Search Results for: Leguminous split
/data/train.csv', 'r').read().split(' ') var_names = f[0].split(',') f = f[1 : -1] f = [list(map(int, x[1 : ])) for x in (y.split(",") for y in f)] def create_image(pixel_array): img = image.new('rgb', (28, 28)) pixels = img.load() count = 0 for i in range(img.size[0]): for j in range(img.size[1]): pixels
/data/train.csv", "r").read().split(" ")[1 : -1] ### separate your observations into a tuple (label, pixel list). f = [(int(x[0]), list(map(int, x[1 : ]))) for x in (y.split(",") for y in f)] ### json event exporter. exporter = predictionio.fileexporter("....
https://predictionio.apache.org/machinelearning/dimensionalityreduction/
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