Ryan Palo rpalo

## walle.fish
function walle
  set light_blue C7E2E8
  set gray C1C1BD
  set light_gray EEE
  set dark_gray 565653
  set bronze C6973F
  set yellow FFE500
  set lime C6E80B
  set brown 8C5D05

## an-old-hope.itermcolors
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
	<key>Ansi 0 Color</key>
	<dict>
		<key>Alpha Component</key>
		<real>1</real>
		<key>Blue Component</key>
		<real>0.19999992847442627</real>

## 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)
	function walle
	set light_blue C7E2E8
	set gray C1C1BD
	set light_gray EEE
	set dark_gray 565653
	set bronze C6973F
	set yellow FFE500
	set lime C6E80B
	set brown 8C5D05
	<?xml version="1.0" encoding="UTF-8"?>
	<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
	<plist version="1.0">
	<dict>
	<key>Ansi 0 Color</key>
	<dict>
	<key>Alpha Component</key>
	<real>1</real>
	<key>Blue Component</key>
	<real>0.19999992847442627</real>
	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!