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Let's define a function f(s)
over a non-empty string s
, which calculates the frequency of the smallest character in s
. For example, if s = "dcce"
then f(s) = 2
because the smallest character is "c"
and its frequency is 2.
Now, given string arrays queries
and words
, return an integer array answer
, where each answer[i]
is the number of words such that f(queries[i])
< f(W)
, where W
is a word in words
.
Example 1:
Input: queries = ["cbd"], words = ["zaaaz"] Output: [1] Explanation: On the first query we have f("cbd") = 1, f("zaaaz") = 3 so f("cbd") < f("zaaaz").
Example 2:
Input: queries = ["bbb","cc"], words = ["a","aa","aaa","aaaa"] Output: [1,2] Explanation: On the first query only f("bbb") < f("aaaa"). On the second query both f("aaa") and f("aaaa") are both > f("cc").
Constraints:
1 <= queries.length <= 2000
1 <= words.length <= 2000
1 <= queries[i].length, words[i].length <= 10
queries[i][j]
,words[i][j]
are English lowercase letters.
We define a function f(s)
, passing in parameters s
is a non-empty string; function is the function of statistics s
appear (by comparing lexicographically) smallest letter frequency.
For example, if s = "dcce"
, then f(s) = 2
, because the smallest letters "c"
, it appears twice.
Now, to give you two string arrays to be look-up table queries
and glossary words
, please return an array of integers answer
as the answer, each of which answer[i]
is to meet f(queries[i])
< f(W)
the number of words, W
it is a glossary words
of words.
Example 1:
Input: queries = [ "cbd"] , words = [ "zaaaz"] Output: [1] Explanation: Query f ( "cbd") = 1 , and f ( "zaaaz") = 3 so that f ( "cbd") <f ( "zaaaz").
Example 2:
Input: queries = [ "bbb", "cc"], words = [ "a", "aa", "aaa", "aaaa"] Output: [1,2] Explanation: first query f ( "bbb ") <f (" aaaa " ), the second query f (" aaa ") and f (" aaaa ") are> f (" cc ").
prompt:
1 <= queries.length <= 2000
1 <= words.length <= 2000
1 <= queries[i].length, words[i].length <= 10
queries[i][j]
,words[i][j]
All lowercase letters