Hi all! I have been using the TSNE module in python’s sklearn to visualize n-dimensional binary valued dataset using ‘hamming’ as the distance metric. I plan on packing the n-dimensional dataset into bits in a uint8 array using numpy’s ‘packbits’ function. To use the TSNE module on the packed dataset, I would have to unpack the new dataset and use the ‘hamming’ distance metric as I have been doing in my original dataset. I however want to write a simple callable function that will unpack just two instances in the packed dataset and measure the hamming distance. In my initial attempt, I used a lambda function as a metric which takes two input arrays, unack them and outputs the hamming distance between the two. Here's an example given 'myPackedDataset' is the packed dataset:
tsneCluster = TSNE(learning_rate=rate_val, perplexity=per_val, n_iter=iter_val, metric=lambda x, y: distance.hamming(np.unpackbits(x), np.unpackbits(y))) fit_result = tsne.fit_transform(myPackedDataset) I get this error at the TSNE declaration: TypeError: Expected an input array of unsigned byte data type I am still trying to understand how the callable function option of the TSNE's metric parameter works but I would really appreciate any help.
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