105 lines
3.2 KiB
Python
Executable File
105 lines
3.2 KiB
Python
Executable File
import sys
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import numpy as np
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from matplotlib import pyplot
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sys.path.append('..')
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from submission import SubmissionBase
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def displayData(X, example_width=None, figsize=(10, 10)):
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"""
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Displays 2D data stored in X in a nice grid.
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"""
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# Compute rows, cols
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if X.ndim == 2:
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m, n = X.shape
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elif X.ndim == 1:
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n = X.size
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m = 1
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X = X[None] # Promote to a 2 dimensional array
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else:
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raise IndexError('Input X should be 1 or 2 dimensional.')
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example_width = example_width or int(np.round(np.sqrt(n)))
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example_height = n / example_width
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# Compute number of items to display
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display_rows = int(np.floor(np.sqrt(m)))
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display_cols = int(np.ceil(m / display_rows))
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fig, ax_array = pyplot.subplots(display_rows, display_cols, figsize=figsize)
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fig.subplots_adjust(wspace=0.025, hspace=0.025)
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ax_array = [ax_array] if m == 1 else ax_array.ravel()
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for i, ax in enumerate(ax_array):
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ax.imshow(X[i].reshape(example_width, example_width, order='F'),
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cmap='Greys', extent=[0, 1, 0, 1])
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ax.axis('off')
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def sigmoid(z):
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"""
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Computes the sigmoid of z.
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"""
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return 1.0 / (1.0 + np.exp(-z))
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class Grader(SubmissionBase):
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# Random Test Cases
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X = np.stack([np.ones(20),
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np.exp(1) * np.sin(np.arange(1, 21)),
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np.exp(0.5) * np.cos(np.arange(1, 21))], axis=1)
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y = (np.sin(X[:, 0] + X[:, 1]) > 0).astype(float)
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Xm = np.array([[-1, -1],
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[-1, -2],
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[-2, -1],
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[-2, -2],
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[1, 1],
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[1, 2],
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[2, 1],
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[2, 2],
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[-1, 1],
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[-1, 2],
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[-2, 1],
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[-2, 2],
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[1, -1],
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[1, -2],
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[-2, -1],
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[-2, -2]])
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ym = np.array([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3])
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t1 = np.sin(np.reshape(np.arange(1, 25, 2), (4, 3), order='F'))
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t2 = np.cos(np.reshape(np.arange(1, 41, 2), (4, 5), order='F'))
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def __init__(self):
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part_names = ['Regularized Logistic Regression',
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'One-vs-All Classifier Training',
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'One-vs-All Classifier Prediction',
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'Neural Network Prediction Function']
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super().__init__('multi-class-classification-and-neural-networks', part_names)
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def __iter__(self):
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for part_id in range(1, 5):
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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(np.array([0.25, 0.5, -0.5]), self.X, self.y, 0.1)
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res = np.hstack(res).tolist()
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elif part_id == 2:
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res = func(self.Xm, self.ym, 4, 0.1)
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elif part_id == 3:
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res = func(self.t1, self.Xm) + 1
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elif part_id == 4:
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res = func(self.t1, self.t2, self.Xm) + 1
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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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