No models fix
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15
src/suite.py
15
src/suite.py
@ -360,9 +360,7 @@ class CVSuite:
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# Normalise data using loaded scalers
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for idx, value in enumerate(data):
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d = np.array(value)
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data[idx] = self.scaler[idx].transform(d.astype(np.float32).reshape(1, -1))[
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0
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][0]
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data[idx] = self.scaler[idx].transform(d.astype(np.float32).reshape(1, -1))[0][0]
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data = np.array([data], dtype=np.float32)
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@ -397,13 +395,19 @@ class CVSuite:
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modelnr = (idx * 2) + idy
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guesses = self.models[modelnr][2]
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for guess in guesses:
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print(guess)
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# Convert guess array
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tag_true = [guess[0] for guess in guesses ]
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tag_predict = [guess[1] for guess in guesses ]
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labels = [Tree(tag).name for tag in range(0, 8)]
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sns.heatmap(confusion_matrix(tag_true, tag_predict), xticklabels=labels, yticklabels=labels, ax=graph, annot=True, cbar=False, fmt='g')
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cm = confusion_matrix(tag_true, tag_predict)
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cmn = cmn = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
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sns.heatmap(cmn, xticklabels=labels, yticklabels=labels, ax=graph, annot=True, cbar=False, fmt='.2f')
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graph.set_title(self.models[modelnr][0])
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graph.set_xlabel("Predicted")
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graph.set_ylabel("Actual")
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@ -508,7 +512,7 @@ class CVSuite:
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img_sobel = img_gray
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self.canvas.add(img_sobel, "Sobel_edge")
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self.log.add("Sobel nonzero", cv2.countNonZero(img_sobel))
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# self.log.add("Sobel nonzero", cv2.countNonZero(img_sobel))
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# Canny edge
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img_canny = cv2.Canny(image=img_blur, threshold1=ct1, threshold2=ct2)
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@ -551,6 +555,7 @@ class CVSuite:
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# Write results to CSV file
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if not part_update:
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if self.models != []:
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self.runTest(self.log.data)
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self.drawConfusionMatrix()
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