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import matplotlib.colors as mcolors
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import torch
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def preprocess_data(data, skip: bool = False):
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# Assuming 'data' is a tensor of shape [n_samples, 3]
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if not skip:
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# Compute argmin and argmax for each row
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argmin_values = torch.argmin(data, dim=1, keepdim=True).float()
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argmax_values = torch.argmax(data, dim=1, keepdim=True).float()
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# Normalize or scale argmin and argmax if necessary
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# For example, here I am just dividing by the number of features
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argmin_values /= data.shape[1] - 1
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argmax_values /= data.shape[1] - 1
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# Concatenate the argmin and argmax values to the original data
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new_data = torch.cat((data, argmin_values, argmax_values), dim=1)
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else:
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new_data = data
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return new_data
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def extract_colors():
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# Extracting the list of xkcd colors as RGB triples
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xkcd_colors = mcolors.XKCD_COLORS
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rgb_values = [mcolors.to_rgb(color) for color in xkcd_colors.values()]
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# Extracting the list of xkcd color names
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xkcd_color_names = list(xkcd_colors.keys())
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# Convert the list of RGB triples to a PyTorch tensor
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rgb_tensor = torch.tensor(rgb_values, dtype=torch.float32)
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return rgb_tensor, xkcd_color_names
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PURE_RGB = preprocess_data(torch.cat([torch.eye(3), torch.eye(3) + torch.eye(3)[:, [1, 2, 0]]], dim=0))
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PURE_HSV = torch.tensor(
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[[0], [1 / 3], [2 / 3], [5 / 6], [1 / 6], [0.5]], dtype=torch.float32
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)
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