49 lines
1.8 KiB
Python
Executable File
49 lines
1.8 KiB
Python
Executable File
import numpy as np
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import sys
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sys.path.append('..')
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from submission import SubmissionBase
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class Grader(SubmissionBase):
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X1 = np.column_stack((np.ones(20), np.exp(1) + np.exp(2) * np.linspace(0.1, 2, 20)))
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Y1 = X1[:, 1] + np.sin(X1[:, 0]) + np.cos(X1[:, 1])
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X2 = np.column_stack((X1, X1[:, 1]**0.5, X1[:, 1]**0.25))
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Y2 = np.power(Y1, 0.5) + Y1
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def __init__(self):
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part_names = ['Warm up exercise',
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'Computing Cost (for one variable)',
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'Gradient Descent (for one variable)',
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'Feature Normalization',
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'Computing Cost (for multiple variables)',
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'Gradient Descent (for multiple variables)',
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'Normal Equations']
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super().__init__('linear-regression', part_names)
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def __iter__(self):
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for part_id in range(1, 8):
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try:
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func = self.functions[part_id]
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# Each part has different expected arguments/different function
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if part_id == 1:
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res = func()
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elif part_id == 2:
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res = func(self.X1, self.Y1, np.array([0.5, -0.5]))
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elif part_id == 3:
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res = func(self.X1, self.Y1, np.array([0.5, -0.5]), 0.01, 10)
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elif part_id == 4:
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res = func(self.X2[:, 1:4])
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elif part_id == 5:
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res = func(self.X2, self.Y2, np.array([0.1, 0.2, 0.3, 0.4]))
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elif part_id == 6:
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res = func(self.X2, self.Y2, np.array([-0.1, -0.2, -0.3, -0.4]), 0.01, 10)
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elif part_id == 7:
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res = func(self.X2, self.Y2)
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else:
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raise KeyError
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yield part_id, res
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except KeyError:
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yield part_id, 0
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