Implementation of cosine similarity based on the user's recommendation algorithm-PHP source code

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1. [File]Cosine. py

#-*-Coding: UTF-8-*-''' Created on 2012-9-3 @ author: Jekey cosine correlation. if the data is sparse, consider using this algorithm '''import codecsfrom math import sqrtusers = {"Angelica": {"Blues Traveler": 3.5, "Broken Bells": 2.0, "Norah Jones": 4.5, "Phoenix": 5.0, "Slightly Stoopid": 1.5, "The Strokes": 2.5, "Vampire Weekend": 2.0}, "Bill": {"Blues Traveler": 2.0, "Broken Bells": 3.5, "Deadmau5": 4.0, "Phoenix": 2.0, "Slightly Stoopid": 3.5, "Vampire Weekend": 3.0}, "Chan ": {"Blues Traveler": 5.0, "Broken Bells": 1.0, "Deadmau5": 1.0, "Norah Jones": 3.0, "Phoenix": 5, "Slightly Stoopid ": 1.0}, "Dan": {"Blues Traveler": 3.0, "Broken Bells": 4.0, "Deadmau5": 4.5, "Phoenix": 3.0, "Slightly Stoopid ": 4.5, "The Strokes": 4.0, "Vampire Weekend": 2.0}, "Hailey": {"Broken Bells": 4.0, "Deadmau5": 1.0, "Norah Jones ": 4.0, "The Strokes": 4.0, "Vampire Weekend": 1.0}, "Jordyn": {"Broken Bells": 4.5, "Deadmau5": 4.0, "Norah Jones ": 5.0, "Phoenix": 5.0, "Slightly Stoopid": 4.5, "The Strokes": 4.0, "Vampire Weekend": 4.0}, "Sam": {"Blues Traveler ": 5.0, "Broken Bells": 2.0, "Norah Jones": 3.0, "Phoenix": 5.0, "Slightly Stoopid": 4.0, "The Strokes": 5.0 }, "Veronica": {"Blues Traveler": 3.0, "Norah Jones": 5.0, "Phoenix": 4.0, "Slightly Stoopid": 2.5, "The Strokes ": 3.0 }}# cosine distance from def cosine (rate1, rate2): sum_xy = 0 sum_x = 0 sum_y = 0 n = 0 for key in rate1: if key in rate2: n + = 1 x = rate1 [key] y = rate2 [key] sum_xy + = x * y sum_x + = x * x sum_y + = y * y # calculate the distance from if n = = 0: return 0 else: sx = pow (sum_x, 1/2) sy = pow (sum_y, 1/2) if sum_xy <> 0: denominator = sx * sy/sum_xy else: denominator = 0 return denominator # return the closest user def computeNearestNeighbor (username, users): distances = [] for key in users: if key <> username: distance = cosine (users [username], users [key]) distances. append (distance, key) distances. sort () return distances # recommended def recommend (username, users): # obtain the name nearest = computeNearestNeighbor (username, users) of the nearest user) [0] [1] recommendations = [] # obtain the neighborRatings = users [nearest] for key in neighborRatings: if not key in users [username]: recommendations. append (key, neighborRatings [key]) recommendations. sort (key = lambda rat: rat [1], reverse = True) return recommendations if _ name _ = '_ main __': print recommend ('haily', users)