similarity

I have many files of a same picture in various resolution, suitable for every devices like mobile, pc, psp etc. Now I am trying to display only unique pictures in the page, but I dont know how to. I could have avoided this if I maintained a database at the first place, but I didn't. And I need your help detecting the largest unique pictures. Well, even thou there are quite a few algorithms to do that, i believe it would still be faster to do that manually. Download all the images feed them into something like windows live photo gallery or any other software which could match similar images.

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: Found this gist using numba for fast computation of cosine similarity. import numba @numba.jit(target='cpu', nopython=True) def fast_cosine(u, v): m = u.shape[0] udotv = 0 u_norm = 0 v_norm = 0 for i in range(m): if (np.isnan(u[i])) or (np.isnan(v[i])): continue udotv += u[i] * v[i] u_norm += u[i] * u[i] v_norm += v[i] * v[i] u_norm = np.sqrt(u_norm) v_norm = np.sqrt(v_norm) if (u_norm == 0) or (v_norm == 0): ratio = 1.0 else: ratio = udotv / (u_norm * v_norm) return ratio Results look promising (500ns vs. only 200us without jit decorator in

I need to find a similarity measurement between two arrays of data. You can call similarity measurement whatever you want, difference, correlation or whatever. For example: 1, 2, 3, 4, 5 < Series 1 2, 3, 4, 5, 6 < Series 2 Should be far more similar to each other than these 2 series: 1, 2, 3, 4, 5 < Series 1 1, 1, 5, 8, 7 < Series 2 Any suggestions? Is there a source code available for it? You can calculate the sample Pearson product-moment correlation coefficient : "The above formula suggests a convenient single-pass algorithm for calculating sample correlations". Write a loop to calculate

I'm trying to come up with a method of finding duplicate addresses, based on a similarity score. Consider these duplicate addresses: addr_1 = '# 3 FAIRMONT LINK SOUTH' addr_2 = '3 FAIRMONT LINK S' addr_3 = '5703 - 48TH AVE' adrr_4 = '5703- 48 AVENUE' I'm planning on applying some string transformation to make long words abbreviated, like NORTH -> N, remove all spaces, commas and dashes and pound symbols. Now, having this output, how can I compare addr_3 with the rest of addresses and detect similar? What percentage of similarity would be safe? Could you provide a simple python code for this?

my topic is similarity and clustering of (a bunch of) text(s). In a nutshell: I want to cluster collected texts together and they should appear in meaningful clusters at the end. To do this, my approach up to now is as follows, my problem is in the clustering. The current software is written in php. 1) Similarity: I treat every document as a "bag-of-words" and convert words into vectors. I use filtering (only "real" words) tokenization (split sentences into words) stemming (reduce words to their base form; Porter's stemmer) pruning (cut of words with too high & low frequency) as methods for

I am trying to calculate semantic similarity between two words. I am using Wordnet-based similarity measures i.e Resnik measure(RES), Lin measure(LIN), Jiang and Conrath measure(JNC) and Banerjee and Pederson measure(BNP). To do that, I am using nltk and Wordnet 3.0. Next, I want to combine the similarity values obtained from different measure. To do that i need to normalize the similarity values as some measure give values between 0 and 1, while others give values greater than 1. So, my question is how do I normalize the similarity values obtained from different measures. Extra detail on what

I'm trying to use TF-IDF to sort documents into categories. I've calculated the tf_idf for some documents, but now when I try to calculate the Cosine Similarity between two of these documents I get a traceback saying: #len(u)==201, len(v)==246 cosine_distance(u, v) ValueError: objects are not aligned #this works though: cosine_distance(u[:200], v[:200]) >> 0.52230249969265641 Is slicing the vector so that len(u)==len(v) the right approach? I would think that cosine similarity would work with vectors of different lengths. I'm using this function : def cosine_distance(u, v): """ Returns the