Ryan Palo rpalo

## example.yaml
a: b
c: "d"
e: 45
f: 156.23
g: -1
h: -0.25
i:
  k: l
  m: n
  o:

## v_and_b_closest_indices.py
closest_indices = np.argmin(distance, axis=1)

## v_and_b_classifying.py
predictions = training_labels[closest_indices]

## v_and_b_4d_distance.py
distance = np.sqrt(np.sum((testing - training)**2, axis=1))

## v_and_b_add_3rd_axis.py
testing = testing.reshape((75, 4, 1))

# This is equivalent to doing:
testing = np.expand_dims(testing, axis=2)

# Or even:
testing = testing[:, :, np.newaxis]

## v_and_b_2d_reshape.py
training = training.reshape((75, 4, 1))  # Add the third axis
training = np.swapaxes(training, 0, 2)  # ROTATE!

## v_and_b_1d_distance.py
distances = np.sqrt((testing - training)**2)

## v_and_b_flip_training.py
training = np.swapaxes(training, 0, 1)

## v_and_b_setup_data.py
import numpy as np

training = np.random.randint(0, 100, 50).reshape(50, 1)
testing = np.random.randint(0, 100, 50).reshape(50, 1)

## v_and_b_vectorized_add.py
import numpy as np

a = np.array([1, 2, 3, 4, 5])
b = np.array([1, 1, 1, 5, 1])

a + b
# => [2, 3, 4, 9, 6]
	testing = testing.reshape((75, 4, 1))

	# This is equivalent to doing:
	testing = np.expand_dims(testing, axis=2)

	# Or even:
	testing = testing[:, :, np.newaxis]
	training = training.reshape((75, 4, 1)) # Add the third axis
	training = np.swapaxes(training, 0, 2) # ROTATE!
	import numpy as np

	training = np.random.randint(0, 100, 50).reshape(50, 1)
	testing = np.random.randint(0, 100, 50).reshape(50, 1)
	import numpy as np

	a = np.array([1, 2, 3, 4, 5])
	b = np.array([1, 1, 1, 5, 1])

	a + b
	# => [2, 3, 4, 9, 6]