raw term scores greater this are
+// capped at the score of the largest bucket.
+const int kMaxRawTermScore = 30;
+
+// If true, assign raw scores to be max(whatever it normally would be, a score
+// that's similar to the score HistoryURL provider would assign). This variable
+// is set in the constructor by examining the field trial state.
+const bool kAlsoDoHupLikeScoring = false;
+
+// Pre-computed information to speed up calculating recency scores.
+// |days_ago_to_recency_score| is a simple array mapping how long ago a page was
+// visited (in days) to the recency score we should assign it.  This allows easy
+// lookups of scores without requiring math. This is initialized by
+// InitDaysAgoToRecencyScoreArray called by
+// ScoredHistoryMatch::Init().
+float days_ago_to_recency_score[kDaysToPrecomputeRecencyScoresFor];
+
+// Pre-computed information to speed up calculating topicality scores.
+// |raw_term_score_to_topicality_score| is a simple array mapping how raw terms
+// scores (a weighted sum of the number of hits for the term, weighted by how
+// important the hit is: hostname, path, etc.) to the topicality score we should
+// assign it.  This allows easy lookups of scores without requiring math. This
+// is initialized by InitRawTermScoreToTopicalityScoreArray() called from
+// ScoredHistoryMatch::Init().
+float raw_term_score_to_topicality_score[kMaxRawTermScore];
+
+// The maximum score that can be assigned to non-inlineable matches. This is
+// useful because often we want inlineable matches to come first (even if they
+// don't sometimes score as well as non-inlineable matches) because if a
+// non-inlineable match comes first than all matches will get demoted later in
+// HistoryQuickProvider to non-inlineable scores. Set to -1 to indicate no
+// maximum score.
+int max_assigned_score_for_non_inlineable_matches = -1;
+
+// Whether ScoredHistoryMatch::Init() has been called.
+bool initialized = false;
+
+// Precalculates raw_term_score_to_topicality_score, used in
+// GetTopicalityScore().
+void InitRawTermScoreToTopicalityScoreArray() {
+  for (int term_score = 0; term_score < kMaxRawTermScore; ++term_score) {
+    float topicality_score;
+    if (term_score < 10) {
+      // If the term scores less than 10 points (no full-credit hit, or
+      // no combination of hits that score that well), then the topicality
+      // score is linear in the term score.
+      topicality_score = 0.1 * term_score;
+    } else {
+      // For term scores of at least ten points, pass them through a log
+      // function so a score of 10 points gets a 1.0 (to meet up exactly
+      // with the linear component) and increases logarithmically until
+      // maxing out at 30 points, with computes to a score around 2.1.
+      topicality_score = (1.0 + 2.25 * log10(0.1 * term_score));
+    }
+    raw_term_score_to_topicality_score[term_score] = topicality_score;
+  }
+}
+
+// Pre-calculates days_ago_to_recency_score, used in GetRecencyScore().
+void InitDaysAgoToRecencyScoreArray() {
+  for (int days_ago = 0; days_ago < kDaysToPrecomputeRecencyScoresFor;
+       days_ago++) {
+    int unnormalized_recency_score;
+    if (days_ago <= 4) {
+      unnormalized_recency_score = 100;
+    } else if (days_ago <= 14) {
+      // Linearly extrapolate between 4 and 14 days so 14 days has a score
+      // of 70.
+      unnormalized_recency_score = 70 + (14 - days_ago) * (100 - 70) / (14 - 4);
+    } else if (days_ago <= 31) {
+      // Linearly extrapolate between 14 and 31 days so 31 days has a score
+      // of 50.
+      unnormalized_recency_score = 50 + (31 - days_ago) * (70 - 50) / (31 - 14);
+    } else if (days_ago <= 90) {
+      // Linearly extrapolate between 30 and 90 days so 90 days has a score
+      // of 30.
+      unnormalized_recency_score = 30 + (90 - days_ago) * (50 - 30) / (90 - 30);
+    } else {
+      // Linearly extrapolate between 90 and 365 days so 365 days has a score
+      // of 10.
+      unnormalized_recency_score =
+          10 + (365 - days_ago) * (20 - 10) / (365 - 90);
+    }
+    days_ago_to_recency_score[days_ago] = unnormalized_recency_score / 100.0;
+    if (days_ago > 0) {
+      DCHECK_LE(days_ago_to_recency_score[days_ago],
+                days_ago_to_recency_score[days_ago - 1]);
+    }
+  }
+}
+
+}  // namespace
+
 // static
 const size_t ScoredHistoryMatch::kMaxVisitsToScore = 10;
+int ScoredHistoryMatch::bookmark_value_ = 1;
+bool ScoredHistoryMatch::allow_tld_matches_ = false;
+bool ScoredHistoryMatch::allow_scheme_matches_ = false;
+bool ScoredHistoryMatch::hqp_experimental_scoring_enabled_ = false;
+float ScoredHistoryMatch::topicality_threshold_ = -1;
+std::vector<ScoredHistoryMatch::ScoreMaxRelevance>*
+    ScoredHistoryMatch::hqp_relevance_buckets_ = nullptr;
 
 ScoredHistoryMatch::ScoredHistoryMatch() : raw_score(0), can_inline(false) {
 }
 
-ScoredHistoryMatch::ScoredHistoryMatch(const history::URLRow& url_info,
-                                       size_t input_location,
-                                       bool match_in_scheme,
-                                       bool innermost_match,
-                                       int raw_score,
-                                       const TermMatches& url_matches,
-                                       const TermMatches& title_matches,
-                                       bool can_inline)
-    : HistoryMatch(url_info, input_location, match_in_scheme, innermost_match),
-      raw_score(raw_score),
-      url_matches(url_matches),
-      title_matches(title_matches),
-      can_inline(can_inline) {
+ScoredHistoryMatch::ScoredHistoryMatch(
+    const history::URLRow& row,
+    const VisitInfoVector& visits,
+    const std::string& languages,
+    const base::string16& lower_string,
+    const String16Vector& terms_vector,
+    const WordStarts& terms_to_word_starts_offsets,
+    const RowWordStarts& word_starts,
+    bool is_url_bookmarked,
+    base::Time now)
+    : HistoryMatch(row, 0, false, false), raw_score(0), can_inline(false) {
+  GURL gurl = row.url();
+  if (!gurl.is_valid())
+    return;
+
+  ScoredHistoryMatch::Init();
+
+  // Figure out where each search term appears in the URL and/or page title
+  // so that we can score as well as provide autocomplete highlighting.
+  base::OffsetAdjuster::Adjustments adjustments;
+  base::string16 url =
+      bookmarks::CleanUpUrlForMatching(gurl, languages, &adjustments);
+  base::string16 title = bookmarks::CleanUpTitleForMatching(row.title());
+  int term_num = 0;
+  for (const auto& term : terms_vector) {
+    TermMatches url_term_matches = MatchTermInString(term, url, term_num);
+    TermMatches title_term_matches = MatchTermInString(term, title, term_num);
+    if (url_term_matches.empty() && title_term_matches.empty()) {
+      // A term was not found in either URL or title - reject.
+      return;
+    }
+    url_matches.insert(url_matches.end(), url_term_matches.begin(),
+                       url_term_matches.end());
+    title_matches.insert(title_matches.end(), title_term_matches.begin(),
+                         title_term_matches.end());
+    ++term_num;
+  }
+
+  // Sort matches by offset and eliminate any which overlap.
+  // TODO(mpearson): Investigate whether this has any meaningful
+  // effect on scoring.  (It's necessary at some point: removing
+  // overlaps and sorting is needed to decide what to highlight in the
+  // suggestion string.  But this sort and de-overlap doesn't have to
+  // be done before scoring.)
+  url_matches = SortAndDeoverlapMatches(url_matches);
+  title_matches = SortAndDeoverlapMatches(title_matches);
+
+  // We can inline autocomplete a match if:
+  //  1) there is only one search term
+  //  2) AND the match begins immediately after one of the prefixes in
+  //     URLPrefix such as http://www and https:// (note that one of these
+  //     is the empty prefix, for cases where the user has typed the scheme)
+  //  3) AND the search string does not end in whitespace (making it look to
+  //     the IMUI as though there is a single search term when actually there
+  //     is a second, empty term).
+  // |best_inlineable_prefix| stores the inlineable prefix computed in
+  // clause (2) or NULL if no such prefix exists.  (The URL is not inlineable.)
+  // Note that using the best prefix here means that when multiple
+  // prefixes match, we'll choose to inline following the longest one.
+  // For a URL like "http://www.washingtonmutual.com", this means
+  // typing "w" will inline "ashington..." instead of "ww.washington...".
+  if (!url_matches.empty() && (terms_vector.size() == 1) &&
+      !IsWhitespace(*lower_string.rbegin())) {
+    const base::string16 gurl_spec = base::UTF8ToUTF16(gurl.spec());
+    const URLPrefix* best_inlineable_prefix =
+        URLPrefix::BestURLPrefix(gurl_spec, terms_vector[0]);
+    if (best_inlineable_prefix) {
+      // Initialize innermost_match.
+      // The idea here is that matches that occur in the scheme or
+      // "www." are worse than matches which don't.  For the URLs
+      // "http://www.google.com" and "http://wellsfargo.com", we want
+      // the omnibox input "w" to cause the latter URL to rank higher
+      // than the former.  Note that this is not the same as checking
+      // whether one match's inlinable prefix has more components than
+      // the other match's, since in this example, both matches would
+      // have an inlinable prefix of "http://", which is one component.
+      //
+      // Instead, we look for the overall best (i.e., most components)
+      // prefix of the current URL, and then check whether the inlinable
+      // prefix has that many components.  If it does, this is an
+      // "innermost" match, and should be boosted.  In the example
+      // above, the best prefixes for the two URLs have two and one
+      // components respectively, while the inlinable prefixes each
+      // have one component; this means the first match is not innermost
+      // and the second match is innermost, resulting in us boosting the
+      // second match.
+      //
+      // Now, the code that implements this.
+      // The deepest prefix for this URL regardless of where the match is.
+      const URLPrefix* best_prefix =
+          URLPrefix::BestURLPrefix(gurl_spec, base::string16());
+      DCHECK(best_prefix);
+      // If the URL is inlineable, we must have a match.  Note the prefix that
+      // makes it inlineable may be empty.
+      can_inline = true;
+      innermost_match =
+          best_inlineable_prefix->num_components == best_prefix->num_components;
+    }
+  }
+
+  const float topicality_score = GetTopicalityScore(
+      terms_vector.size(), url, terms_to_word_starts_offsets, word_starts);
+  const float frequency_score = GetFrequency(now, is_url_bookmarked, visits);
+  raw_score = base::saturated_cast<int>(GetFinalRelevancyScore(
+      topicality_score, frequency_score, *hqp_relevance_buckets_));
+
+  if (kAlsoDoHupLikeScoring && can_inline) {
+    // HistoryURL-provider-like scoring gives any match that is
+    // capable of being inlined a certain minimum score.  Some of these
+    // are given a higher score that lets them be shown in inline.
+    // This test here derives from the test in
+    // HistoryURLProvider::PromoteMatchForInlineAutocomplete().
+    const bool promote_to_inline =
+        (row.typed_count() > 1) || (IsHostOnly() && (row.typed_count() == 1));
+    int hup_like_score =
+        promote_to_inline
+            ? HistoryURLProvider::kScoreForBestInlineableResult
+            : HistoryURLProvider::kBaseScoreForNonInlineableResult;
+
+    // Also, if the user types the hostname of a host with a typed
+    // visit, then everything from that host get given inlineable scores
+    // (because the URL-that-you-typed will go first and everything
+    // else will be assigned one minus the previous score, as coded
+    // at the end of HistoryURLProvider::DoAutocomplete().
+    if (base::UTF8ToUTF16(gurl.host()) == terms_vector[0])
+      hup_like_score = HistoryURLProvider::kScoreForBestInlineableResult;
+
+    // HistoryURLProvider has the function PromoteOrCreateShorterSuggestion()
+    // that's meant to promote prefixes of the best match (if they've
+    // been visited enough related to the best match) or
+    // create/promote host-only suggestions (even if they've never
+    // been typed).  The code is complicated and we don't try to
+    // duplicate the logic here.  Instead, we handle a simple case: in
+    // low-typed-count ranges, give host-only matches (i.e.,
+    // http://www.foo.com/ vs. http://www.foo.com/bar.html) a boost so
+    // that the host-only match outscores all the other matches that
+    // would normally have the same base score.  This behavior is not
+    // identical to what happens in HistoryURLProvider even in these
+    // low typed count ranges--sometimes it will create/promote when
+    // this test does not (indeed, we cannot create matches like HUP
+    // can) and vice versa--but the underlying philosophy is similar.
+    if (!promote_to_inline && IsHostOnly())
+      hup_like_score++;
+
+    // All the other logic to goes into hup-like-scoring happens in
+    // the tie-breaker case of MatchScoreGreater().
+
+    // Incorporate hup_like_score into raw_score.
+    raw_score = std::max(raw_score, hup_like_score);
+  }
+
+  // If this match is not inlineable and there's a cap on the maximum
+  // score that can be given to non-inlineable matches, apply the cap.
+  if (!can_inline && (max_assigned_score_for_non_inlineable_matches != -1)) {
+    raw_score =
+        std::min(raw_score, max_assigned_score_for_non_inlineable_matches);
+  }
+
+  // Now that we're done processing this entry, correct the offsets of the
+  // matches in |url_matches| so they point to offsets in the original URL
+  // spec, not the cleaned-up URL string that we used for matching.
+  std::vector<size_t> offsets = OffsetsFromTermMatches(url_matches);
+  base::OffsetAdjuster::UnadjustOffsets(adjustments, &offsets);
+  url_matches = ReplaceOffsetsInTermMatches(url_matches, offsets);
 }
 
 ScoredHistoryMatch::~ScoredHistoryMatch() {
@@ -66,3 +340,369 @@ bool ScoredHistoryMatch::MatchScoreGreater(const ScoredHistoryMatch& m1,
   // URLs that have been visited more recently are better.
   return m1.url_info.last_visit() > m2.url_info.last_visit();
 }
+
+// static
+TermMatches ScoredHistoryMatch::FilterTermMatchesByWordStarts(
+    const TermMatches& term_matches,
+    const WordStarts& terms_to_word_starts_offsets,
+    const WordStarts& word_starts,
+    size_t start_pos,
+    size_t end_pos) {
+  // Return early if no filtering is needed.
+  if (start_pos == std::string::npos)
+    return term_matches;
+  TermMatches filtered_matches;
+  WordStarts::const_iterator next_word_starts = word_starts.begin();
+  WordStarts::const_iterator end_word_starts = word_starts.end();
+  for (const auto& term_match : term_matches) {
+    const size_t term_offset =
+        terms_to_word_starts_offsets[term_match.term_num];
+    // Advance next_word_starts until it's >= the position of the term we're
+    // considering (adjusted for where the word begins within the term).
+    while ((next_word_starts != end_word_starts) &&
+           (*next_word_starts < (term_match.offset + term_offset)))
+      ++next_word_starts;
+    // Add the match if it's before the position we start filtering at or
+    // after the position we stop filtering at (assuming we have a position
+    // to stop filtering at) or if it's at a word boundary.
+    if ((term_match.offset < start_pos) ||
+        ((end_pos != std::string::npos) && (term_match.offset >= end_pos)) ||
+        ((next_word_starts != end_word_starts) &&
+         (*next_word_starts == term_match.offset + term_offset)))
+      filtered_matches.push_back(term_match);
+  }
+  return filtered_matches;
+}
+
+// static
+void ScoredHistoryMatch::Init() {
+  // Because the code below is not thread safe, we check that we're only calling
+  // it from one thread: the UI thread.  Specifically, we check "if we've heard
+  // of the UI thread then we'd better be on it."  The first part is necessary
+  // so unit tests pass.  (Many unit tests don't set up the threading naming
+  // system; hence CurrentlyOn(UI thread) will fail.)
+  using content::BrowserThread;
+  DCHECK(!BrowserThread::IsThreadInitialized(BrowserThread::UI) ||
+         BrowserThread::CurrentlyOn(BrowserThread::UI));
+
+  if (initialized)
+    return;
+
+  initialized = true;
+
+  // When doing HUP-like scoring, don't allow a non-inlineable match
+  // to beat the score of good inlineable matches.  This is a problem
+  // because if a non-inlineable match ends up with the highest score
+  // from HistoryQuick provider, all HistoryQuick matches get demoted
+  // to non-inlineable scores (scores less than 1200).  Without
+  // HUP-like-scoring, these results would actually come from the HUP
+  // and not be demoted, thus outscoring the demoted HQP results.
+  // When the HQP provides these, we need to clamp the non-inlineable
+  // results to preserve this behavior.
+  if (kAlsoDoHupLikeScoring) {
+    max_assigned_score_for_non_inlineable_matches =
+        HistoryURLProvider::kScoreForBestInlineableResult - 1;
+  }
+  bookmark_value_ = OmniboxFieldTrial::HQPBookmarkValue();
+  allow_tld_matches_ = OmniboxFieldTrial::HQPAllowMatchInTLDValue();
+  allow_scheme_matches_ = OmniboxFieldTrial::HQPAllowMatchInSchemeValue();
+
+  InitRawTermScoreToTopicalityScoreArray();
+  InitDaysAgoToRecencyScoreArray();
+  InitHQPExperimentalParams();
+}
+
+float ScoredHistoryMatch::GetTopicalityScore(
+    const int num_terms,
+    const base::string16& url,
+    const WordStarts& terms_to_word_starts_offsets,
+    const RowWordStarts& word_starts) {
+  ScoredHistoryMatch::Init();
+  // A vector that accumulates per-term scores.  The strongest match--a
+  // match in the hostname at a word boundary--is worth 10 points.
+  // Everything else is less.  In general, a match that's not at a word
+  // boundary is worth about 1/4th or 1/5th of a match at the word boundary
+  // in the same part of the URL/title.
+  DCHECK_GT(num_terms, 0);
+  std::vector<int> term_scores(num_terms, 0);
+  WordStarts::const_iterator next_word_starts =
+      word_starts.url_word_starts_.begin();
+  WordStarts::const_iterator end_word_starts =
+      word_starts.url_word_starts_.end();
+  const size_t question_mark_pos = url.find('?');
+  const size_t colon_pos = url.find(':');
+  // The + 3 skips the // that probably appears in the protocol
+  // after the colon.  If the protocol doesn't have two slashes after
+  // the colon, that's okay--all this ends up doing is starting our
+  // search for the next / a few characters into the hostname.  The
+  // only times this can cause problems is if we have a protocol without
+  // a // after the colon and the hostname is only one or two characters.
+  // This isn't worth worrying about.
+  const size_t end_of_hostname_pos = (colon_pos != std::string::npos)
+                                         ? url.find('/', colon_pos + 3)
+                                         : url.find('/');
+  size_t last_part_of_hostname_pos = (end_of_hostname_pos != std::string::npos)
+                                         ? url.rfind('.', end_of_hostname_pos)
+                                         : url.rfind('.');
+  // Loop through all URL matches and score them appropriately.
+  // First, filter all matches not at a word boundary and in the path (or
+  // later).
+  url_matches = FilterTermMatchesByWordStarts(
+      url_matches, terms_to_word_starts_offsets, word_starts.url_word_starts_,
+      end_of_hostname_pos, std::string::npos);
+  if (colon_pos != std::string::npos) {
+    // Also filter matches not at a word boundary and in the scheme.
+    url_matches = FilterTermMatchesByWordStarts(
+        url_matches, terms_to_word_starts_offsets, word_starts.url_word_starts_,
+        0, colon_pos);
+  }
+  for (const auto& url_match : url_matches) {
+    const size_t term_offset = terms_to_word_starts_offsets[url_match.term_num];
+    // Advance next_word_starts until it's >= the position of the term we're
+    // considering (adjusted for where the word begins within the term).
+    while ((next_word_starts != end_word_starts) &&
+           (*next_word_starts < (url_match.offset + term_offset))) {
+      ++next_word_starts;
+    }
+    const bool at_word_boundary =
+        (next_word_starts != end_word_starts) &&
+        (*next_word_starts == url_match.offset + term_offset);
+    if ((question_mark_pos != std::string::npos) &&
+        (url_match.offset > question_mark_pos)) {
+      // The match is in a CGI ?... fragment.
+      DCHECK(at_word_boundary);
+      term_scores[url_match.term_num] += 5;
+    } else if ((end_of_hostname_pos != std::string::npos) &&
+               (url_match.offset > end_of_hostname_pos)) {
+      // The match is in the path.
+      DCHECK(at_word_boundary);
+      term_scores[url_match.term_num] += 8;
+    } else if ((colon_pos == std::string::npos) ||
+               (url_match.offset > colon_pos)) {
+      // The match is in the hostname.
+      if ((last_part_of_hostname_pos == std::string::npos) ||
+          (url_match.offset < last_part_of_hostname_pos)) {
+        // Either there are no dots in the hostname or this match isn't
+        // the last dotted component.
+        term_scores[url_match.term_num] += at_word_boundary ? 10 : 2;
+      } else {
+        // The match is in the last part of a dotted hostname (usually this
+        // is the top-level domain .com, .net, etc.).
+        if (allow_tld_matches_)
+          term_scores[url_match.term_num] += at_word_boundary ? 10 : 0;
+      }
+    } else {
+      // The match is in the protocol (a.k.a. scheme).
+      // Matches not at a word boundary should have been filtered already.
+      DCHECK(at_word_boundary);
+      match_in_scheme = true;
+      if (allow_scheme_matches_)
+        term_scores[url_match.term_num] += 10;
+    }
+  }
+  // Now do the analogous loop over all matches in the title.
+  next_word_starts = word_starts.title_word_starts_.begin();
+  end_word_starts = word_starts.title_word_starts_.end();
+  int word_num = 0;
+  title_matches = FilterTermMatchesByWordStarts(
+      title_matches, terms_to_word_starts_offsets,
+      word_starts.title_word_starts_, 0, std::string::npos);
+  for (const auto& title_match : title_matches) {
+    const size_t term_offset =
+        terms_to_word_starts_offsets[title_match.term_num];
+    // Advance next_word_starts until it's >= the position of the term we're
+    // considering (adjusted for where the word begins within the term).
+    while ((next_word_starts != end_word_starts) &&
+           (*next_word_starts < (title_match.offset + term_offset))) {
+      ++next_word_starts;
+      ++word_num;
+    }
+    if (word_num >= 10)
+      break;  // only count the first ten words
+    DCHECK(next_word_starts != end_word_starts);
+    DCHECK_EQ(*next_word_starts, title_match.offset + term_offset)
+        << "not at word boundary";
+    term_scores[title_match.term_num] += 8;
+  }
+  // TODO(mpearson): Restore logic for penalizing out-of-order matches.
+  // (Perhaps discount them by 0.8?)
+  // TODO(mpearson): Consider: if the earliest match occurs late in the string,
+  // should we discount it?
+  // TODO(mpearson): Consider: do we want to score based on how much of the
+  // input string the input covers?  (I'm leaning toward no.)
+
+  // Compute the topicality_score as the sum of transformed term_scores.
+  float topicality_score = 0;
+  for (int term_score : term_scores) {
+    // Drop this URL if it seems like a term didn't appear or, more precisely,
+    // didn't appear in a part of the URL or title that we trust enough
+    // to give it credit for.  For instance, terms that appear in the middle
+    // of a CGI parameter get no credit.  Almost all the matches dropped
+    // due to this test would look stupid if shown to the user.
+    if (term_score == 0)
+      return 0;
+    topicality_score += raw_term_score_to_topicality_score[std::min(
+        term_score, kMaxRawTermScore - 1)];
+  }
+  // TODO(mpearson): If there are multiple terms, consider taking the
+  // geometric mean of per-term scores rather than the arithmetic mean.
+
+  const float final_topicality_score = topicality_score / num_terms;
+
+  // Demote the URL if the topicality score is less than threshold.
+  if (hqp_experimental_scoring_enabled_ &&
+      (final_topicality_score < topicality_threshold_)) {
+    return 0.0;
+  }
+
+  return final_topicality_score;
+}
+
+// static
+float ScoredHistoryMatch::GetRecencyScore(int last_visit_days_ago) {
+  ScoredHistoryMatch::Init();
+  // Lookup the score in days_ago_to_recency_score, treating
+  // everything older than what we've precomputed as the oldest thing
+  // we've precomputed.  The std::max is to protect against corruption
+  // in the database (in case last_visit_days_ago is negative).
+  return days_ago_to_recency_score[std::max(
+      std::min(last_visit_days_ago, kDaysToPrecomputeRecencyScoresFor - 1), 0)];
+}
+
+// static
+float ScoredHistoryMatch::GetFrequency(const base::Time& now,
+                                       const bool bookmarked,
+                                       const VisitInfoVector& visits) {
+  // Compute the weighted average |value_of_transition| over the last at
+  // most kMaxVisitsToScore visits, where each visit is weighted using
+  // GetRecencyScore() based on how many days ago it happened.  Use
+  // kMaxVisitsToScore as the denominator for the average regardless of
+  // how many visits there were in order to penalize a match that has
+  // fewer visits than kMaxVisitsToScore.
+  float summed_visit_points = 0;
+  const size_t max_visit_to_score =
+      std::min(visits.size(), ScoredHistoryMatch::kMaxVisitsToScore);
+  for (size_t i = 0; i < max_visit_to_score; ++i) {
+    int value_of_transition =
+        (visits[i].second == ui::PAGE_TRANSITION_TYPED) ? 20 : 1;
+    if (bookmarked)
+      value_of_transition = std::max(value_of_transition, bookmark_value_);
+    const float bucket_weight =
+        GetRecencyScore((now - visits[i].first).InDays());
+    summed_visit_points += (value_of_transition * bucket_weight);
+  }
+  return visits.size() * summed_visit_points /
+         ScoredHistoryMatch::kMaxVisitsToScore;
+}
+
+// static
+float ScoredHistoryMatch::GetFinalRelevancyScore(
+    float topicality_score,
+    float frequency_score,
+    const std::vector<ScoreMaxRelevance>& hqp_relevance_buckets) {
+  DCHECK(hqp_relevance_buckets.size() > 0);
+  DCHECK_EQ(hqp_relevance_buckets[0].first, 0.0);
+
+  if (topicality_score == 0)
+    return 0;
+  // Here's how to interpret intermediate_score: Suppose the omnibox
+  // has one input term.  Suppose we have a URL for which the omnibox
+  // input term has a single URL hostname hit at a word boundary.  (This
+  // implies topicality_score = 1.0.).  Then the intermediate_score for
+  // this URL will depend entirely on the frequency_score with
+  // this interpretation:
+  // - a single typed visit more than three months ago, no other visits -> 0.2
+  // - a visit every three days, no typed visits -> 0.706
+  // - a visit every day, no typed visits -> 0.916
+  // - a single typed visit yesterday, no other visits -> 2.0
+  // - a typed visit once a week -> 11.77
+  // - a typed visit every three days -> 14.12
+  // - at least ten typed visits today -> 20.0 (maximum score)
+  //
+  // The below code maps intermediate_score to the range [0, 1399].
+  // For example:
+  // HQP default scoring buckets: "0.0:400,1.5:600,12.0:1300,20.0:1399"
+  // We will linearly interpolate the scores between:
+  //      0 to 1.5    --> 400 to 600
+  //    1.5 to 12.0   --> 600 to 1300
+  //    12.0 to 20.0  --> 1300 to 1399
+  //       >= 20.0    --> 1399
+  //
+  // The score maxes out at 1399 (i.e., cannot beat a good inlineable result
+  // from HistoryURL provider).
+  const float intermediate_score = topicality_score * frequency_score;
+
+  // Find the threshold where intermediate score is greater than bucket.
+  size_t i = 1;
+  for (; i < hqp_relevance_buckets.size(); ++i) {
+    const ScoreMaxRelevance& hqp_bucket = hqp_relevance_buckets[i];
+    if (intermediate_score >= hqp_bucket.first) {
+      continue;
+    }
+    const ScoreMaxRelevance& previous_bucket = hqp_relevance_buckets[i - 1];
+    const float slope = ((hqp_bucket.second - previous_bucket.second) /
+                         (hqp_bucket.first - previous_bucket.first));
+    return (previous_bucket.second +
+            (slope * (intermediate_score - previous_bucket.first)));
+  }
+  // It will reach this stage when the score is > highest bucket score.
+  // Return the highest bucket score.
+  return hqp_relevance_buckets[i - 1].second;
+}
+
+// static
+void ScoredHistoryMatch::InitHQPExperimentalParams() {
+  // These are default HQP relevance scoring buckets.
+  // See GetFinalRelevancyScore() for details.
+  std::string hqp_relevance_buckets_str = "0.0:400,1.5:600,12.0:1300,20.0:1399";
+
+  // Fetch the experiment params if they are any.
+  hqp_experimental_scoring_enabled_ =
+      OmniboxFieldTrial::HQPExperimentalScoringEnabled();
+
+  if (hqp_experimental_scoring_enabled_) {
+    // Add the topicality threshold from experiment params.
+    float hqp_experimental_topicality_threhold =
+        OmniboxFieldTrial::HQPExperimentalTopicalityThreshold();
+    topicality_threshold_ = hqp_experimental_topicality_threhold;
+
+    // Add the HQP experimental scoring buckets.
+    std::string hqp_experimental_scoring_buckets =
+        OmniboxFieldTrial::HQPExperimentalScoringBuckets();
+    if (!hqp_experimental_scoring_buckets.empty())
+      hqp_relevance_buckets_str = hqp_experimental_scoring_buckets;
+  }
+
+  // Parse the hqp_relevance_buckets_str string once and store them in vector
+  // which is easy to access.
+  hqp_relevance_buckets_ =
+      new std::vector<ScoredHistoryMatch::ScoreMaxRelevance>();
+
+  bool is_valid_bucket_str = GetHQPBucketsFromString(hqp_relevance_buckets_str,
+                                                     hqp_relevance_buckets_);
+  DCHECK(is_valid_bucket_str);
+}
+
+// static
+bool ScoredHistoryMatch::GetHQPBucketsFromString(
+    const std::string& buckets_str,
+    std::vector<ScoreMaxRelevance>* hqp_buckets) {
+  DCHECK(hqp_buckets != NULL);
+  DCHECK(!buckets_str.empty());
+
+  base::StringPairs kv_pairs;
+  if (base::SplitStringIntoKeyValuePairs(buckets_str, ':', ',', &kv_pairs)) {
+    for (base::StringPairs::const_iterator it = kv_pairs.begin();
+         it != kv_pairs.end(); ++it) {
+      ScoreMaxRelevance bucket;
+      bool is_valid_intermediate_score =
+          base::StringToDouble(it->first, &bucket.first);
+      DCHECK(is_valid_intermediate_score);
+      bool is_valid_hqp_score = base::StringToInt(it->second, &bucket.second);
+      DCHECK(is_valid_hqp_score);
+      hqp_buckets->push_back(bucket);
+    }
+    return true;
+  }
+  return false;
+}