107 lines
3.6 KiB
Python
107 lines
3.6 KiB
Python
import numpy as np
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class DTLearner(object):
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LEAF = -1
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NA = -1
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def __init__(self, leaf_size = 1, verbose = False):
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self.leaf_size = leaf_size
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self.verbose = verbose
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def author(self):
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return 'felixm' # replace tb34 with your Georgia Tech username
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def create_node(self, factor, split_value, left, right):
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return np.array([[factor, split_value, left, right], ])
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def get_max_correlation(self, xs, y):
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""" Return the index of the column x of xs that has the highest
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absolute correlation with y. I would like to get a scalar value from
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np.corrcoef instead of a matrix, so I use [0, 1] to get a scalar value
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from the matrix. """
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# This should deliver the same result, but does not. I am not willing
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# to investigate right now.
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# a = np.argmax([abs(np.corrcoef(xs[:,i], y)[0, 1])
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# for i in range(xs.shape[1])])
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i_max = 0
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corr_max = 0
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for i in range(xs.shape[1]):
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corr_matrix = np.corrcoef(xs[:, i], y=y)
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corr = corr_matrix[0, 1]
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corr = abs(corr)
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if corr > corr_max:
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corr_max = corr
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i_max = i
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return i_max
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def make_tree_absolute(self, tree):
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for i in range(tree.shape[0]):
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if tree[i, 2] == self.NA:
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continue
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tree[i, 2] = i + tree[i, 2]
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tree[i, 3] = i + tree[i, 3]
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return tree
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def build_tree(self, xs, y):
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assert(xs.shape[0] == y.shape[0])
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assert(xs.shape[0] > 0) # If this is 0 something went wrong.
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if xs.shape[0] == 1:
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return self.create_node(self.LEAF, y[0], self.NA, self.NA)
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if np.all(y[0] == y):
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return self.create_node(self.LEAF, y[0], self.NA, self.NA)
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i = self.get_max_correlation(xs, y)
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split_value = np.median(xs[:,i])
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select_lt = xs[:, i] <= split_value
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select_rt = xs[:, i] > split_value
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# Avoid case where all values are low or equal to the median.
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if select_lt.all() or select_rt.all():
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select_lt = xs[:, i] < split_value
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select_rt = xs[:, i] >= split_value
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lt = self.build_tree(xs[select_lt], y[select_lt])
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rt = self.build_tree(xs[select_rt], y[select_rt])
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root = self.create_node(i, split_value, 1, rt.shape[0] + 1)
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root = np.concatenate([root, lt, rt])
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return root
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def addEvidence(self, data_x, data_y):
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"""
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@summary: Add training data to learner
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@param dataX: X values of data to add
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@param dataY: the Y training values
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"""
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self.rel_tree = self.build_tree(data_x, data_y)
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# self.abs_tree = self.make_tree_absolute(self.rel_tree)
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def query_point(self, point):
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node_index = 0
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while self.rel_tree[node_index, 0] != self.LEAF:
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node = self.rel_tree[node_index]
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split_factor = int(node[0])
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split_value = node[1]
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if point[split_factor] <= split_value:
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node_index += int(node[2])
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else:
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node_index += int(node[3])
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return self.rel_tree[node_index, 1]
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def query(self, points):
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"""
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@summary: Estimate a set of test points given the model we built.
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@param points: should be a numpy array with each row corresponding to a specific query.
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@returns the estimated values according to the saved model.
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"""
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query_point = lambda p: self.query_point(p)
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r = np.apply_along_axis(query_point, 1, points)
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return r
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if __name__=="__main__":
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print("the secret clue is 'zzyzx'")
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