mthomure

(ns hello-quil.noisy-spirals
  (:require [quil.core :as q]
            [hello-quil.noisy-spirals-dynamic :as d]
            [clojure.core.async :as a]
            [overtone.midi :as midi]
            [mojion.leap :as leap]))

(q/defsketch gen-art-14
  :title "100 Noisy Spirals"
  :size [d/SIZE d/SIZE]

mthomure

from cPickle import load as load_pickle
from glimpse.experiment import Layer, ExtractFeatures, GetTrainingSet

# load an experiment object from disk, as saved by the 'glab' command.
def load_experiment(path):
    with open(path) as f:
        # load saved experiment. return type is described here:
        # https://github.com/mthomure/glimpse-project/blob/master/glimpse/experiment/experiment.py#L77
        return load_pickle(f)

mthomure

from glimpse.experiment import *

def make_model_for_protos(protos):
    """Create a model for an existing set of prototypes."""
    model = Model()
    # Here, we assume that all prototypes have the same spatial extent.
    model.s2_kernels = [protos]
    return model

def train_classifier(model, pool, images, labels):

mthomure

# This script illustrates how to apply a trained image classifier to a new set
# of images.
# Author: Mick Thomure
# Date: 12/29/2013  

from glimpse.experiment import *
from glimpse.pools import *

def PredictImageClasses(exp, images, pool):
  """Apply existing model to a new set of images.