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sigLearn.py

+import numpy as np
+import iisignature
+from sklearn.ensemble import RandomForestRegressor
+import numbers
+class sigLearn:
+	def __init__(self, order=2, alpha=0.1):
+		if not isinstance(order, numbers.Integral) or order<1:
+			raise NameError('The order must be a positive integer.')
+		if not isinstance(alpha, numbers.Real) or alpha<=0.0:
+			raise NameError('Alpha must be a positive real.')
+
+		self.order=int(order)
+		self.reg=None
+		self.alpha=alpha
+
+	def train(self, x, y):
+		'''
+
+		Trains the model using signatures.
+
+		x: list of inputs.
+		y: list of outputs.
+
+		'''
+        # We check that types of x and y are the expected ones
+		if x is None or y is None:
+			return
+		if not (type(x) is list or type(x) is tuple) or not (type(y) is list or type(y) is tuple):
+			raise NameError('Input and output must be lists or tuples.')
+		if len(x)!=len(y):
+			raise NameError('The number of inputs and the number of outputs must coincide.')
+		X=[list(iisignature.sig(np.array(stream), self.order)) for stream in x]
+		self.reg = RandomForestRegressor(n_estimators=100, oob_score=True)
+		self.reg.fit(X, y)
+
+	def predict(self, x):
+		'''
+
+		Predicts the outputs of the inputs x using the the pre-trained model.
+
+		x: list of inputs.
+		Returns:
+		list of predicted outputs.
+
+		'''
+		if self.reg is None:
+			raise NameError('The model is not trained.')
+		X=[list(iisignature.sig(np.array(stream), self.order)) for stream in x]
+
+		return self.reg.predict(X)
+