outfits

./outfits \

data/productMeta.txt.gz \

data/duplicate_list.txt.gz \

data/image_features_Baby.b \

models/Baby-also_viewed-10-0.000000.txt \

data/urlPath_Baby.txt

1. 5 input params:

categoryPath, duplicatePath, imFeaturePath, modelPath, urlPath

2. load data

  corpus corp("null", // review path

              argv + 6, // graph paths

              categoryPath, // path to category data

              duplicatePath, // path to duplicate ASIN listing

              imFeaturePath, // path to image features

              G, // number of graphs

              productsPerTopic,

              0); // max number of reviews to read (0 for all)

3. load models  

  int NW;

  double* W = loadModel(modelPath, &NW);

3.1    if (startsWith(lb, "  \"NW\""))

  int K = NW / 4096;

  lrMahalanobis ec(&corp, 0, 0, 0, K);

  ec.init();

4. load urls

  map<string, string>* urls = loadUrls(urlPath);

5. queriesBaby

else if (string(imFeaturePath).find("Baby") != string::npos)

  {

    printf("Running queries for Baby's products\n");

    int NQ = 1000;

    queriesBaby(&ec, W, urls, NQ);

  }

5.1 queriesFromCategories(ec, W, categoriesS, urls, NQ);

5.1.1   double** Uc = projectFeatures(ec, W);

5.1.2   vector<int> outfit = queriesProductCategory(ec, Uc, item, categories, &categoryVecs);

5.1.2.1 res.push_back(query(ec, Uc, item, *it, categoryVecs));

5.1.2.1.1    double d = dist(Uc[qItem], Uc[item], ec->K); // Find the distance between the query