Пример #1
0
def process(training_file, test_file, check, draw):
    # Load training data.
    with open(training_file) as f:
        class_1 = array(pickle.load(f))
        class_2 = array(pickle.load(f))
        labels = pickle.load(f)
    model = bayes.BayesClassifier()
    model.train([class_1, class_2], [1, -1])

    # Load test data.
    with open(test_file) as f:
        class_1 = array(pickle.load(f))
        class_2 = array(pickle.load(f))
        labels = pickle.load(f)

    if check:
        n = class_1.shape[0]
        n_correct = 0
        n_correct += sum(model.classify(class_1)[0] == labels[:n])
        n_correct += sum(model.classify(class_2)[0] == labels[n:])
        print 'percent correct:', 100 * n_correct / float(2 * n)

    if draw:

        def classify(x, y, model=model):
            points = vstack((x, y))
            return model.classify(points.T)[0]

        imtools.plot_2d_boundary([-6, 6, -6, 6], [class_1, class_2], classify,
                                 [1, -1])
        show()
Пример #2
0
def process(training_file, test_file, check, draw):
    # Load training data.
    with open(training_file) as f:
        class_1 = pickle.load(f)
        class_2 = pickle.load(f)
        labels = pickle.load(f)
    model = knn.KnnClassifier(labels, vstack((class_1, class_2)))

    # Load test data.
    with open(test_file) as f:
        class_1 = pickle.load(f)
        class_2 = pickle.load(f)
        labels = pickle.load(f)

    if check:
        n = class_1.shape[0]
        n_correct = 0
        for i in range(n):
            if model.classify(class_1[i]) == labels[i]: n_correct += 1
            if model.classify(class_2[i]) == labels[n + i]: n_correct += 1
        print 'percent correct:', 100 * n_correct / float(2 * n)

    if draw:

        def classify(x, y, model=model):
            return array([model.classify([xx, yy]) for (xx, yy) in zip(x, y)])

        imtools.plot_2d_boundary([-6, 6, -6, 6], [class_1, class_2], classify,
                                 [1, -1])
        show()
Пример #3
0
def process(training_file, test_file, check, draw):
  # Load training data.
  with open(training_file) as f:
    class_1 = pickle.load(f)
    class_2 = pickle.load(f)
    labels = pickle.load(f)
  model = knn.KnnClassifier(labels, vstack((class_1, class_2)))

  # Load test data.
  with open(test_file) as f:
    class_1 = pickle.load(f)
    class_2 = pickle.load(f)
    labels = pickle.load(f)

  if check:
    n = class_1.shape[0]
    n_correct = 0
    for i in range(n):
      if model.classify(class_1[i]) == labels[i]: n_correct += 1
      if model.classify(class_2[i]) == labels[n + i]: n_correct += 1
    print 'percent correct:', 100 * n_correct / float(2 * n)

  if draw:
    def classify(x, y, model=model):
      return array([model.classify([xx, yy]) for (xx, yy) in zip(x, y)])
    imtools.plot_2d_boundary(
        [-6, 6, -6, 6], [class_1, class_2], classify, [1, -1])
    show()
Пример #4
0
def process(training_file, test_file, check, draw):
    # Load training data.
    with open(training_file) as f:
        class_1 = array(pickle.load(f))
        class_2 = array(pickle.load(f))
        labels = pickle.load(f)
    model = bayes.BayesClassifier()
    model.train([class_1, class_2], [1, -1])

    # Load test data.
    with open(test_file) as f:
        class_1 = array(pickle.load(f))
        class_2 = array(pickle.load(f))
        labels = pickle.load(f)

    if check:
        n = class_1.shape[0]
        n_correct = 0
        n_correct += sum(model.classify(class_1)[0] == labels[:n])
        n_correct += sum(model.classify(class_2)[0] == labels[n:])
        print "percent correct:", 100 * n_correct / float(2 * n)

    if draw:

        def classify(x, y, model=model):
            points = vstack((x, y))
            return model.classify(points.T)[0]

        imtools.plot_2d_boundary([-6, 6, -6, 6], [class_1, class_2], classify, [1, -1])
        show()
Пример #5
0
def process(training_file, test_file, check, draw):
    # Load training data.
    with open(training_file) as f:
        class_1 = pickle.load(f)
        class_2 = pickle.load(f)
        labels = pickle.load(f)

    # Convert data to lists for libsvm.
    class_1 = map(list, class_1)
    class_2 = map(list, class_2)
    labels = list(labels)
    samples = class_1 + class_2
    problem = svmutil.svm_problem(labels, samples)
    # Don't print to stdout, use radial basis functions.
    param = svmutil.svm_parameter('-q -t 2')
    model = svmutil.svm_train(problem, param)

    # Load test data.
    with open(test_file) as f:
        class_1 = pickle.load(f)
        class_2 = pickle.load(f)
        labels = pickle.load(f)

    class_1 = map(list, class_1)
    class_2 = map(list, class_2)
    labels = list(labels)

    if check:
        # Sadly, this prints to stdout too :-/
        svmutil.svm_predict(labels, class_1 + class_2,
                            model)  # Prints accuracy.

    if draw:

        def classify(x, y, model=model):
            return array(
                svmutil.svm_predict([0] * len(x), map(list, zip(x, y)),
                                    model)[0])

        imtools.plot_2d_boundary(
            [-6, 6, -6, 6], [array(class_1), array(class_2)], classify,
            [1, -1])
        show()
Пример #6
0
def process(training_file, test_file, check, draw):
  # Load training data.
  with open(training_file) as f:
    class_1 = pickle.load(f)
    class_2 = pickle.load(f)
    labels = pickle.load(f)

  # Convert data to lists for libsvm.
  class_1 = map(list, class_1)
  class_2 = map(list, class_2)
  labels = list(labels)
  samples = class_1 + class_2
  problem = svmutil.svm_problem(labels, samples)
  # Don't print to stdout, use radial basis functions.
  param = svmutil.svm_parameter('-q -t 2')
  model = svmutil.svm_train(problem, param)

  # Load test data.
  with open(test_file) as f:
    class_1 = pickle.load(f)
    class_2 = pickle.load(f)
    labels = pickle.load(f)

  class_1 = map(list, class_1)
  class_2 = map(list, class_2)
  labels = list(labels)

  if check:
    # Sadly, this prints to stdout too :-/
    svmutil.svm_predict(labels, class_1 + class_2, model)  # Prints accuracy.

  if draw:
    def classify(x, y, model=model):
      return array(svmutil.svm_predict([0] * len(x), map(list, zip(x, y)),
                                       model)[0])
    imtools.plot_2d_boundary(
        [-6, 6, -6, 6], [array(class_1), array(class_2)], classify, [1, -1])
    show()