Create search-suggestions-system.py

Author: kamyu104

Python/search-suggestions-system.py

@@ -0,0 +1,118 @@
+# Time:  ctor: O(n * l), n is the number of products
+#                      , l is the average length of product name
+#        suggest: O(l^2)
+# Space: O(t), t is the number of nodes in trie
+
+import collections
+
+
+class TrieNode(object):
+
+    def __init__(self):
+        self.__TOP_COUNT = 3
+        self.leaves = collections.defaultdict(TrieNode)
+        self.infos = []
+
+    def insert(self, words, i):
+        curr = self
+        for c in words[i]:
+            curr = curr.leaves[c]
+            curr.add_info(words, i)
+
+    def add_info(self, words, i):
+        self.infos.append(i)
+        self.infos.sort(key=lambda x: words[x])
+        if len(self.infos) > self.__TOP_COUNT:
+            self.infos.pop()
+
+
+class Solution(object):
+    def suggestedProducts(self, products, searchWord):
+        """
+        :type products: List[str]
+        :type searchWord: str
+        :rtype: List[List[str]]
+        """
+        trie = TrieNode()
+        for i in xrange(len(products)):
+            trie.insert(products, i)
+        result = [[] for _ in xrange(len(searchWord))]
+        for i, c in enumerate(searchWord):
+            if c not in trie.leaves:
+                break
+            trie = trie.leaves[c]
+            result[i] = map(lambda x: products[x], trie.infos)
+        return result
+
+
+# Time:  ctor: O(n * l * log(n * l)), n is the number of products
+#                                   , l is the average length of product name
+#        suggest: O(l^2)
+# Space: O(t), t is the number of nodes in trie
+class TrieNode2(object):
+
+    def __init__(self):
+        self.__TOP_COUNT = 3
+        self.leaves = collections.defaultdict(TrieNode2)
+        self.infos = []
+
+    def insert(self, words, i):
+        curr = self
+        for c in words[i]:
+            curr = curr.leaves[c]
+            curr.add_info(i)
+
+    def add_info(self, i):
+        if len(self.infos) == self.__TOP_COUNT:
+            return
+        self.infos.append(i)
+
+
+class Solution2(object):
+    def suggestedProducts(self, products, searchWord):
+        """
+        :type products: List[str]
+        :type searchWord: str
+        :rtype: List[List[str]]
+        """
+        products.sort()
+        trie = TrieNode2()
+        for i in xrange(len(products)):
+            trie.insert(products, i)
+        result = [[] for _ in xrange(len(searchWord))]
+        for i, c in enumerate(searchWord):
+            if c not in trie.leaves:
+                break
+            trie = trie.leaves[c]
+            result[i] = map(lambda x: products[x], trie.infos)
+        return result
+
+
+# Time:  ctor: O(n * l * log(n * l)), n is the number of products
+#                                   , l is the average length of product name
+#        suggest: O(l^2 * n)
+# Space: O(n * l)
+import bisect
+
+
+class Solution3(object):
+    def suggestedProducts(self, products, searchWord):
+        """
+        :type products: List[str]
+        :type searchWord: str
+        :rtype: List[List[str]]
+        """
+        products.sort()  # Time: O(n * l * log(n * l))
+        result = []
+        prefix = ""
+        for i, c in enumerate(searchWord):  # Time: O(l)
+            prefix += c
+            start = bisect.bisect_left(products, prefix)  # Time: O(log(n * l))
+            new_products = []
+            for j in xrange(start, len(products)):  # Time: O(n * l)
+                if not (i < len(products[j]) and products[j][i] == c):
+                    break
+                new_products.append(products[j])
+            products = new_products
+            result.append(products[:3])
+        return result