File size: 2,183 Bytes
8d2b389 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | import re
import numpy as np
from sklearn.preprocessing import MinMaxScaler
class Discretizer:
def __init__(self, low_limit=-1, high_limit=1, n_tokens=10002):
self.scaler = MinMaxScaler()
self.boundaries = np.linspace(low_limit, high_limit, n_tokens - 1)
self.centers = (self.boundaries[1:] + self.boundaries[:-1]) / 2
self.centers = np.concatenate((self.centers[:1], self.centers, self.centers[-1:]))
def get_centers(self):
return self.centers
def discretize(self, context, fit_length=None):
fit_length = len(context) if fit_length is None else fit_length
self.scaler.fit(context[:fit_length].reshape(-1, 1))
scaled_context = self.scaler.transform(context.reshape(-1, 1)).reshape(-1) - 0.5
bin_ids = np.digitize(x=scaled_context, bins=self.boundaries, right=True)
dispersed_context = self.centers[bin_ids]
dispersed_context[np.isnan(context)] = np.nan
return dispersed_context
def inverse_discretize(self, scaled_context):
context = self.scaler.inverse_transform(scaled_context.reshape(-1, 1) + 0.5).reshape(-1)
return context
class Serializer:
def __init__(self, prec=4, time_sep=" ", time_flag="###", nan_flag="Nan"):
self.prec = prec
self.time_sep = time_sep
self.time_flag = time_flag
self.nan_flag = nan_flag
def serialize(self, context):
serialized_context = np.array([f"{self.time_flag}{i:.{self.prec}f}{self.time_flag}" for i in context])
serialized_context[np.isnan(context)] = f"{self.time_flag}{self.nan_flag}{self.time_flag}"
serialized_context = self.time_sep.join(serialized_context)
return serialized_context
def inverse_serialize(self, serialized_context):
pattern = rf"{self.time_flag}(.*?){self.time_flag}"
matches = re.findall(pattern, serialized_context)
context = []
for num in matches:
try:
context.append(float(num))
except ValueError as e:
print(e)
context.append(np.NaN)
context = np.array(context)
return context
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