def test_many_evaluation_results(self):
data = Table("iris")
learners = [
classification.MajorityLearner(),
classification.LogisticRegressionLearner(),
classification.TreeLearner(),
classification.SVMLearner(),
classification.KNNLearner(),
classification.CN2Learner(),
classification.SGDClassificationLearner(),
classification.RandomForestLearner(),
classification.NaiveBayesLearner(),
classification.SGDClassificationLearner()
]
res = evaluation.CrossValidation(data, learners, k=2, store_data=True)
# this is a mixin; pylint: disable=no-member
self.send_signal("Evaluation Results", res)
def test_many_evaluation_results(self):
"""
Now works with more than 9 evaluation results.
GH-2394 (ROC Analysis)
GH-2522 (Lift Curve, Calibration Plot)
"""
data = Table("iris")
learners = [
classification.MajorityLearner(),
classification.LogisticRegressionLearner(),
classification.TreeLearner(),
classification.SVMLearner(),
classification.KNNLearner(),
classification.CN2Learner(),
classification.SGDClassificationLearner(),
classification.RandomForestLearner(),
classification.NaiveBayesLearner(),
classification.SGDClassificationLearner(),
]
res = evaluation.CrossValidation(data, learners, k=2, store_data=True)
self.send_signal("Evaluation Results", res)
# print(testdata)
# Initialize Image Embedder
imemb = ImageEmbedder(model="squeezenet")
# Embed Training images
imembdata, skippedim, numskippedim = imemb(imdata, col="image")
# print(imembdata)
# print(skippedim)
# print(numskippedim)
# Initialize learner
# learner = classification.naive_bayes.NaiveBayesLearner()
# learner = classification.TreeLearner
learner = classification.KNNLearner()
# Train learner for model
lmodel = learner(imembdata)
# Set object for getting class values from data based on prediction
classval = imembdata.domain.class_var.values
# Set Thresholding type flag
threshtype = cv2.THRESH_BINARY
thresh = 30
# keep looping, until interrupted
while (True):
# get the current frame
(grabbed, frame) = camera.read()