test

Former-commit-id: e6fb8d39c0

README.md

@@ -3,6 +3,7 @@
 
 This software package contains a Barnes-Hut implementation of the t-SNE algorithm. The implementation is described in [this paper](http://lvdmaaten.github.io/publications/papers/JMLR_2014.pdf).
 
+**Note:** This repository is a version of t-SNE modified to support ongoing research. It may be slightly slower than the original. If you're just trying to run t-SNE, check the original repository that we forked from.
 
 # Installation #
 

bhtsne.py

@@ -64,7 +64,7 @@ INITIAL_DIMENSIONS = 50
 DEFAULT_PERPLEXITY = 50
 DEFAULT_THETA = 0.5
 EMPTY_SEED = -1
-DEFAULT_USE_PCA = False
+DEFAULT_USE_PCA = True
 DEFAULT_MAX_ITERATIONS = 1000
 
 ###
@@ -102,7 +102,8 @@ def _is_filelike_object(f):
         return isinstance(f, io.IOBase)
 
 
-def init_bh_tsne(samples, workdir, no_dims, initial_dims, perplexity, theta, randseed, verbose, use_pca, max_iter):
+def init_bh_tsne(samples, workdir, no_dims=DEFAULT_NO_DIMS, initial_dims=INITIAL_DIMENSIONS, perplexity=DEFAULT_PERPLEXITY,
+            theta=DEFAULT_THETA, randseed=EMPTY_SEED, verbose=False, use_pca=DEFAULT_USE_PCA, max_iter=DEFAULT_MAX_ITERATIONS):
 
     if use_pca:
         samples = samples - np.mean(samples, axis=0)
@@ -172,7 +173,9 @@ def bh_tsne(workdir, verbose=False):
         # The last piece of data is the cost for each sample, we ignore it
         #read_unpack('{}d'.format(sample_count), output_file)
 
-def run_bh_tsne(data, no_dims=2, perplexity=50, theta=0.5, randseed=-1, verbose=False, initial_dims=50, use_pca=True, max_iter=1000):
+def run_bh_tsne(data, no_dims=2, perplexity=50, theta=0.5, randseed=-1,
+        verbose=False, initial_dims=50, use_pca=True, max_iter=1000,
+        return_betas=False, return_cost_per_point=False, return_cost_per_iter=False):
     '''
     Run TSNE based on the Barnes-HT algorithm
 
@@ -200,8 +203,8 @@ def run_bh_tsne(data, no_dims=2, perplexity=50, theta=0.5, randseed=-1, verbose=
         if _is_filelike_object(data):
             data = load_data(data)
 
-        init_bh_tsne(data, tmp_dir_path, no_dims, perplexity, theta, randseed, verbose, initial_dims, use_pca, max_iter)
+        init_bh_tsne(data, tmp_dir_path, no_dims=no_dims, perplexity=perplexity, theta=theta, randseed=randseed,verbose=verbose, initial_dims=initial_dims, use_pca=use_pca, max_iter=max_iter)
-        sys.exit(0)
+        os._exit(0)
     else:
         try:
             os.waitpid(child_pid, 0)
@@ -215,8 +218,23 @@ def run_bh_tsne(data, no_dims=2, perplexity=50, theta=0.5, randseed=-1, verbose=
             for r in result:
                 sample_res.append(r)
             res.append(sample_res)
+
+        ret = np.asarray(res, dtype='float64')
+
+        if return_betas:
+            betas = np.loadtxt(path_join(tmp_dir_path, 'betas.txt'))
+            ret = (ret, betas)
+
+        if return_cost_per_point:
+            cpp = np.loadtxt(path_join(tmp_dir_path, 'cost_per_point.txt'))
+            ret = (*ret, cpp)
+
+        if return_cost_per_iter:
+            cpi = np.loadtxt(path_join(tmp_dir_path, 'cost_per_iter.txt'))
+            ret = (*ret, cpi)
+
         rmtree(tmp_dir_path)
-        return np.asarray(res, dtype='float64')
+        return ret
 
 
 def main(args):

cachedir/joblib/tsneGrid/wrapGetResults/0c12479eefa6b83c513d9426678bd6c5/metadata.json

@@ -0,0 +1 @@
+{"duration": 0.8360249996185303, "input_args": {"listofParamsPlusData": "[(array([[5.1, 3.5, 1.4, 0.2],\n       [4.9, 3. , 1.4, 0.2],\n       [4.7, 3.2, 1.3, 0.2],\n       [4.6, 3.1, 1.5, 0.2],\n       [5. , 3.6, 1.4, 0.2],\n       [5.4, 3.9, 1.7, 0.4],\n       [4.6, 3.4, 1.4, 0.3],\n       [5. , 3.4, 1.5, 0.2],\n       [4.4, 2.9, 1.4, 0.2],\n       [4.9, 3.1, 1.5, 0.1],\n       [5.4, 3.7, 1.5, 0.2],\n       [4.8, 3.4, 1.6, 0.2],\n       [4.8, 3. , 1.4, 0.1],\n       [4.3, 3. , 1.1, 0.1],\n       [5.8, 4. , 1.2, 0.2],\n       [5.7, 4.4, 1.5, 0.4],\n       [5.4, 3.9, 1.3, 0.4],\n       [5.1, 3.5, 1.4, 0.3],\n       [5.7, 3.8, 1.7, 0.3],\n       [5.1, 3.8, 1.5, 0.3],\n       [5.4, 3.4, 1.7, 0.2],\n       [5.1, 3.7, 1.5, 0.4],\n       [4.6, 3.6, 1. , 0.2],\n       [5.1, 3.3, 1.7, 0.5],\n       [4.8, 3.4, 1.9, 0.2],\n       [5. , 3. , 1.6, 0.2],\n       [5. , 3.4, 1.6, 0.4],\n       [5.2, 3.5, 1.5, 0.2],\n       [5.2, 3.4, 1.4, 0.2],\n       [4.7, 3.2, 1.6, 0.2],\n       [4.8, 3.1, 1.6, 0.2],\n       [5.4, 3.4, 1.5, 0.4],\n       [5.2, 4.1, 1.5, 0.1],\n       [5.5, 4.2, 1.4, 0.2],\n       [4.9, 3.1, 1.5, 0.1],\n       [5. , 3.2, 1.2, 0.2],\n       [5.5, 3.5, 1.3, 0.2],\n       [4.9, 3.1, 1.5, 0.1],\n       [4.4, 3. , 1.3, 0.2],\n       [5.1, 3.4, 1.5, 0.2],\n       [5. , 3.5, 1.3, 0.3],\n       [4.5, 2.3, 1.3, 0.3],\n       [4.4, 3.2, 1.3, 0.2],\n       [5. , 3.5, 1.6, 0.6],\n       [5.1, 3.8, 1.9, 0.4],\n       [4.8, 3. , 1.4, 0.3],\n       [5.1, 3.8, 1.6, 0.2],\n       [4.6, 3.2, 1.4, 0.2],\n       [5.3, 3.7, 1.5, 0.2],\n       [5. , 3.3, 1.4, 0.2],\n       [7. , 3.2, 4.7, 1.4],\n       [6.4, 3.2, 4.5, 1.5],\n       [6.9, 3.1, 4.9, 1.5],\n       [5.5, 2.3, 4. , 1.3],\n       [6.5, 2.8, 4.6, 1.5],\n       [5.7, 2.8, 4.5, 1.3],\n       [6.3, 3.3, 4.7, 1.6],\n       [4.9, 2.4, 3.3, 1. ],\n       [6.6, 2.9, 4.6, 1.3],\n       [5.2, 2.7, 3.9, 1.4],\n       [5. , 2. , 3.5, 1. ],\n       [5.9, 3. , 4.2, 1.5],\n       [6. , 2.2, 4. , 1. ],\n       [6.1, 2.9, 4.7, 1.4],\n       [5.6, 2.9, 3.6, 1.3],\n       [6.7, 3.1, 4.4, 1.4],\n       [5.6, 3. , 4.5, 1.5],\n       [5.8, 2.7, 4.1, 1. ],\n       [6.2, 2.2, 4.5, 1.5],\n       [5.6, 2.5, 3.9, 1.1],\n       [5.9, 3.2, 4.8, 1.8],\n       [6.1, 2.8, 4. , 1.3],\n       [6.3, 2.5, 4.9, 1.5],\n       [6.1, 2.8, 4.7, 1.2],\n       [6.4, 2.9, 4.3, 1.3],\n       [6.6, 3. , 4.4, 1.4],\n       [6.8, 2.8, 4.8, 1.4],\n       [6.7, 3. , 5. , 1.7],\n       [6. , 2.9, 4.5, 1.5],\n       [5.7, 2.6, 3.5, 1. ],\n       [5.5, 2.4, 3.8, 1.1],\n       [5.5, 2.4, 3.7, 1. ],\n       [5.8, 2.7, 3.9, 1.2],\n       [6. , 2.7, 5.1, 1.6],\n       [5.4, 3. , 4.5, 1.5],\n       [6. , 3.4, 4.5, 1.6],\n       [6.7, 3.1, 4.7, 1.5],\n       [6.3, 2.3, 4.4, 1.3],\n       [5.6, 3. , 4.1, 1.3],\n       [5.5, 2.5, 4. , 1.3],\n       [5.5, 2.6, 4.4, 1.2],\n       [6.1, 3. , 4.6, 1.4],\n       [5.8, 2.6, 4. , 1.2],\n       [5. , 2.3, 3.3, 1. ],\n       [5.6, 2.7, 4.2, 1.3],\n       [5.7, 3. , 4.2, 1.2],\n       [5.7, 2.9, 4.2, 1.3],\n       [6.2, 2.9, 4.3, 1.3],\n       [5.1, 2.5, 3. , 1.1],\n       [5.7, 2.8, 4.1, 1.3],\n       [6.3, 3.3, 6. , 2.5],\n       [5.8, 2.7, 5.1, 1.9],\n       [7.1, 3. , 5.9, 2.1],\n       [6.3, 2.9, 5.6, 1.8],\n       [6.5, 3. , 5.8, 2.2],\n       [7.6, 3. , 6.6, 2.1],\n       [4.9, 2.5, 4.5, 1.7],\n       [7.3, 2.9, 6.3, 1.8],\n       [6.7, 2.5, 5.8, 1.8],\n       [7.2, 3.6, 6.1, 2.5],\n       [6.5, 3.2, 5.1, 2. ],\n       [6.4, 2.7, 5.3, 1.9],\n       [6.8, 3. , 5.5, 2.1],\n       [5.7, 2.5, 5. , 2. ],\n       [5.8, 2.8, 5.1, 2.4],\n       [6.4, 3.2, 5.3, 2.3],\n       [6.5, 3. , 5.5, 1.8],\n       [7.7, 3.8, 6.7, 2.2],\n       [7.7, 2.6, 6.9, 2.3],\n       [6. , 2.2, 5. , 1.5],\n       [6.9, 3.2, 5.7, 2.3],\n       [5.6, 2.8, 4.9, 2. ],\n       [7.7, 2.8, 6.7, 2. ],\n       [6.3, 2.7, 4.9, 1.8],\n       [6.7, 3.3, 5.7, 2.1],\n       [7.2, 3.2, 6. , 1.8],\n       [6.2, 2.8, 4.8, 1.8],\n       [6.1, 3. , 4.9, 1.8],\n       [6.4, 2.8, 5.6, 2.1],\n       [7.2, 3. , 5.8, 1.6],\n       [7.4, 2.8, 6.1, 1.9],\n       [7.9, 3.8, 6.4, 2. ],\n       [6.4, 2.8, 5.6, 2.2],\n       [6.3, 2.8, 5.1, 1.5],\n       [6.1, 2.6, 5.6, 1.4],\n       [7.7, 3. , 6.1, 2.3],\n       [6.3, 3.4, 5.6, 2.4],\n       [6.4, 3.1, 5.5, 1.8],\n       [6. , 3. , 4.8, 1.8],\n       [6.9, 3.1, 5.4, 2.1],\n       [6.7, 3.1, 5.6, 2.4],\n       [6.9, 3.1, 5.1, 2.3],\n       [5.8, 2.7, 5.1, 1.9],\n       [6.8, 3.2, 5.9, 2.3],\n       [6.7, 3.3, 5.7, 2.5],\n       [6.7, 3. , 5.2, 2.3],\n       [6.3, 2.5, 5. , 1.9],\n       [6.5, 3. , 5.2, 2. ],\n       [6.2, 3.4, 5.4, 2.3],\n       [5.9, 3. , 5.1, 1.8]]), 2, 20, 1, 1137, False, 4, True, 500, True, True, True)]"}}

cachedir/joblib/tsneGrid/wrapGetResults/0ef8bace2617cb12410b09593d5ce22c/metadata.json

@@ -0,0 +1 @@
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cachedir/joblib/tsneGrid/wrapGetResults/1ddefb98fa182253eab204d4754b98f6/metadata.json

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cachedir/joblib/tsneGrid/wrapGetResults/205d8b2fca4a45add9abe01d08dd883b/metadata.json

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cachedir/joblib/tsneGrid/wrapGetResults/323e1aabcf84763833a281b1f709acab/metadata.json

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cachedir/joblib/tsneGrid/wrapGetResults/41f6a961027cd56fd1d3e9c6aeb61d19/metadata.json

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cachedir/joblib/tsneGrid/wrapGetResults/4a51497f39535226bcb6004a9e504fc4/metadata.json

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cachedir/joblib/tsneGrid/wrapGetResults/6bc93cade94d918a4730ee6065d2b2ab/metadata.json

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cachedir/joblib/tsneGrid/wrapGetResults/744444ae1d1e91db785c5dc3f99e52d0/metadata.json

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cachedir/joblib/tsneGrid/wrapGetResults/7c31520b589820031fb034d593ca82c7/metadata.json

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cachedir/joblib/tsneGrid/wrapGetResults/7e8da8329c985f22bbe4c729b0408dc4/metadata.json

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cachedir/joblib/tsneGrid/wrapGetResults/80422bba65b43c68417b12b82620818f/metadata.json

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cachedir/joblib/tsneGrid/wrapGetResults/94d953b4276b5a631e6ff1576179d473/metadata.json

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cachedir/joblib/tsneGrid/wrapGetResults/9add6660fb41c6c51eaf5aa6ea656b83/metadata.json

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cachedir/joblib/tsneGrid/wrapGetResults/a1a276ff6f0f454d29cb3ab6d0f231a7/metadata.json

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cachedir/joblib/tsneGrid/wrapGetResults/b47197b4ad1d2557f0c4671d020bae90/metadata.json

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cachedir/joblib/tsneGrid/wrapGetResults/cabe82c787215a6d9b2831382e7a5c7d/metadata.json

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cachedir/joblib/tsneGrid/wrapGetResults/ccca277f0d9b005ee1be994805cc09db/metadata.json

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cachedir/joblib/tsneGrid/wrapGetResults/d350d460d4ccaf5b7bb759623976a40a/metadata.json

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cachedir/joblib/tsneGrid/wrapGetResults/e99f60c71f5ec695eadc81606983d0ad/metadata.json

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cachedir/joblib/tsneGrid/wrapGetResults/ed3c939610a6e7dd8cb1aee724872b4a/metadata.json

@@ -0,0 +1 @@
+{"duration": 0.7937450408935547, "input_args": {"listofParamsPlusData": "[(array([[5.1, 3.5, 1.4, 0.2],\n       [4.9, 3. , 1.4, 0.2],\n       [4.7, 3.2, 1.3, 0.2],\n       [4.6, 3.1, 1.5, 0.2],\n       [5. , 3.6, 1.4, 0.2],\n       [5.4, 3.9, 1.7, 0.4],\n       [4.6, 3.4, 1.4, 0.3],\n       [5. , 3.4, 1.5, 0.2],\n       [4.4, 2.9, 1.4, 0.2],\n       [4.9, 3.1, 1.5, 0.1],\n       [5.4, 3.7, 1.5, 0.2],\n       [4.8, 3.4, 1.6, 0.2],\n       [4.8, 3. , 1.4, 0.1],\n       [4.3, 3. , 1.1, 0.1],\n       [5.8, 4. , 1.2, 0.2],\n       [5.7, 4.4, 1.5, 0.4],\n       [5.4, 3.9, 1.3, 0.4],\n       [5.1, 3.5, 1.4, 0.3],\n       [5.7, 3.8, 1.7, 0.3],\n       [5.1, 3.8, 1.5, 0.3],\n       [5.4, 3.4, 1.7, 0.2],\n       [5.1, 3.7, 1.5, 0.4],\n       [4.6, 3.6, 1. , 0.2],\n       [5.1, 3.3, 1.7, 0.5],\n       [4.8, 3.4, 1.9, 0.2],\n       [5. , 3. , 1.6, 0.2],\n       [5. , 3.4, 1.6, 0.4],\n       [5.2, 3.5, 1.5, 0.2],\n       [5.2, 3.4, 1.4, 0.2],\n       [4.7, 3.2, 1.6, 0.2],\n       [4.8, 3.1, 1.6, 0.2],\n       [5.4, 3.4, 1.5, 0.4],\n       [5.2, 4.1, 1.5, 0.1],\n       [5.5, 4.2, 1.4, 0.2],\n       [4.9, 3.1, 1.5, 0.1],\n       [5. , 3.2, 1.2, 0.2],\n       [5.5, 3.5, 1.3, 0.2],\n       [4.9, 3.1, 1.5, 0.1],\n       [4.4, 3. , 1.3, 0.2],\n       [5.1, 3.4, 1.5, 0.2],\n       [5. , 3.5, 1.3, 0.3],\n       [4.5, 2.3, 1.3, 0.3],\n       [4.4, 3.2, 1.3, 0.2],\n       [5. , 3.5, 1.6, 0.6],\n       [5.1, 3.8, 1.9, 0.4],\n       [4.8, 3. , 1.4, 0.3],\n       [5.1, 3.8, 1.6, 0.2],\n       [4.6, 3.2, 1.4, 0.2],\n       [5.3, 3.7, 1.5, 0.2],\n       [5. , 3.3, 1.4, 0.2],\n       [7. , 3.2, 4.7, 1.4],\n       [6.4, 3.2, 4.5, 1.5],\n       [6.9, 3.1, 4.9, 1.5],\n       [5.5, 2.3, 4. , 1.3],\n       [6.5, 2.8, 4.6, 1.5],\n       [5.7, 2.8, 4.5, 1.3],\n       [6.3, 3.3, 4.7, 1.6],\n       [4.9, 2.4, 3.3, 1. ],\n       [6.6, 2.9, 4.6, 1.3],\n       [5.2, 2.7, 3.9, 1.4],\n       [5. , 2. , 3.5, 1. ],\n       [5.9, 3. , 4.2, 1.5],\n       [6. , 2.2, 4. , 1. ],\n       [6.1, 2.9, 4.7, 1.4],\n       [5.6, 2.9, 3.6, 1.3],\n       [6.7, 3.1, 4.4, 1.4],\n       [5.6, 3. , 4.5, 1.5],\n       [5.8, 2.7, 4.1, 1. ],\n       [6.2, 2.2, 4.5, 1.5],\n       [5.6, 2.5, 3.9, 1.1],\n       [5.9, 3.2, 4.8, 1.8],\n       [6.1, 2.8, 4. , 1.3],\n       [6.3, 2.5, 4.9, 1.5],\n       [6.1, 2.8, 4.7, 1.2],\n       [6.4, 2.9, 4.3, 1.3],\n       [6.6, 3. , 4.4, 1.4],\n       [6.8, 2.8, 4.8, 1.4],\n       [6.7, 3. , 5. , 1.7],\n       [6. , 2.9, 4.5, 1.5],\n       [5.7, 2.6, 3.5, 1. ],\n       [5.5, 2.4, 3.8, 1.1],\n       [5.5, 2.4, 3.7, 1. ],\n       [5.8, 2.7, 3.9, 1.2],\n       [6. , 2.7, 5.1, 1.6],\n       [5.4, 3. , 4.5, 1.5],\n       [6. , 3.4, 4.5, 1.6],\n       [6.7, 3.1, 4.7, 1.5],\n       [6.3, 2.3, 4.4, 1.3],\n       [5.6, 3. , 4.1, 1.3],\n       [5.5, 2.5, 4. , 1.3],\n       [5.5, 2.6, 4.4, 1.2],\n       [6.1, 3. , 4.6, 1.4],\n       [5.8, 2.6, 4. , 1.2],\n       [5. , 2.3, 3.3, 1. ],\n       [5.6, 2.7, 4.2, 1.3],\n       [5.7, 3. , 4.2, 1.2],\n       [5.7, 2.9, 4.2, 1.3],\n       [6.2, 2.9, 4.3, 1.3],\n       [5.1, 2.5, 3. , 1.1],\n       [5.7, 2.8, 4.1, 1.3],\n       [6.3, 3.3, 6. , 2.5],\n       [5.8, 2.7, 5.1, 1.9],\n       [7.1, 3. , 5.9, 2.1],\n       [6.3, 2.9, 5.6, 1.8],\n       [6.5, 3. , 5.8, 2.2],\n       [7.6, 3. , 6.6, 2.1],\n       [4.9, 2.5, 4.5, 1.7],\n       [7.3, 2.9, 6.3, 1.8],\n       [6.7, 2.5, 5.8, 1.8],\n       [7.2, 3.6, 6.1, 2.5],\n       [6.5, 3.2, 5.1, 2. ],\n       [6.4, 2.7, 5.3, 1.9],\n       [6.8, 3. , 5.5, 2.1],\n       [5.7, 2.5, 5. , 2. ],\n       [5.8, 2.8, 5.1, 2.4],\n       [6.4, 3.2, 5.3, 2.3],\n       [6.5, 3. , 5.5, 1.8],\n       [7.7, 3.8, 6.7, 2.2],\n       [7.7, 2.6, 6.9, 2.3],\n       [6. , 2.2, 5. , 1.5],\n       [6.9, 3.2, 5.7, 2.3],\n       [5.6, 2.8, 4.9, 2. ],\n       [7.7, 2.8, 6.7, 2. ],\n       [6.3, 2.7, 4.9, 1.8],\n       [6.7, 3.3, 5.7, 2.1],\n       [7.2, 3.2, 6. , 1.8],\n       [6.2, 2.8, 4.8, 1.8],\n       [6.1, 3. , 4.9, 1.8],\n       [6.4, 2.8, 5.6, 2.1],\n       [7.2, 3. , 5.8, 1.6],\n       [7.4, 2.8, 6.1, 1.9],\n       [7.9, 3.8, 6.4, 2. ],\n       [6.4, 2.8, 5.6, 2.2],\n       [6.3, 2.8, 5.1, 1.5],\n       [6.1, 2.6, 5.6, 1.4],\n       [7.7, 3. , 6.1, 2.3],\n       [6.3, 3.4, 5.6, 2.4],\n       [6.4, 3.1, 5.5, 1.8],\n       [6. , 3. , 4.8, 1.8],\n       [6.9, 3.1, 5.4, 2.1],\n       [6.7, 3.1, 5.6, 2.4],\n       [6.9, 3.1, 5.1, 2.3],\n       [5.8, 2.7, 5.1, 1.9],\n       [6.8, 3.2, 5.9, 2.3],\n       [6.7, 3.3, 5.7, 2.5],\n       [6.7, 3. , 5.2, 2.3],\n       [6.3, 2.5, 5. , 1.9],\n       [6.5, 3. , 5.2, 2. ],\n       [6.2, 3.4, 5.4, 2.3],\n       [5.9, 3. , 5.1, 1.8]]), 2, 30, 10, 1137, False, 4, True, 500, True, True, True)]"}}

cachedir/joblib/tsneGrid/wrapGetResults/fe7e0f41ce54d6580987f815a89fad6f/metadata.json

@@ -0,0 +1 @@
+{"duration": 8.928506851196289, "input_args": {"listofParamsPlusData": "[(array([[ 5,  1,  1, ...,  3,  1,  1],\n       [ 5,  4,  4, ...,  3,  2,  1],\n       [ 3,  1,  1, ...,  3,  1,  1],\n       ...,\n       [ 5, 10, 10, ...,  8, 10,  2],\n       [ 4,  8,  6, ..., 10,  6,  1],\n       [ 4,  8,  8, ..., 10,  4,  1]]), 2, 30, 10, 1137, False, 9, True, 500, True, True, True)]"}}

cachedir/joblib/tsneGrid/wrapGetResults/func_code.py

@@ -1,5 +1,5 @@
-# first line: 198
+# first line: 217
 def wrapGetResults(listofParamsPlusData):
     pool = Pool()
     
-    return pool.map(multi_run_wrapper, listofParamsPlusData)
+    return zip(*pool.map(multi_run_wrapper, listofParamsPlusData))

css/style.css

@@ -227,7 +227,7 @@ circle.swatch{
 /* Legend of the Overview t-SNE canvas */
 #legend4 {
 height: 7vw;
-width: 5vw;
+width: 7vw;
 margin-left: -20px;
 margin-top: -10px;
 text-align: left;
@@ -512,7 +512,7 @@ ul {
   height: 140px;
   -webkit-animation: spin 2s linear infinite; /* Safari */
   animation: spin 2s linear infinite;
-  margin-left: 540px;
+  margin-left: 450px;
   margin-top: 500px;
   margin-bottom: 500px;
 }

index.html

@@ -104,12 +104,9 @@
                           </div>
                           <div class="param">
                         <label for="param-learningrate" data-toggle="tooltip" data-placement="right" title="Tip: if the learning rate is too high, the data may look like a ‘ball’ with any point approximately equidistant from its nearest neighbours. If the learning rate is too low, most points may look compressed in a dense cloud with few outliers. If the cost function gets stuck in a bad local minimum increasing the learning rate may help. (Source: https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html)">Learning rate</label>
-                        <input id="param-learningrate" type="range" min="1" max="150" value="10", step="1">
+                        <input id="param-learningrate" type="range" min="1" max="150" value="1", step="1">
-                        <output for="param-learningrate" id="param-learningrate-value">10</output>
+                        <output for="param-learningrate" id="param-learningrate-value">1</output>
                           </div>                          
-
-
-                          
                           <div class="param">
                               <div class="row">
                               <div class="col-md-3">
@@ -129,36 +126,23 @@
 
                           </div>
                           </div>
-                          <div class="param">
+                          <div class="param" style="margin-top: -10px">
-                        <label for="param-distance" data-toggle="tooltip" data-placement="right" title="Tip: distance different measurements. Usually Euclidean distance is used.">Distance metric</label>
-                        <select id="param-distance" name="param-distance">
-                            <option value="euclideanDist" selected>Euclidean distance</option>
-                            <option value="jaccardDist">Jaccard dissimilarity</option>
-                        </select>
                         <input id="file-input" type="file" name="name" style="display: none;" />
-                        <button type="button" class="button" onclick='loadAnalysis();' style="padding:0 12px 0 12px" data-toggle="tooltip" data-placement="right" title="Tip: load previously executed file in .txt format.">Load execut.</button>
+                        <button type="button" class="button" onclick='loadAnalysis();' style="padding:15px 12px 15px 12px; margin-left:15px" data-toggle="tooltip" data-placement="right" title="Tip: load previously executed file in .txt format.">Load execution</button>
-                          </div>
+                        <button type="button" class="button" onclick="SaveAnalysis()" style="padding:15px 12px 15px 12px; margin-left:20px" data-toggle="tooltip" data-placement="right" title="Tip: save/store previously executed file in .txt format.">Store execution</button>
-                          <div class="param">
+                        <div class="col-md-" style="margin-top: 14px; margin-left:15px">
-                        <label for="param-transform" data-toggle="tooltip" data-placement="right" title="Tip: distance transformation measurements. Usually either no transformation or Log10/Asinh (logarithmic/hyperbolic) distance are used.">Data transform</label>
-                        <select id="param-transform" name="param-transform">
-                            <option value="noTrans" selected>No transform</option>
-                            <option value="logTrans">Log10</option>
-                            <option value="asinhTrans">Asinh</option>
-                            <option value="binTrans">Binarize</option>
-                        </select>
-                        <button type="button" class="button" onclick="SaveAnalysis()" style="padding:0 10px 0 10px" data-toggle="tooltip" data-placement="right" title="Tip: save/store previously executed file in .txt format.">Store execut.</button>
-                          </div>
-                        <div class="row">
-                          <div class="col-md-8">
-                              <p><div id="run-button"><button id="ExecuteBut" class="btn btn-primary btn-block" onclick="getData();" value="Execute new t-SNE analysis" style="margin-top: 4px"><i class="fas fa-running fa-lg" style="margin-right: 10px"></i>Execute new t-SNE analysis</button></div></p>
-                          </div>
-                          <div class="col-md-4" style="margin-top: 10px">
                             <label data-toggle="tooltip" data-placement="right" title="Tip: if you store distances the file size will be larger but on a loading of this execution it will be processed much quicker than without this option enabled.">
                                 <input id="downloadDists" checked type="checkbox" >
                                 Distances cached on "Store execution"
                             </label>
                           </div>
                         </div>
+                        <div class="row">
+                          <div class="col-md-12" style="margin-top:10px">
+                              <p><div id="run-button"><button id="ExecuteBut" class="btn btn-primary btn-block" onclick="getData();" value="Execute new t-SNE analysis" style="margin-top: 4px"><i class="fas fa-running fa-lg" style="margin-right: 10px"></i>Execute new t-SNE analysis</button></div></p>
+                          </div>
+
+                        </div>
                     </div>
                   </div>
                 </div>
@@ -291,7 +275,7 @@
                             <svg id="legend1" style =  "margin-left: -25px"></svg>
                           </div>
                           <div class="col-md-2">
-                            <svg id="legend4" style =  "margin-left: -45px"></svg>
+                            <svg id="legend4" style =  "margin-left: -50px"></svg>
                           </div>
                           </div>
                           <div class="annotationAllClass" style="margin-top: 20px">
@@ -318,7 +302,7 @@
                   </div>
         <div class="row">   
             <div class="col-md-3"  style="margin-top:-4px">
-                <div class="panel panel-default" style="padding-bottom : 60px">
+                <div class="panel panel-default" style="padding-bottom : 70px">
                   <div class="panel-heading">
                     <h2 class="panel-title" data-toggle="tooltip" data-placement="right" style="display:inline-block" title="Tip: a view related to the overall quality of the projection.">Shepard Heatmap</h2>
                     <div style="display:inline-block; float:right">
@@ -423,6 +407,7 @@
       $("#ExecuteBut").click(function(){
           var mode = document.getElementById('param-EX-view').value
           mode = parseInt(mode)
+
           if (mode == 1) {
             $("#myModal").modal('show');
             $('.modal-backdrop').removeClass("modal-backdrop");  
@@ -434,7 +419,7 @@
         $('#myModal').modal('hide');
       }
 
-      $("#cost").html('(Unknown Iteration and Cost Values)');
+      $("#cost").html('(Unknown Overall Cost)');
       $("#datasetDetails").html('(Unknown Number of Dimensions and Instances)');
       $("#CategoryName").html('No Classification');
       $("#knnBarChartDetails").html('(Number of Selected Points: 0/0)');

js/tsne_vis.js

@@ -19,7 +19,7 @@ var dim = document.getElementById('overviewRect').offsetWidth-2; var dimensions
 
 // Category = the name of the category if it exists. The user has to add an asterisk ("*") mark in order to let the program identify this feature as a label/category name. 
 // ColorsCategorical = the categorical colors (maximum value = 10).
-var Category; var ColorsCategorical; var valCategExists = 0; 
+var Category; var ColorsCategorical; var valCategExists = 0; var insideCall = false;
 
 // This is for the removal of the distances cache. 
 var returnVal = false; 
@@ -34,7 +34,7 @@ var sliderTrigger = false; var sliderInsideTrigger = false; var parameters; var
 var inside = 0; var kValuesLegend = []; var findNearestTable = []; var howManyPoints;
 var maxKNN = 0
 
-var mode = 1; var colors = ['#a6cee3','#fb9a99','#b2df8a','#33a02c','#1f78b4','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a']; var projections = []; var metricsSorting = []; var dataReceivedFromServer = []; var dataReceivedFromServerOptimized = []; var metrics = []; var FocusedIDs = [];
+var mode = 1; var colors = ['#a6cee3','#fb9a99','#b2df8a','#33a02c','#1f78b4','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a']; var projections = []; var betas = []; var cost_per_point = []; var cost_overall; var metricsSorting = []; var dataReceivedFromServer = []; var dataReceivedFromServerOptimized = []; var metrics = []; var FocusedIDs = [];
 
 var Category; var target_names = []
 
@@ -140,6 +140,9 @@ function ReSort(flagInitialize) {
   projections = dataReceivedFromServer['projections']
   parameters = dataReceivedFromServer['parameters']
   metricsSorting = dataReceivedFromServer['metrics']
+  betas = dataReceivedFromServer['betas']
+  cost_per_point = dataReceivedFromServer['cpp']
+  cost_overall = dataReceivedFromServer['cpi']
 
   if (FocusedIDs.length != 0) {
     if (globalFlagCheck) {
@@ -147,11 +150,12 @@ function ReSort(flagInitialize) {
       metricsCopy = metrics
     }
     globalFlagCheck = false
-    document.getElementById("textToChange").innerHTML = "[Sorting Projections According to Metric for the Current Points Selection:";
     metricsSorting = dataReceivedFromServerOptimized['metrics']
     metrics = dataReceivedFromServerOptimized['metricsEntire']
     if (FocusedIDs.length == points.length) {
-      document.getElementById("textToChange").innerHTML = "[Sorting Metric:";
+      document.getElementById("textToChange").innerHTML = "[Sorting Projections According to Metric:";
+    } else {
+      document.getElementById("textToChange").innerHTML = "[Sorting Projections According to Metric for Current Selection:";
     }
   } else {
     if (!globalFlagCheck) {
@@ -669,6 +673,7 @@ if(k >= 6) {
           Plotly.relayout(graphDiv, update)
 
           sliderInsideTrigger = true
+          insideCall = true;
           activeProjectionNumberProv = order[SelProjIDSProv[0]]
 
       getData()
@@ -676,6 +681,7 @@ if(k >= 6) {
 
   if (flagInitialize) {
     closeModalFun()
+    insideCall = true;
     getData()
   }
   
@@ -804,19 +810,68 @@ function parseData(url) {
             // results.data variable is all the columns except strings, undefined values, or "Version" plus beta and cost values."
             // results.meta.fields variable is all the features (columns) plus beta and cost strings.  
             if (mode == 2) {
-              init(results.data, results_all, results.meta.fields); // Call the init() function that starts everything!
+                if (insideCall) {
+                  init(results.data, results_all, results.meta.fields); // Call the init() function that starts everything!
+                } else {
+                  $.post("http://127.0.0.1:5000/resetAll", JSON.stringify(''), function(){
+                  });
+                  var parametersLocal = []
+                  parametersLocal.push(document.getElementById("param-perplexity-value").value)
+                  parametersLocal.push(document.getElementById("param-learningrate-value").value)
+                  parametersLocal.push(document.getElementById("param-maxiter-value").value)
+                  parametersLocal.push(results_all)
+                  $.post("http://127.0.0.1:5000/receiverSingle", JSON.stringify(parametersLocal), function(){
+                    $.get("http://127.0.0.1:5000/senderSingle", function( data ) {
+                      dataReceivedFromServer = data
+                      projections = dataReceivedFromServer['projections']
+                      betas = dataReceivedFromServer['betas']
+                      cost_per_point = dataReceivedFromServer['cpp']
+                      cost_overall = dataReceivedFromServer['cpi']
+                      activeProjectionNumber = 0
+                      activeProjectionNumberProv = 0
+                      init(results.data, results_all, results.meta.fields); // Call the init() function that starts everything!
+                  });
+                  
+                });
+              }
             } else {
-              // ajax the JSON to the server
+              if (flagAnalysis) {
-              $.post("http://127.0.0.1:5000/resetAll", JSON.stringify(''), function(){
-              });
-              $.post("http://127.0.0.1:5000/receiver", JSON.stringify(results_all), function(){
-                $.get("http://127.0.0.1:5000/sender", function( data ) {
-                  dataReceivedFromServer = data
-                  ReSortOver()
 
-            });
+                $('#myModal').modal('hide');
+                $.post("http://127.0.0.1:5000/resetAll", JSON.stringify(''), function(){
+                  });
+
+                  var parametersLocal = []
+                  parametersLocal.push(document.getElementById("param-perplexity-value").value)
+                  parametersLocal.push(document.getElementById("param-learningrate-value").value)
+                  parametersLocal.push(document.getElementById("param-maxiter-value").value)
+                  parametersLocal.push(results_all)
+                  $.post("http://127.0.0.1:5000/receiverSingle", JSON.stringify(parametersLocal), function(){
+                    $.get("http://127.0.0.1:5000/senderSingle", function( data ) {
+                      dataReceivedFromServer = data
+                      projections = dataReceivedFromServer['projections']
+                      betas = dataReceivedFromServer['betas']
+                      cost_per_point = dataReceivedFromServer['cpp']
+                      cost_overall = dataReceivedFromServer['cpi']
+                      activeProjectionNumber = 0
+                      init(results.data, results_all, results.meta.fields); // Call the init() function that starts everything!
+                  });
+                  
+                });
+              } else {
+                    // ajax the JSON to the server
+                    $.post("http://127.0.0.1:5000/resetAll", JSON.stringify(''), function(){
+                    });
+                    $.post("http://127.0.0.1:5000/receiver", JSON.stringify(results_all), function(){
+                      $.get("http://127.0.0.1:5000/sender", function( data ) {
+                        dataReceivedFromServer = data
+                        ReSortOver()
+
+                  });
+                  
+                });
+              }
 
-          });
         }
             
           }
@@ -834,6 +889,9 @@ function ReSortOver() {
   parameters = dataReceivedFromServer['parameters']
   metricsSorting = dataReceivedFromServer['metrics']
   metrics = dataReceivedFromServer['metricsEntire']
+  betas = dataReceivedFromServer['betas']
+  cost_per_point = dataReceivedFromServer['cpp']
+  cost_overall = dataReceivedFromServer['cpi']
 
   var traces = []
   var target_names = []
@@ -2629,10 +2687,10 @@ function init(data, results_all, fields) {
     } else{
       tsne = new tsnejs.tSNE(opt); // Set new t-SNE with specific perplexity.
       dists = [];
-      dists = computeDistances(data, document.getElementById("param-distance").value, document.getElementById("param-transform").value); // Compute the distances in the high-dimensional space.
+      dists = computeDistances(data, 'euclideanDist', 'noTrans'); // Compute the distances in the high-dimensional space.
       InitialFormDists.push(dists);
-      tsne.initDataDist(dists); // Init t-SNE with dists.
+      //tsne.initDataDist(dists); // Init t-SNE with dists.
-      for(var i = 0; i < final_dataset.length; i++) {final_dataset[i].beta = tsne.beta[i]; beta_all[i] = tsne.beta[i];} // Calculate beta and bring it back from the t-SNE algorithm.
+      //for(var i = 0; i < final_dataset.length; i++) {final_dataset[i].beta = tsne.beta[i]; beta_all[i] = tsne.beta[i];} // Calculate beta and bring it back from the t-SNE algorithm.
     }
     var object;
     all_labels = [];
@@ -2748,28 +2806,6 @@ function euclideanDist(data) {
 
 }
 
-// Calculate jaccard dist
-function jaccardDist(data) {
-
-  dist = initDist(data);
-  for(var i = 0; i < data.length; i++) {
-    for(var j = i + 1; j < data.length; j++) {
-      for(var d in data[0]) {
-        if(d != Category) {
-          x = data[i][d];
-          y = data[j][d];
-          if(x == y) {
-            dist[i][j] += 1;
-          }
-        }
-      }
-      dist[j][i] = dist[i][j];
-    }
-  }
-  return dist;
-
-}
-
 // Normalize distances to prevent numerical issues.
 function normDist(data, dist) {
 
@@ -2793,51 +2829,6 @@ function noTrans(data) {
   return data;
 }
 
-// Log tranformation
-function logTrans(data) {
-
-  for(var i = 0; i < data.length; i++) {
-    for(var d in data[0]) {
-      if(d != Category) {
-        X = data[i][d];
-        data[i][d] = Math.log10(X + 1);
-      }
-    }
-  }
-  return data;
-
-}
-
-// asinh tranformation
-function asinhTrans(data) {
-
-  for(var i = 0; i < data.length; i++) {
-    for(var d in data[0]) {
-      if(d != Category) {
-        X = data[i][d];
-        data[i][d] = Math.log(X + Math.sqrt(X * X + 1));
-      }
-    }
-  }
-  return data;
-
-}
-// Binarize tranformation
-function binTrans(data) {
-
-  for(var i = 0; i < data.length; i++) {
-    for(var d in data[0]) {
-      if(d != Category) {
-        X = data[i][d];
-        if(X > 0) data[i][d] = 1;
-        if(X < 0) data[i][d] = 0;
-      }
-    }
-  }
-  return data;
-
-}
-
 // Compute the distances by applying the chosen distance functions and transformation functions.
 function computeDistances(data, distFunc, transFunc) {
   dist = eval(distFunc)(eval(transFunc)(data));
@@ -2884,7 +2875,7 @@ function OverallCostLineChart(){
     margin: {
       l: 40,
       r: 15,
-      b: 26,
+      b: 30,
       t: 5
     },
   };
@@ -2899,7 +2890,15 @@ function updateEmbedding(AnalysisResults) {
   points = [];
   points2d = [];
   if (AnalysisResults == ""){ // Check if the embedding does not need to load because we had a previous analysis uploaded.
-    var Y = tsne.getSolution(); // We receive the solution from the t-SNE
+    if (sliderTrigger) {
+      if (sliderInsideTrigger) {
+        var Y = projections[activeProjectionNumberProv]
+      } else {
+        var Y = projections[activeProjectionNumber]
+      }
+    } else {
+      var Y = projections[activeProjectionNumber]
+    }
     var xExt = d3.extent(Y, d => d[0]);
     var yExt = d3.extent(Y, d => d[1]);
     var maxExt = [Math.min(xExt[0], yExt[0]), Math.max(xExt[1], yExt[1])];
@@ -2911,10 +2910,11 @@ function updateEmbedding(AnalysisResults) {
     var y = d3.scaleLinear() // Scale the y points into the canvas width/height
             .domain(maxExt)
             .range([10, +dimensionsY-10]);
-      for(var i = 0; i < final_dataset.length; i++) {
+
+      for(var i = 0; i < betas[activeProjectionNumber].length; i++) {
         x_position[i] = x(Y[i][0]); // x points position
         y_position[i] = y(Y[i][1]); // y points position
-            points[i] = {id: i, x: x_position[i], y: y_position[i], beta: final_dataset[i].beta, cost: final_dataset[i].cost, selected: true, schemaInv: false, DimON: null, pcp: false}; // Create the points and points2D (2 dimensions) 
+            points[i] = {id: i, x: x_position[i], y: y_position[i], beta: betas[activeProjectionNumber][i], cost: cost_per_point[activeProjectionNumber][i], selected: true, schemaInv: false, DimON: null, pcp: false}; // Create the points and points2D (2 dimensions) 
             points2d[i] = {x: x_position[i], y: y_position[i]}; // and add everything that we know about the points (e.g., selected = true, pcp = false in the beginning and so on)
             points[i] = extend(points[i], ArrayContainsDataFeaturesCleared[i]);
             points[i] = extend(points[i], dataFeatures[i]);
@@ -2934,12 +2934,10 @@ function updateEmbedding(AnalysisResults) {
       ArrayWithCostsList = AnalysisResults.slice(2*dataFeatures.length+length+10, 2*dataFeatures.length+length+11);
       Iterations = IterationsList[0];
       ArrayWithCosts = ArrayWithCostsList[0];
-      $("#cost").html("(Number. of Iter.: " + ParametersSet[3] + ", Ov. Cost: " + overallCost + ")");
+      $("#cost").html("(Overall Cost: " + overallCost + ")");
       $('#param-perplexity-value').text(ParametersSet[1]);
       $('#param-learningrate-value').text(ParametersSet[2]);
       $('#param-maxiter-value').text(ParametersSet[3]);
-      document.getElementById("param-distance").value = ParametersSet[4];
-      document.getElementById("param-transform").value = ParametersSet[5];
     } else{
       var length = (AnalysisResults.length - 9) / 2;
       points = AnalysisResults.slice(0, length); // Load the points from the previous analysis
@@ -2950,12 +2948,10 @@ function updateEmbedding(AnalysisResults) {
       ArrayWithCostsList = AnalysisResults.slice(2*length+8, 2*length+9);
       Iterations = IterationsList[0];
       ArrayWithCosts = ArrayWithCostsList[0];
-      $("#cost").html("(Number of Iter.: " + ParametersSet[3] + ", Ov. Cost: " + overallCost + ")");
+      $("#cost").html("(Overall Cost: " + overallCost + ")");
       $('#param-perplexity-value').text(ParametersSet[1]);
       $('#param-learningrate-value').text(ParametersSet[2]);
       $('#param-maxiter-value').text(ParametersSet[3]);
-      document.getElementById("param-distance").value = ParametersSet[4];
-      document.getElementById("param-transform").value = ParametersSet[5];
     }
     $("#data").html(ParametersSet[0]); // Print on the screen the classification label.
     $("#param-dataset").html('-');
@@ -3014,7 +3010,7 @@ function ShepardHeatMap () {
       dist_list2d = []; // Distances lists empty
       dist_list = [];
       // Calculate the 2D distances.
-      dists2d = computeDistances(points2d, document.getElementById("param-distance").value, document.getElementById("param-transform").value);
+      dists2d = computeDistances(points2d, 'euclideanDist', 'noTrans');
       InitialFormDists2D.push(dists2d);
       for (var j=0; j<dists2d.length; j++){ // Fill them with the distances 2D and high-dimensional, respectively.
         dists2d[j] = dists2d[j].slice(0,j);
@@ -3237,7 +3233,7 @@ function ShepardHeatMap () {
       dist_list2d = []; // Distances lists empty
       dist_list = [];
       // Calculate the 2D distances.
-      dists2d = computeDistances(points2d, document.getElementById("param-distance").value, document.getElementById("param-transform").value);
+      dists2d = computeDistances(points2d, 'euclideanDist', 'noTrans');
       InitialFormDists2D.push(dists2d);
     }
 
@@ -3381,12 +3377,43 @@ function ShepardHeatMap () {
 function step() {
       step_counter++;
       if(step_counter <= max_counter) {
-          cost = tsne.step();
+          //cost = tsne.step();
-          cost_each = cost[1];
+          //cost_each = cost[1];
-          for(var i = 0; i < final_dataset.length; i++) final_dataset[i].cost = cost_each[i];
+          //for(var i = 0; i < final_dataset.length; i++) final_dataset[i].cost = cost_each[i];
-          $("#cost").html("(Number of Iter.: " + tsne.iter + ", Ov. Cost: " + cost[0].toFixed(3) + ")");
+          if (sliderTrigger) {
-          ArrayWithCosts.push(cost[0].toFixed(3));
+            if (sliderInsideTrigger) {
-          Iterations.push(step_counter);
+              if (cost_overall[activeProjectionNumberProv][step_counter-1].toFixed(3) < 0) {
+                $("#cost").html("(Overall Cost: 0.000)");
+                ArrayWithCosts.push(0);
+                Iterations.push(step_counter);
+              } else {
+                $("#cost").html("(Overall Cost: " + cost_overall[activeProjectionNumberProv][step_counter-1].toFixed(3) + ")");
+                ArrayWithCosts.push(cost_overall[activeProjectionNumberProv][step_counter-1].toFixed(3));
+                Iterations.push(step_counter);
+              }
+
+            } else {
+              if (cost_overall[activeProjectionNumber][step_counter-1].toFixed(3) < 0) {
+                $("#cost").html("(Overall Cost: 0.000)");
+                ArrayWithCosts.push(0);
+                Iterations.push(step_counter);
+              } else {
+                $("#cost").html("(Overall Cost: " + cost_overall[activeProjectionNumber][step_counter-1].toFixed(3) + ")");
+                ArrayWithCosts.push(cost_overall[activeProjectionNumber][step_counter-1].toFixed(3));
+                Iterations.push(step_counter);
+              }
+            }
+          } else {
+            if (cost_overall[activeProjectionNumber][step_counter-1].toFixed(3) < 0) {
+              $("#cost").html("(Overall Cost: 0.000)");
+              ArrayWithCosts.push(0);
+              Iterations.push(step_counter);
+            } else {
+              $("#cost").html("(Overall Cost: " + cost_overall[activeProjectionNumber][step_counter-1].toFixed(3) + ")");
+              ArrayWithCosts.push(cost_overall[activeProjectionNumber][step_counter-1].toFixed(3));
+              Iterations.push(step_counter);
+            }
+        }
         }
         else {
             clearInterval(runner);
@@ -3586,7 +3613,11 @@ function CostHistogram(points){
     var max = (d3.max(points,function(d){ return d.cost; }));
     var min = (d3.min(points,function(d){ return d.cost; }));
     for (var i=0; i<points.length; i++){
-      frequency.push((points[i].cost-min)/(max-min));
+      if ((points[i].cost-min)/(max-min) < 0) {
+        frequency.push(0)
+      } else {
+        frequency.push((points[i].cost-min)/(max-min));
+      }
     }
     var trace2 = {
       x: frequency,
@@ -5179,7 +5210,7 @@ if (points.length) { // If points exist (at least 1 point)
     var minSize1 = points[temp].cost;
     for (var i=temp+1; i<points.length; i++){
       if (minSize1 > points[i].cost){
-        minSize1 = points[i].cost;
+          minSize1 = points[i].cost;
       }
     }
     
@@ -5233,7 +5264,12 @@ if (points.length) { // If points exist (at least 1 point)
         .attr("transform", "translate(10,20)");
 
       var SizeRange1 = [];
-      SizeRange1.push((minSize1).toFixed(4));
+
+      if (minSize1 < 0) {
+        SizeRange1.push(0.0000)
+      } else {
+        SizeRange1.push((minSize1).toFixed(4));
+      }
       SizeRange1.push(((maxSize1-minSize1)/2).toFixed(4));
       SizeRange1.push((maxSize1).toFixed(4));
 
@@ -5267,7 +5303,11 @@ if (points.length) { // If points exist (at least 1 point)
     var min = points[temp].cost;
     for (var i=temp+1; i<points.length; i++){
       if (min > points[i].cost){
-        min = points[i].cost;
+        if (points[i].cost < 0) {
+          min = 0;
+        } else {
+          min = points[i].cost;
+        }
       }
     }
 
@@ -5294,7 +5334,7 @@ if (points.length) { // If points exist (at least 1 point)
       labels_cost = d3.range(min, max+calcStep, calcStep);
       for (var i=0; i<9; i++){
         labels_cost[i] = labels_cost[i].toFixed(5);
-        abbr_labels_cost[i] = abbreviateNumber(labels_cost[i]);
+        abbr_labels_cost[i] = Math.abs(abbreviateNumber(labels_cost[i]));
       }
 
       var svg = d3.select("#legend1"); // Add the legend for the beta/cost
@@ -5575,8 +5615,8 @@ if (points.length) { // If points exist (at least 1 point)
         var abbr_labels_cost = [];
         labels_cost = d3.range(min, max+calcStep, calcStep);
         for (var i=0; i<7; i++){
-          labels_cost[i] = labels_cost[i].toFixed(5);
+          labels_cost[i] = labels_cost[i].toFixed(4);
-          abbr_labels_cost[i] = abbreviateNumber(labels_cost[i]);
+          abbr_labels_cost[i] = Math.abs(abbreviateNumber(labels_cost[i]));
         }
   
         var svg = d3.select("#legend1"); // Add the legend for the beta/cost
@@ -5588,7 +5628,7 @@ if (points.length) { // If points exist (at least 1 point)
           .labelFormat(d3.format(",.5f"))
           .cells(7)
           .labels([abbr_labels_cost[0],abbr_labels_cost[1],abbr_labels_cost[2],abbr_labels_cost[3],abbr_labels_cost[4],abbr_labels_cost[5],abbr_labels_cost[6]])
-          .title("Remaining Cost")
+          .title("Rem. Cost")
           .scale(colorScale);
   
         svg.select(".legendLinear")
@@ -6027,8 +6067,8 @@ function SaveAnalysis(){ // Save the analysis into a .txt file
   let perplexity = document.getElementById("param-perplexity-value").value;
   let learningRate = document.getElementById("param-learningrate-value").value;
   let IterValue = document.getElementById("param-maxiter-value").value;
-  let parDist = document.getElementById("param-distance").value;
+  let parDist = 'euclideanDist';
-  let parTrans = document.getElementById("param-transform").value;
+  let parTrans = 'noTrans';
   let Parameters = [];
   if (dataset == "empty"){
     Parameters.push(new_file.name);

tsne.cpp

@@ -43,9 +43,23 @@
 
 using namespace std;
 
+static double sign(double x) { return (x == .0 ? .0 : (x < .0 ? -1.0 : 1.0)); }
+
+static void zeroMean(double* X, int N, int D);
+static void computeGaussianPerplexity(double* X, int N, int D, double* P, double perplexity);
+static void computeGaussianPerplexity(double* X, int N, int D, unsigned int** _row_P, unsigned int** _col_P, double** _val_P, double perplexity, int K);
+static double randn();
+static void computeExactGradient(double* P, double* Y, int N, int D, double* dC);
+static void computeGradient(unsigned int* inp_row_P, unsigned int* inp_col_P, double* inp_val_P, double* Y, int N, int D, double* dC, double theta);
+static double evaluateError(double* P, double* Y, int N, int D);
+static double evaluateError(unsigned int* row_P, unsigned int* col_P, double* val_P, double* Y, int N, int D, double theta, bool save_cost_per_point = false);
+static void computeSquaredEuclideanDistance(double* X, int N, int D, double* DD);
+static void symmetrizeMatrix(unsigned int** row_P, unsigned int** col_P, double** val_P, int N);
+static void save_1D_array(double* array, int n, const char* filename);
+
 // Perform t-SNE
 void TSNE::run(double* X, int N, int D, double* Y, int no_dims, double perplexity, double theta, int rand_seed,
-               bool skip_random_init, int max_iter, int stop_lying_iter, int mom_switch_iter) {
+               bool skip_random_init, int max_iter, int stop_lying_iter, int mom_switch_iter, int cost_iter_step) {
 
     // Set random seed
     if (skip_random_init != true) {
@@ -133,20 +147,22 @@ void TSNE::run(double* X, int N, int D, double* Y, int no_dims, double perplexit
     else {      for(int i = 0; i < row_P[N]; i++) val_P[i] *= 12.0; }
 
 	// Initialize solution (randomly)
-  if (skip_random_init != true) {
+    if (skip_random_init != true) {
-  	for(int i = 0; i < N * no_dims; i++) Y[i] = randn() * .0001;
+        for(int i = 0; i < N * no_dims; i++) Y[i] = randn() * .0001;
-  }
+    }
 
 	// Perform main training loop
     if(exact) printf("Input similarities computed in %4.2f seconds!\nLearning embedding...\n", (float) (end - start) / CLOCKS_PER_SEC);
     else printf("Input similarities computed in %4.2f seconds (sparsity = %f)!\nLearning embedding...\n", (float) (end - start) / CLOCKS_PER_SEC, (double) row_P[N] / ((double) N * (double) N));
     start = clock();
 
+    double* cost_per_iter = new double[max_iter];
+
 	for(int iter = 0; iter < max_iter; iter++) {
 
         // Compute (approximate) gradient
         if(exact) computeExactGradient(P, Y, N, no_dims, dY);
-        else computeGradient(P, row_P, col_P, val_P, Y, N, no_dims, dY, theta);
+        else computeGradient(row_P, col_P, val_P, Y, N, no_dims, dY, theta);
 
         // Update gains
         for(int i = 0; i < N * no_dims; i++) gains[i] = (sign(dY[i]) != sign(uY[i])) ? (gains[i] + .2) : (gains[i] * .8);
@@ -167,22 +183,29 @@ void TSNE::run(double* X, int N, int D, double* Y, int no_dims, double perplexit
         if(iter == mom_switch_iter) momentum = final_momentum;
 
         // Print out progress
-        if (iter > 0 && (iter % 50 == 0 || iter == max_iter - 1)) {
+        if (iter % cost_iter_step == 0 || iter == max_iter - 1) {
             end = clock();
             double C = .0;
             if(exact) C = evaluateError(P, Y, N, no_dims);
             else      C = evaluateError(row_P, col_P, val_P, Y, N, no_dims, theta);  // doing approximate computation here!
             if(iter == 0)
-                printf("Iteration %d: error is %f\n", iter + 1, C);
+                printf("Iteration %d: error is %f\n", iter, C);
             else {
                 total_time += (float) (end - start) / CLOCKS_PER_SEC;
-                printf("Iteration %d: error is %f (50 iterations in %4.2f seconds)\n", iter, C, (float) (end - start) / CLOCKS_PER_SEC);
+                printf("Iteration %d: error is %f (%d iterations in %4.2f seconds)\n", iter, C, cost_iter_step, (float) (end - start) / CLOCKS_PER_SEC);
             }
+            cost_per_iter[iter] = C;
 			start = clock();
         }
     }
     end = clock(); total_time += (float) (end - start) / CLOCKS_PER_SEC;
 
+    // Save the cost per iteration to file
+    save_1D_array(cost_per_iter, max_iter, "cost_per_iter.txt");
+
+    // This time it's for saving the final cost per point
+    evaluateError(row_P, col_P, val_P, Y, N, no_dims, theta, true);
+
     // Clean up memory
     free(dY);
     free(uY);
@@ -193,12 +216,13 @@ void TSNE::run(double* X, int N, int D, double* Y, int no_dims, double perplexit
         free(col_P); col_P = NULL;
         free(val_P); val_P = NULL;
     }
+    delete[] cost_per_iter;
     printf("Fitting performed in %4.2f seconds.\n", total_time);
 }
 
 
 // Compute gradient of the t-SNE cost function (using Barnes-Hut algorithm)
-void TSNE::computeGradient(double* P, unsigned int* inp_row_P, unsigned int* inp_col_P, double* inp_val_P, double* Y, int N, int D, double* dC, double theta)
+static void computeGradient(unsigned int* inp_row_P, unsigned int* inp_col_P, double* inp_val_P, double* Y, int N, int D, double* dC, double theta)
 {
 
     // Construct space-partitioning tree on current map
@@ -222,7 +246,7 @@ void TSNE::computeGradient(double* P, unsigned int* inp_row_P, unsigned int* inp
 }
 
 // Compute gradient of the t-SNE cost function (exact)
-void TSNE::computeExactGradient(double* P, double* Y, int N, int D, double* dC) {
+static void computeExactGradient(double* P, double* Y, int N, int D, double* dC) {
 
 	// Make sure the current gradient contains zeros
 	for(int i = 0; i < N * D; i++) dC[i] = 0.0;
@@ -272,7 +296,7 @@ void TSNE::computeExactGradient(double* P, double* Y, int N, int D, double* dC)
 
 
 // Evaluate t-SNE cost function (exactly)
-double TSNE::evaluateError(double* P, double* Y, int N, int D) {
+static double evaluateError(double* P, double* Y, int N, int D) {
 
     // Compute the squared Euclidean distance matrix
     double* DD = (double*) malloc(N * N * sizeof(double));
@@ -308,7 +332,7 @@ double TSNE::evaluateError(double* P, double* Y, int N, int D) {
 }
 
 // Evaluate t-SNE cost function (approximately)
-double TSNE::evaluateError(unsigned int* row_P, unsigned int* col_P, double* val_P, double* Y, int N, int D, double theta)
+static double evaluateError(unsigned int* row_P, unsigned int* col_P, double* val_P, double* Y, int N, int D, double theta, bool save_cost_per_point)
 {
 
     // Get estimate of normalization term
@@ -316,11 +340,13 @@ double TSNE::evaluateError(unsigned int* row_P, unsigned int* col_P, double* val
     double* buff = (double*) calloc(D, sizeof(double));
     double sum_Q = .0;
     for(int n = 0; n < N; n++) tree->computeNonEdgeForces(n, theta, buff, &sum_Q);
+    double* cost_per_point = new double[N];
 
     // Loop over all edges to compute t-SNE error
     int ind1, ind2;
-    double C = .0, Q;
+    double C = .0, Q, c;
     for(int n = 0; n < N; n++) {
+        cost_per_point[n] = .0;
         ind1 = n * D;
         for(int i = row_P[n]; i < row_P[n + 1]; i++) {
             Q = .0;
@@ -329,19 +355,25 @@ double TSNE::evaluateError(unsigned int* row_P, unsigned int* col_P, double* val
             for(int d = 0; d < D; d++) buff[d] -= Y[ind2 + d];
             for(int d = 0; d < D; d++) Q += buff[d] * buff[d];
             Q = (1.0 / (1.0 + Q)) / sum_Q;
-            C += val_P[i] * log((val_P[i] + FLT_MIN) / (Q + FLT_MIN));
+            c = val_P[i] * log((val_P[i] + FLT_MIN) / (Q + FLT_MIN));
+            cost_per_point[n] += c;
+            C += c;
         }
     }
 
+    if (save_cost_per_point)
+        save_1D_array(cost_per_point, N, "cost_per_point.txt");
+
     // Clean up memory
     free(buff);
     delete tree;
+    delete[] cost_per_point;
     return C;
 }
 
 
 // Compute input similarities with a fixed perplexity
-void TSNE::computeGaussianPerplexity(double* X, int N, int D, double* P, double perplexity) {
+static void computeGaussianPerplexity(double* X, int N, int D, double* P, double perplexity) {
 
 	// Compute the squared Euclidean distance matrix
 	double* DD = (double*) malloc(N * N * sizeof(double));
@@ -412,7 +444,7 @@ void TSNE::computeGaussianPerplexity(double* X, int N, int D, double* P, double
 
 
 // Compute input similarities with a fixed perplexity using ball trees (this function allocates memory another function should free)
-void TSNE::computeGaussianPerplexity(double* X, int N, int D, unsigned int** _row_P, unsigned int** _col_P, double** _val_P, double perplexity, int K) {
+static void computeGaussianPerplexity(double* X, int N, int D, unsigned int** _row_P, unsigned int** _col_P, double** _val_P, double perplexity, int K) {
 
     if(perplexity > K) printf("Perplexity should be lower than K!\n");
 
@@ -439,6 +471,7 @@ void TSNE::computeGaussianPerplexity(double* X, int N, int D, unsigned int** _ro
     printf("Building tree...\n");
     vector<DataPoint> indices;
     vector<double> distances;
+    double* betas = new double[N];
     for(int n = 0; n < N; n++) {
 
         if(n % 10000 == 0) printf(" - point %d of %d\n", n, N);
@@ -495,6 +528,9 @@ void TSNE::computeGaussianPerplexity(double* X, int N, int D, unsigned int** _ro
             iter++;
         }
 
+        // Save the found beta to a vector of all betas
+        betas[n] = beta;
+
         // Row-normalize current row of P and store in matrix
         for(unsigned int m = 0; m < K; m++) cur_P[m] /= sum_P;
         for(unsigned int m = 0; m < K; m++) {
@@ -503,15 +539,19 @@ void TSNE::computeGaussianPerplexity(double* X, int N, int D, unsigned int** _ro
         }
     }
 
+    // Save the betas to disk, so we can recover them later
+    save_1D_array(betas, N, "betas.txt");
+
     // Clean up memory
     obj_X.clear();
     free(cur_P);
     delete tree;
+    delete[] betas;
 }
 
 
 // Symmetrizes a sparse matrix
-void TSNE::symmetrizeMatrix(unsigned int** _row_P, unsigned int** _col_P, double** _val_P, int N) {
+static void symmetrizeMatrix(unsigned int** _row_P, unsigned int** _col_P, double** _val_P, int N) {
 
     // Get sparse matrix
     unsigned int* row_P = *_row_P;
@@ -599,7 +639,7 @@ void TSNE::symmetrizeMatrix(unsigned int** _row_P, unsigned int** _col_P, double
 }
 
 // Compute squared Euclidean distance matrix
-void TSNE::computeSquaredEuclideanDistance(double* X, int N, int D, double* DD) {
+static void computeSquaredEuclideanDistance(double* X, int N, int D, double* DD) {
     const double* XnD = X;
     for(int n = 0; n < N; ++n, XnD += D) {
         const double* XmD = XnD + D;
@@ -618,7 +658,7 @@ void TSNE::computeSquaredEuclideanDistance(double* X, int N, int D, double* DD)
 
 
 // Makes data zero-mean
-void TSNE::zeroMean(double* X, int N, int D) {
+static void zeroMean(double* X, int N, int D) {
 
 	// Compute data mean
 	double* mean = (double*) calloc(D, sizeof(double));
@@ -647,7 +687,7 @@ void TSNE::zeroMean(double* X, int N, int D) {
 
 
 // Generates a Gaussian random number
-double TSNE::randn() {
+static double randn() {
 	double x, y, radius;
 	do {
 		x = 2 * (rand() / ((double) RAND_MAX + 1)) - 1;
@@ -702,3 +742,19 @@ void TSNE::save_data(double* data, int* landmarks, double* costs, int n, int d)
     fclose(h);
 	printf("Wrote the %i x %i data matrix successfully!\n", n, d);
 }
+
+// Function that saves a 1D array in the numpy.savetxt format
+void save_1D_array(double* array, int n, const char* filename) {
+
+    // Open file, write first 2 integers and then the data
+	FILE *h;
+	if((h = fopen(filename, "w")) == NULL) {
+		printf("Error: could not open [%s] for writing.\n", filename);
+		return;
+	}
+	//fprintf(h, "%d\n", n);
+    for (int i = 0; i < n; ++i)
+        fprintf(h, "%f\n", array[i]);
+    fclose(h);
+	printf("Wrote [%s] successfully, with %i rows!\n", filename, n);
+}

tsne.h

@@ -34,30 +34,18 @@
 #ifndef TSNE_H
 #define TSNE_H
 
-
+#ifdef __cplusplus
-static inline double sign(double x) { return (x == .0 ? .0 : (x < .0 ? -1.0 : 1.0)); }
+extern "C" {
-
+namespace TSNE {
-
+#endif
-class TSNE
-{
-public:
     void run(double* X, int N, int D, double* Y, int no_dims, double perplexity, double theta, int rand_seed,
-             bool skip_random_init, int max_iter=1000, int stop_lying_iter=250, int mom_switch_iter=250);
+             bool skip_random_init, int max_iter, int stop_lying_iter, int mom_switch_iter, int cost_iter_step);
     bool load_data(double** data, int* n, int* d, int* no_dims, double* theta, double* perplexity, int* rand_seed, int* max_iter);
     void save_data(double* data, int* landmarks, double* costs, int n, int d);
-    void symmetrizeMatrix(unsigned int** row_P, unsigned int** col_P, double** val_P, int N); // should be static!
+    //void save_1D_array(double* data, int N, char* filename);
-
+#ifdef __cplusplus
-
-private:
-    void computeGradient(double* P, unsigned int* inp_row_P, unsigned int* inp_col_P, double* inp_val_P, double* Y, int N, int D, double* dC, double theta);
-    void computeExactGradient(double* P, double* Y, int N, int D, double* dC);
-    double evaluateError(double* P, double* Y, int N, int D);
-    double evaluateError(unsigned int* row_P, unsigned int* col_P, double* val_P, double* Y, int N, int D, double theta);
-    void zeroMean(double* X, int N, int D);
-    void computeGaussianPerplexity(double* X, int N, int D, double* P, double perplexity);
-    void computeGaussianPerplexity(double* X, int N, int D, unsigned int** _row_P, unsigned int** _col_P, double** _val_P, double perplexity, int K);
-    void computeSquaredEuclideanDistance(double* X, int N, int D, double* DD);
-    double randn();
 };
+}
+#endif
 
 #endif

tsneGrid.py

@@ -42,12 +42,30 @@ def Reset():
     global projectionsAll
     projectionsAll = []
 
+    global betas
+    betas = []
+
+    global cpp
+    cpp = []
+
+    global cpi
+    cpi = []
+
     global SelectedListofParams
     SelectedListofParams = []
 
     global SelectedProjectionsReturn
     SelectedProjectionsReturn = []
 
+    global SelectedProjectionsBeta
+    SelectedProjectionsBeta = []
+
+    global SelectedProjectionsCPP
+    SelectedProjectionsCPP = []
+
+    global SelectedProjectionsCPI
+    SelectedProjectionsCPI = []
+
     global clusterIndex
     clusterIndex = []
 
@@ -160,8 +178,9 @@ def preprocess(data):
     return dataNP, length, gatherLabels
 
 def multi_run_wrapper(args):
-    embedding_array = bhtsne.run_bh_tsne(*args)
+    projectionsAllLoc, betasL, cppL, cpiL = bhtsne.run_bh_tsne(*args)
-    return embedding_array
+
+    return projectionsAllLoc, betasL, cppL, cpiL
 
 def procrustesFun(projections):
     similarityList = []
@@ -198,7 +217,7 @@ memory = Memory(location, verbose=0)
 def wrapGetResults(listofParamsPlusData):
     pool = Pool()
     
-    return pool.map(multi_run_wrapper, listofParamsPlusData)
+    return zip(*pool.map(multi_run_wrapper, listofParamsPlusData))
 
 wrapGetResults = memory.cache(wrapGetResults)
 
@@ -213,12 +232,9 @@ def calculateGrid():
     D_highSpace = distance.squareform(distance.pdist(dataProc))
 
     DEFAULT_NO_DIMS = 2
-    INITIAL_DIMENSIONS = 50
+    VERBOSE = False
-    DEFAULT_PERPLEXITY = 50
+    DEFAULT_USE_PCA = True
-    DEFAULT_THETA = 0.5
+    randseed=1137
-    EMPTY_SEED = -1
-    VERBOSE = True
-    DEFAULT_USE_PCA = False
 
     # all other data sets
     perplexity = [5,10,15,20,25,30,35,40,45,50] # 10 perplexity
@@ -249,16 +265,26 @@ def calculateGrid():
     for k in n_iter:
         for j in learning_rate:
             for i in perplexity:
-                listofParamsPlusData.append((dataProc,DEFAULT_NO_DIMS,length,i,j,EMPTY_SEED,VERBOSE,DEFAULT_USE_PCA,k))
+                listofParamsPlusData.append((dataProc,DEFAULT_NO_DIMS,i,j,randseed,VERBOSE,length,DEFAULT_USE_PCA,k,True,True,True))
                 listofParamsAll.append((i,j,k)) 
 
-    projectionsAll = wrapGetResults(listofParamsPlusData)
+    projectionsAll, betas, cpp, cpi = wrapGetResults(listofParamsPlusData)
 
     global SelectedListofParams
     SelectedListofParams = []
+
     global SelectedProjectionsReturn
     SelectedProjectionsReturn = []
 
+    global SelectedProjectionsBeta
+    SelectedProjectionsBeta = []
+
+    global SelectedProjectionsCPP
+    SelectedProjectionsCPP = []
+
+    global SelectedProjectionsCPI
+    SelectedProjectionsCPI = []
+    
     global clusterIndex
     clusterIndex = procrustesFun(projectionsAll)
 
@@ -295,10 +321,18 @@ def calculateGrid():
     global KeepKs
     KeepKs = []
 
+
+
     for index in clusterIndex:
         SelectedProjectionsReturn.append(projectionsAll[index].tolist())
         SelectedListofParams.append(listofParamsAll[index])
 
+        SelectedProjectionsBeta.append(betas[index].tolist())
+
+        SelectedProjectionsCPP.append(cpp[index].tolist())
+
+        SelectedProjectionsCPI.append(cpi[index].tolist())
+
         D_lowSpace = distance.squareform(distance.pdist(projectionsAll[index]))
         D_lowSpaceList.append(D_lowSpace)
 
@@ -372,10 +406,13 @@ def background_process():
     global overalProjectionsNumber
     global metricsMatrix
     global metricsMatrixEntire
+    global SelectedProjectionsBeta
+    global SelectedProjectionsCPP
+    global SelectedProjectionsCPI
 
     while (len(projectionsAll) != overalProjectionsNumber):
         pass
-    return jsonify({ 'projections': SelectedProjectionsReturn, 'parameters': SelectedListofParams, 'metrics': metricsMatrix, 'metricsEntire': metricsMatrixEntire })
+    return jsonify({ 'projections': SelectedProjectionsReturn, 'parameters': SelectedListofParams, 'metrics': metricsMatrix, 'metricsEntire': metricsMatrixEntire, 'betas': SelectedProjectionsBeta, 'cpp': SelectedProjectionsCPP, 'cpi': SelectedProjectionsCPI})
 
 @app.route('/receiverOptimizer', methods = ['POST'])
 def OptimizeSelection():
@@ -462,6 +499,69 @@ def SendOptimizedProjections():
 
     return jsonify({'metrics': metricsMatrixSel, 'metricsEntire': metricsMatrixEntireSel })
 
+@app.route('/receiverSingle', methods = ['POST'])
+def singleParameters():
+    data = request.get_data().decode('utf8').replace("'", '"')
+    data = json.loads(data)
+
+    global dataProc
+    dataProc, length, labels = preprocess(data[3])
+
+    DEFAULT_NO_DIMS = 2
+    VERBOSE = False
+    DEFAULT_USE_PCA = True
+    randseed=1137
+
+    perplexity = int(data[0])
+    learning_rate = int(data[1])
+    n_iter = int(data[2])
+
+    global projectionsAll
+
+    listofParamsPlusData = []
+    listofParamsAll= []
+    listofParamsPlusData.append((dataProc,DEFAULT_NO_DIMS,perplexity,learning_rate,randseed,VERBOSE,length,DEFAULT_USE_PCA,n_iter,True,True,True))
+    listofParamsAll.append((perplexity,learning_rate,n_iter)) 
+
+    projectionsAll, betas, cpp, cpi = wrapGetResults(listofParamsPlusData)
+
+
+    global SelectedProjectionsReturn
+    SelectedProjectionsReturn = []
+
+    global SelectedProjectionsBeta
+    SelectedProjectionsBeta = []
+
+    global SelectedProjectionsCPP
+    SelectedProjectionsCPP = []
+
+    global SelectedProjectionsCPI
+    SelectedProjectionsCPI = []
+
+    SelectedProjectionsReturn.append(projectionsAll[0].tolist())
+
+    SelectedProjectionsBeta.append(betas[0].tolist())
+
+    SelectedProjectionsCPP.append(cpp[0].tolist())
+
+    SelectedProjectionsCPI.append(cpi[0].tolist())
+
+    return 'OK'
+
+@app.route('/senderSingle')
+def sendSingle():
+
+    global projectionsAll
+    global SelectedProjectionsReturn
+    global SelectedProjectionsBeta 
+    global SelectedProjectionsCPP
+    global SelectedProjectionsCPI
+    while (len(projectionsAll) != 1):
+        pass
+    return jsonify({ 'projections': SelectedProjectionsReturn, 'betas': SelectedProjectionsBeta, 'cpp': SelectedProjectionsCPP, 'cpi': SelectedProjectionsCPI})
+
+
 if __name__ == '__main__':
     app.run("0.0.0.0", "5000")
 
+

tsne_main.cpp

@@ -10,14 +10,12 @@
 int main() {
 
     // Define some variables
-	int origN, N, D, no_dims, max_iter, *landmarks;
+	int origN, N, D, no_dims, max_iter;
-	double perc_landmarks;
 	double perplexity, theta, *data;
     int rand_seed = -1;
-    TSNE* tsne = new TSNE();
 
     // Read the parameters and the dataset
-	if(tsne->load_data(&data, &origN, &D, &no_dims, &theta, &perplexity, &rand_seed, &max_iter)) {
+	if(TSNE::load_data(&data, &origN, &D, &no_dims, &theta, &perplexity, &rand_seed, &max_iter)) {
 
 		// Make dummy landmarks
         N = origN;
@@ -29,10 +27,10 @@ int main() {
 		double* Y = (double*) malloc(N * no_dims * sizeof(double));
 		double* costs = (double*) calloc(N, sizeof(double));
         if(Y == NULL || costs == NULL) { printf("Memory allocation failed!\n"); exit(1); }
-		tsne->run(data, N, D, Y, no_dims, perplexity, theta, rand_seed, false, max_iter);
+		TSNE::run(data, N, D, Y, no_dims, perplexity, theta, rand_seed, false, max_iter, 250, 250, 1);
 
 		// Save the results
-		tsne->save_data(Y, landmarks, costs, N, no_dims);
+		TSNE::save_data(Y, landmarks, costs, N, no_dims);
 
         // Clean up the memory
 		free(data); data = NULL;
@@ -40,5 +38,4 @@ int main() {
 		free(costs); costs = NULL;
 		free(landmarks); landmarks = NULL;
     }
-    delete(tsne);
 }