| <html> | |
| <head> | |
| <title>GENE_FAMILY: HD-ZIP</title> | |
| <style> | |
| body { font-family: Arial, sans-serif; } | |
| .container { border: 2px solid #ddd; margin: 20px; padding: 20px; } | |
| .header { padding: 0.6em; background-color: #ffdddd; font-weight: bold; } | |
| .title { padding: 0.6em; background-color: #80c4e6bd; font-weight: bold; } | |
| .section { padding: 10px; } | |
| .metrics { display: flex; } | |
| .metrics ul { list-style: none; padding: 0; } | |
| .metrics ul + ul { margin-left: 2em; } | |
| .confusion-matrix img, .learning-curve img { width: 100%; max-width: 565px; } | |
| .class_dist { width: 20em; } | |
| .mod_sum { margin-bottom: 2em; } | |
| </style> | |
| </head> | |
| <body> | |
| <div class="container title"> | |
| <h3>GENE_FAMILY: <a href='https://planttfdb.gao-lab.org/family.php?fam=HD-ZIP' target='_blank'>HD-ZIP</a></h3> | |
| </div> | |
| <div class="container"> | |
| <div class="header">MODEL: FEEDFORWARD_k2</div> | |
| <div style="display: flex;"> | |
| <div class="section"> | |
| <h2 class='mod_sum'>Model Architecture</h2> | |
| <pre>Model: "FEEDFORWARD_k2" | |
| ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ | |
| ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ | |
| ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ | |
| │ dense (Dense) │ (None, 256) │ 113,152 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout (Dropout) │ (None, 256) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_1 (Dense) │ (None, 128) │ 32,896 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout_1 (Dropout) │ (None, 128) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_2 (Dense) │ (None, 64) │ 8,256 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout_2 (Dropout) │ (None, 64) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_3 (Dense) │ (None, 32) │ 2,080 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout_3 (Dropout) │ (None, 32) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_4 (Dense) │ (None, 1) │ 33 │ | |
| └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘ | |
| Total params: 469,253 (1.79 MB) | |
| Trainable params: 156,417 (611.00 KB) | |
| Non-trainable params: 0 (0.00 B) | |
| Optimizer params: 312,836 (1.19 MB) | |
| </pre> | |
| </div> | |
| <div class="section learning-curve"> | |
| <h2>Learning Curve</h2> | |
| <img src="data:image/png;base64,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" alt="Learning Curve"> | |
| </div> | |
| </div> | |
| <div style="display: flex;"> | |
| <div class="section class_dist"> | |
| <h2>Class Distribution</h2> | |
| <pre> Count Percentage | |
| class | |
| 1 2504 50.08 | |
| 0 2496 49.92</pre> | |
| <h3>Additional Metrics</h3> | |
| <ul> | |
| <li>Total Samples: 5000</li> | |
| <li>Imbalance Ratio: 1.00</li> | |
| </ul> | |
| </div> | |
| <div class="section"> | |
| <h2>Classification Report</h2> | |
| <pre> precision recall f1-score support | |
| Class 0 0.9918 0.9739 0.9828 499 | |
| Class 1 0.9745 0.9920 0.9832 501 | |
| accuracy 0.9830 1000 | |
| macro avg 0.9832 0.9830 0.9830 1000 | |
| weighted avg 0.9832 0.9830 0.9830 1000 | |
| </pre> | |
| <h3>Metrics</h3> | |
| <div class="metrics"> | |
| <ul> | |
| <li>True Positives (TP): 497</li> | |
| <li>True Negatives (TN): 486</li> | |
| </ul> | |
| <ul> | |
| <li>False Positives (FP): 13</li> | |
| <li>False Negatives (FN): 4</li> | |
| </ul> | |
| </div> | |
| </div> | |
| <div class="section confusion-matrix"> | |
| <h2>Confusion Matrix</h2> | |
| <img src="data:image/png;base64,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" alt="Confusion Matrix"> | |
| </div> | |
| </div> | |
| </div> | |
| <div class="container"> | |
| <div class="header">MODEL: FEEDFORWARD_k3</div> | |
| <div style="display: flex;"> | |
| <div class="section"> | |
| <h2 class='mod_sum'>Model Architecture</h2> | |
| <pre>Model: "FEEDFORWARD_k3" | |
| ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ | |
| ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ | |
| ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ | |
| │ dense_5 (Dense) │ (None, 256) │ 2,235,136 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout_4 (Dropout) │ (None, 256) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_6 (Dense) │ (None, 128) │ 32,896 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout_5 (Dropout) │ (None, 128) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_7 (Dense) │ (None, 64) │ 8,256 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout_6 (Dropout) │ (None, 64) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_8 (Dense) │ (None, 32) │ 2,080 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout_7 (Dropout) │ (None, 32) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_9 (Dense) │ (None, 1) │ 33 │ | |
| └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘ | |
| Total params: 6,835,205 (26.07 MB) | |
| Trainable params: 2,278,401 (8.69 MB) | |
| Non-trainable params: 0 (0.00 B) | |
| Optimizer params: 4,556,804 (17.38 MB) | |
| </pre> | |
| </div> | |
| <div class="section learning-curve"> | |
| <h2>Learning Curve</h2> | |
| <img src="data:image/png;base64,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" alt="Learning Curve"> | |
| </div> | |
| </div> | |
| <div style="display: flex;"> | |
| <div class="section class_dist"> | |
| <h2>Class Distribution</h2> | |
| <pre> Count Percentage | |
| class | |
| 1 2504 50.08 | |
| 0 2496 49.92</pre> | |
| <h3>Additional Metrics</h3> | |
| <ul> | |
| <li>Total Samples: 5000</li> | |
| <li>Imbalance Ratio: 1.00</li> | |
| </ul> | |
| </div> | |
| <div class="section"> | |
| <h2>Classification Report</h2> | |
| <pre> precision recall f1-score support | |
| Class 0 0.9980 1.0000 0.9990 499 | |
| Class 1 1.0000 0.9980 0.9990 501 | |
| accuracy 0.9990 1000 | |
| macro avg 0.9990 0.9990 0.9990 1000 | |
| weighted avg 0.9990 0.9990 0.9990 1000 | |
| </pre> | |
| <h3>Metrics</h3> | |
| <div class="metrics"> | |
| <ul> | |
| <li>True Positives (TP): 500</li> | |
| <li>True Negatives (TN): 499</li> | |
| </ul> | |
| <ul> | |
| <li>False Positives (FP): 0</li> | |
| <li>False Negatives (FN): 1</li> | |
| </ul> | |
| </div> | |
| </div> | |
| <div class="section confusion-matrix"> | |
| <h2>Confusion Matrix</h2> | |
| <img src="data:image/png;base64,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" alt="Confusion Matrix"> | |
| </div> | |
| </div> | |
| </div> | |
| <div class="container"> | |
| <div class="header">MODEL: FEEDFORWARD_k4</div> | |
| <div style="display: flex;"> | |
| <div class="section"> | |
| <h2 class='mod_sum'>Model Architecture</h2> | |
| <pre>Model: "FEEDFORWARD_k4" | |
| ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ | |
| ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ | |
| ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ | |
| │ dense_10 (Dense) │ (None, 256) │ 36,379,392 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout_8 (Dropout) │ (None, 256) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_11 (Dense) │ (None, 128) │ 32,896 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout_9 (Dropout) │ (None, 128) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_12 (Dense) │ (None, 64) │ 8,256 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout_10 (Dropout) │ (None, 64) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_13 (Dense) │ (None, 1) │ 65 │ | |
| └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘ | |
| Total params: 109,261,829 (416.80 MB) | |
| Trainable params: 36,420,609 (138.93 MB) | |
| Non-trainable params: 0 (0.00 B) | |
| Optimizer params: 72,841,220 (277.87 MB) | |
| </pre> | |
| </div> | |
| <div class="section learning-curve"> | |
| <h2>Learning Curve</h2> | |
| <img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABMW0lEQVR4nO3dfZyM9f7H8fc1szd21+6ylr1xXyh3SaiQhBC1ESIUThTduTvlpo7Qr5LOSSqHqHA6h5KoIzlJSG7qJNmOUElYsttaaW/Zm5nr98fasWN3WfZmdud6PR+Pecxc3+t7Xddnrp2d6z3XzYxhmqYpAAAAWIbN0wUAAACgfBEAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGB9PF1CZOZ1OHT9+XMHBwTIMw9PlAACAYjBNU6mpqYqOjpbNZs19YQTAEjh+/Ljq1q3r6TIAAMBlOHr0qOrUqePpMjyCAFgCwcHBknJfQCEhIR6uBgAAFEdKSorq1q3r2o5bEQGwBPIO+4aEhBAAAQCoZKx8+pY1D3wDAABYGAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYjNcEwC+++EIxMTGKjo6WYRj68MMPLzrNli1b1KZNG1WpUkVXXHGFXn/99bIvFAAAwMO8JgCmp6erVatWmjdvXrH6Hzp0SL1791anTp20e/duPfnkkxo7dqxWrVpVxpWiPCWkJ+jr+K+VkJ7g6VKKRI2lo6LXWNHrk6ixtFBj6agMNVZmXvNbwL169VKvXr2K3f/1119XvXr1NHfuXElS06ZN9c033+hvf/ub+vfvX0ZVepfjv+xR/A/fKurq6xR9RUtPl1PA6gOrNfPLmXKaTtkMm6a3n65+jft5uiw3qw+s1mvrZyjid4d+C7PrsZ4zqPEyVPQaK3p9EjWWFmosHZWhxsrOME3T9HQRpc0wDH3wwQfq27dvkX1uvvlmtW7dWq+88oqr7YMPPtDAgQOVkZEhX1/fAtNkZmYqMzPTNZySkqK6desqOTlZISEhpfocEtITFJcSp3oh9RQZFHnJ0zucDmU6MpXlyHLdZzmz3NoKjHdkKTPrtBwZ6XKkpcqZni4zI0Nm+mkpI0NGRqZ0+ozsGZmq/lOCmv54WoYkU9Leq6oosX6InHZDOTYpxy457IYcNslxdjjHJjnsucPZZ+9zbGa+/rl9cgwzt79dyjacctgNZdtM5dicMiWZMmWaZqH3TtOZO+w05ZBDYSmmok6Ziq9u6PcQQ4aMsvnxb9OUIcnmlAxTspkFH9vOPs5rMxymbtzv0OAvTNlMyWlI73c0tKuxXfYLzauI+Rum6Tb//Ms8f3qj0PrMguOdUr1Ep1oeketv/X196Vi4LbehIjClOklOtaioNVb0+qRCa/xfA+lIhE2mTTKN3Ndn7s2Q05b72DQk53nj89pc/fP3NSSzkOmdhnt/s7B5SWpzwKG7t537f1l5U+7/i01F/U8U738m/2v+Yv8z7o/dpzecUv3fnLrm8Ln1+F1DKa5W8daj+3oqv/W4opOh/15tL5v3xstgmqZu+MGhQVvP1fhGL7um/N9nl7U9LExKSopCQ0PLZPtdWVg2ADZp0kQjRozQk08+6WrbsWOHOnbsqOPHjysqKqrANDNmzNDMmTMLtJf2C+iJLU9o5/8+UeQpp+KrG6rf6DpFBUWdC2vOrHOPczLlyDwjI+OM7Kezzt6y5ZeZo4AsKSBLqpIlBWRKAVlm7uPChs/2q5Jdak+jTOTkC5T5710hM99w1dNS1Klzb8QJ1aWUwGJsGC5xY5B3AwCUDYchnVkxV22v6Vkq8yMAetEh4Mtx/qedvCxc1KegqVOnauLEia7hvD2ApSkhPUEN532sP+0xXcHlx9o7lVzVUJV8oS0wX7CzWyh8+Dhzb/45lzadodwwGHWqTMoCAJQhuylFnLLQxq4cWDYARkZGKiHB/cTSxMRE+fj4qEaNGoVO4+/vL39//zKt6+jBWN1yNvxJucHl6l+l3ChY+px2m5wBfnIG+MsMrCIFVJECA2QEBcoIDJStalXZg6rKp2pV+VQNlm/VEKWfTlbOrHluVxA5Danq9MkKC68jMydHZnaOzOxsmTnZMnNypJyzw9k5ueNzsmVmZ59tP9uWne0ap5wcmVl5w/nHZ0sF+udI2dnuw07nhZ+4zSbZzx7yyH9vs50bd/axcXZYNkOGzV54m90mwzhvnnabZNhyx+VNZ7dJttzpzMwspW7a5HYU0JQU2udO2YOqutdwkXm5arAV3ZY3D7fp7PbC284+x5xTf+jY2LEy8h0oMA1DdRfMl08R/yflLefkSR196OEKW2NFr08qusY6c+fKXr2a5HTKdDgkpyk5HTIdTsksqs2ZO+x0Sg6nTGe+PoW1OZy583c6JLd5OM/2d8g0nXKmpil57dqC/y933CFbcNVz/38X+5+5aFve/4Bdhs0o9P+iqDbHH6d0bPyEQtbjy7JXq16MdVd0W8H1c259XrzNvOh6DOnRQ7aAgDJ+pRWP8/RppXz6qXuNNkORV13rqZK8kmUDYPv27fXRRx+5tX366adq27Ztoef/lZfI302lXaSPERAgW1CQ7EFBshV1q3rucZH9qlaV4ed3Wed9bEz7VRHzPpDdzN01/9ujd6nbPSMu6zmXhaxff9XB7j3cg6DNpis/2yDfqKgKc67LH++/r/inp+fWabMp+pmZqjZggKfLchP9f88UqDH4lls8XZabil5jRa9PKrzGkJ49PF2Wm6Abb6iU/y8hPUvnsGVpqQzrsbD3Rt/I0jn/D7m85hzAtLQ0/fzzz5Kk1q1ba86cOerSpYvCwsJUr149TZ06Vb/++qvefvttSblfA9OiRQuNHj1aDzzwgL788kuNGTNG77zzTrGvAi6LcwiyExJ0oEtX90+QNkMN310hv4YNZAsMzP3UWQEc/2WPEn7crcirWlfIq4DPfwOJqoBvclLu3zzrSJz86tersG9w1FhyFb0+iRpLCzWWjrKskXMAvSgAfv755+rSpUuB9uHDh2vp0qUaMWKEDh8+rM8//9w1bsuWLZowYYL27t2r6OhoTZ48WWPGjCn2MsvqBVRZgktlUBne5AAA5YsA6EUB0BPK8gVEcAEAoGwQAC18DmBF5xsZSfADAABlwmt+Cg4AAADFQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFiMVwXA+fPnq2HDhqpSpYratGmjrVu3XrD/smXL1KpVKwUGBioqKkp/+tOfdPLkyXKqFgAAwDO8JgCuWLFC48eP11NPPaXdu3erU6dO6tWrl+Li4grtv23bNg0bNkwjR47U3r17tXLlSu3cuVOjRo0q58oBAADKl9cEwDlz5mjkyJEaNWqUmjZtqrlz56pu3bpasGBBof2/+uorNWjQQGPHjlXDhg110003afTo0frmm2/KuXIAAIDy5RUBMCsrS7t27VKPHj3c2nv06KEdO3YUOk2HDh107NgxrVu3TqZp6rffftP777+v22+/vTxKBgAA8BivCIBJSUlyOByKiIhwa4+IiFBCQkKh03To0EHLli3ToEGD5Ofnp8jISFWrVk2vvfZakcvJzMxUSkqK2w0AAKCy8YoAmMcwDLdh0zQLtOXZt2+fxo4dq6efflq7du3SJ598okOHDmnMmDFFzn/WrFkKDQ113erWrVuq9QMAAJQHwzRN09NFlFRWVpYCAwO1cuVK3XXXXa72cePGKTY2Vlu2bCkwzX333aczZ85o5cqVrrZt27apU6dOOn78uKKiogpMk5mZqczMTNdwSkqK6tatq+TkZIWEhJTyswIAAGUhJSVFoaGhlt5+e8UeQD8/P7Vp00YbNmxwa9+wYYM6dOhQ6DQZGRmy2dyfvt1ul5S757Aw/v7+CgkJcbsBAABUNl4RACVp4sSJevPNN7V48WLt379fEyZMUFxcnOuQ7tSpUzVs2DBX/5iYGK1evVoLFizQL7/8ou3bt2vs2LG6/vrrFR0d7amnAQAAUOZ8PF1AaRk0aJBOnjypZ555RvHx8WrRooXWrVun+vXrS5Li4+PdvhNwxIgRSk1N1bx58/TnP/9Z1apVU9euXTV79mxPPQUAAIBy4RXnAHoK5xAAAFD5sP32okPAAAAAKB4CIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgfTxcAAMClcDgcys7O9nQZqMB8fX1lt9s9XUaFRgAEAFQKpmkqISFBf/zxh6dLQSVQrVo1RUZGyjAMT5dSIREAAQCVQl74q1WrlgIDA9mwo1CmaSojI0OJiYmSpKioKA9XVDERAAEAFZ7D4XCFvxo1ani6HFRwAQEBkqTExETVqlWLw8GF4CIQAECFl3fOX2BgoIcrQWWR91rhfNHCEQABAJUGh31RXLxWLowACAAAYDFeFQDnz5+vhg0bqkqVKmrTpo22bt16wf6ZmZl66qmnVL9+ffn7++vKK6/U4sWLy6laAIAV3HLLLRo/frynywDceM1FICtWrND48eM1f/58dezYUQsXLlSvXr20b98+1atXr9BpBg4cqN9++01vvfWWGjVqpMTEROXk5JRz5QAAAOXLawLgnDlzNHLkSI0aNUqSNHfuXK1fv14LFizQrFmzCvT/5JNPtGXLFv3yyy8KCwuTJDVo0KA8SwYAAPAIrzgEnJWVpV27dqlHjx5u7T169NCOHTsKnWbNmjVq27atXnzxRdWuXVtNmjTR448/rtOnT5dHyQAAD4pPPq0dB5MUn1y+7/mnTp3SsGHDVL16dQUGBqpXr146cOCAa/yRI0cUExOj6tWrKygoSM2bN9e6detc0w4dOlQ1a9ZUQECAGjdurCVLlpRr/fAeXrEHMCkpSQ6HQxEREW7tERERSkhIKHSaX375Rdu2bVOVKlX0wQcfKCkpSQ8//LB+//33Is8DzMzMVGZmpms4JSWl9J4EAKBcrNgZp6mr98hpSjZDmtWvpQa1K/xUodI2YsQIHThwQGvWrFFISIgmT56s3r17a9++ffL19dUjjzyirKwsffHFFwoKCtK+fftUtWpVSdK0adO0b98+/ec//1F4eLh+/vlndlrgsnlFAMxz/iXfpmkWeRm40+mUYRhatmyZQkNDJeUeRh4wYID+/ve/u75EMr9Zs2Zp5syZpV84AOCyxLy2TSdSMy/e8SyH09SJtHP9naY0edUe/W39T7Lbiv+1ITWD/fXRYzddUq15wW/79u3q0KGDJGnZsmWqW7euPvzwQ919992Ki4tT//791bJlS0nSFVdc4Zo+Li5OrVu3Vtu2bSVx2hJKxisCYHh4uOx2e4G9fYmJiQX2CuaJiopS7dq1XeFPkpo2bSrTNHXs2DE1bty4wDRTp07VxIkTXcMpKSmqW7duKT0LAMClOpGaqYSUMyWfT1rxQ+Tl2r9/v3x8fHTDDTe42mrUqKGrrrpK+/fvlySNHTtWDz30kD799FPdeuut6t+/v6655hpJ0kMPPaT+/fvr22+/VY8ePdS3b19XkAQulVecA+jn56c2bdpow4YNbu0bNmwo8p+jY8eOOn78uNLS0lxtP/30k2w2m+rUqVPoNP7+/goJCXG7AQA8p2awvyJDqhT7VrOqf+HzqXqJ8wkufD4XYppmke15R6tGjRqlX375Rffdd5/27Nmjtm3b6rXXXpMk9erVS0eOHNH48eN1/PhxdevWTY8//vgl1wFIkkwv8e6775q+vr7mW2+9Ze7bt88cP368GRQUZB4+fNg0TdOcMmWKed9997n6p6ammnXq1DEHDBhg7t2719yyZYvZuHFjc9SoUcVeZnJysinJTE5OLvXnAwA45/Tp0+a+ffvM06dPl3he7359xLxiysdm/clrzSumfGy++/WRUqiwaJ07dzbHjRtn/vTTT6Ykc/v27a5xSUlJZkBAgLly5cpCp50yZYrZsmXLQse9/vrrZnBwcJnU7A0u9Jph+22aXnEIWJIGDRqkkydP6plnnlF8fLxatGihdevWqX79+pKk+Ph4xcXFufpXrVpVGzZs0GOPPaa2bduqRo0aGjhwoJ599llPPQUAQDkY1K6ebm5SU4eTMtQgPFBRoQXP+S4LjRs3Vp8+ffTAAw9o4cKFCg4O1pQpU1S7dm316dNHkjR+/Hj16tVLTZo00alTp7Rp0yY1bdpUkvT000+rTZs2at68uTIzM7V27VrXOOBSeU0AlKSHH35YDz/8cKHjli5dWqDt6quvLnDYGADg/aJCA8ot+OW3ZMkSjRs3TnfccYeysrJ08803a926dfL19ZUkORwOPfLIIzp27JhCQkJ022236eWXX5aUe7rT1KlTdfjwYQUEBKhTp0569913y/05wDsYplnESQm4qJSUFIWGhio5OZnzAQGgDJ05c0aHDh1y/dwncDEXes2w/faSi0AAAABQfARAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMR4PgDk5Ofrss8+0cOFCpaamSpKOHz+utLQ0D1cGAIDnNWjQQHPnzi1WX8Mw9OGHH5ZpPfAOHv0t4CNHjui2225TXFycMjMz1b17dwUHB+vFF1/UmTNn9Prrr3uyPAAAAK/k0T2A48aNU9u2bXXq1CkFBJz7Ue677rpLGzdu9GBlAAAA3sujAXDbtm36y1/+Ij8/P7f2+vXr69dff/VQVQAAr5f8q3Toi9z7MrRw4ULVrl1bTqfTrf3OO+/U8OHDdfDgQfXp00cRERGqWrWq2rVrp88++6zUlr9nzx517dpVAQEBqlGjhh588EG3U6w+//xzXX/99QoKClK1atXUsWNHHTlyRJL03XffqUuXLgoODlZISIjatGmjb775ptRqg2d5NAA6nU45HI4C7ceOHVNwcLAHKgIAeL1v35bmtpD+EZN7/+3bZbaou+++W0lJSdq8ebOr7dSpU1q/fr2GDh2qtLQ09e7dW5999pl2796tnj17KiYmRnFxcSVedkZGhm677TZVr15dO3fu1MqVK/XZZ5/p0UcflZR7Dn7fvn3VuXNn/e9//9OXX36pBx98UIZhSJKGDh2qOnXqaOfOndq1a5emTJkiX1/fEteFisGj5wB2795dc+fO1aJFiyTlnryalpam6dOnq3fv3p4sDQBQGSzsLKUlFr+/0yGl/3Zu2HRKax6TNj4r2ezFn0/VWtLoLRftFhYWpttuu03Lly9Xt27dJEkrV65UWFiYunXrJrvdrlatWrn6P/vss/rggw+0Zs0aV1C7XMuWLdPp06f19ttvKygoSJI0b948xcTEaPbs2fL19VVycrLuuOMOXXnllZKkpk2buqaPi4vTE088oauvvlqS1Lhx4xLVg4rFowHw5ZdfVpcuXdSsWTOdOXNGQ4YM0YEDBxQeHq533nnHk6UBACqDtEQp9XjJ55M/FJayoUOH6sEHH9T8+fPl7++vZcuW6Z577pHdbld6erpmzpyptWvX6vjx48rJydHp06dLZQ/g/v371apVK1f4k6SOHTvK6XTqxx9/1M0336wRI0aoZ8+e6t69u2699VYNHDhQUVFRkqSJEydq1KhR+uc//6lbb71Vd999tysoovLz6CHg6OhoxcbG6vHHH9fo0aPVunVrvfDCC9q9e7dq1arlydIAAJVB1VpScHTxb0ERhc8nKOLS5lO1+NuomJgYOZ1Offzxxzp69Ki2bt2qe++9V5L0xBNPaNWqVXruuee0detWxcbGqmXLlsrKyirxqjFN03U493x57UuWLNGXX36pDh06aMWKFWrSpIm++uorSdKMGTO0d+9e3X777dq0aZOaNWumDz74oMR1oWLw6B5ASQoICND999+v+++/39OlAAAqm2Ichi3g27elj8ZLpkMy7FLMXOm6YaVdmUtAQID69eunZcuW6eeff1aTJk3Upk0bSdLWrVs1YsQI3XXXXZKktLQ0HT58uFSW26xZM/3jH/9Qenq6ay/g9u3bZbPZ1KRJE1e/1q1bq3Xr1po6darat2+v5cuX68Ybb5QkNWnSRE2aNNGECRM0ePBgLVmyxFUrKjePBsC3377wibfDhpXdPyQAwKKuGyZd2U36/Rcp7AoptHaZL3Lo0KGKiYnR3r17XXv/JKlRo0ZavXq1YmJiZBiGpk2bVuCK4ZIsc/r06Ro+fLhmzJihEydO6LHHHtN9992niIgIHTp0SIsWLdKdd96p6Oho/fjjj/rpp580bNgwnT59Wk888YQGDBighg0b6tixY9q5c6f69+9fKrXB8zwaAMeNG+c2nJ2drYyMDPn5+SkwMJAACAAoG6G1yyX45enatavCwsL0448/asiQIa72l19+Wffff786dOig8PBwTZ48WSkpKaWyzMDAQK1fv17jxo1Tu3btFBgYqP79+2vOnDmu8T/88IP+8Y9/6OTJk4qKitKjjz6q0aNHKycnRydPntSwYcP022+/KTw8XP369dPMmTNLpTZ4nmGapunpIvI7cOCAHnroIT3xxBPq2bOnp8u5oJSUFIWGhio5OVkhISGeLgcAvNaZM2d06NAhNWzYUFWqVPF0OagELvSaYftdAX4L+HyNGzfWCy+8UGDvIAAAAEpHhQuAkmS323X8eClc1g8AgJdYtmyZqlatWuitefPmni4PlYxHzwFcs2aN27BpmoqPj9e8efPUsWNHD1UFAEDFc+edd+qGG24odBy/0IFL5dEA2LdvX7dhwzBUs2ZNde3aVS+99JJnigIAoAIKDg7mZ1JRajwaAEvrUncAAAAUX4U8BxAAAABlp9z3AE6cOLHYffO+qwgAAAClp9wD4O7du4vVr6jfLwQAAEDJlHsA3Lx5c3kvEgAAAPlwDiAAAIDFePQqYEnauXOnVq5cqbi4OGVlZbmNW716tYeqAgAA8F4e3QP47rvvqmPHjtq3b58++OADZWdna9++fdq0aZNCQ0M9WRoAAF4rOzvb0yXAwzwaAJ9//nm9/PLLWrt2rfz8/PTKK69o//79GjhwoOrVq+fJ0gAAXiwhPUFfx3+thPSEclneJ598optuuknVqlVTjRo1dMcdd+jgwYOu8ceOHdM999yjsLAwBQUFqW3btvrvf//rGr9mzRq1bdtWVapUUXh4uPr16+caZxiGPvzwQ7flVatWTUuXLpUkHT58WIZh6L333tMtt9yiKlWq6F//+pdOnjypwYMHq06dOgoMDFTLli31zjvvuM3H6XRq9uzZatSokfz9/VWvXj0999xzkqSuXbvq0Ucfdet/8uRJ+fv7a9OmTaWx2lCGPBoADx48qNtvv12S5O/vr/T0dBmGoQkTJmjRokWeLA0A4KVWH1itnqt6auSnI9VzVU+tPlD2pxulp6dr4sSJ2rlzpzZu3Cibzaa77rpLTqdTaWlp6ty5s44fP641a9bou+++06RJk1w/lvDxxx+rX79+uv3227V7925t3LhRbdu2veQaJk+erLFjx2r//v3q2bOnzpw5ozZt2mjt2rX6/vvv9eCDD+q+++5zC55Tp07V7NmzNW3aNO3bt0/Lly9XRESEJGnUqFFavny5MjMzXf2XLVum6OhodenSpYRrDGXNo+cAhoWFKTU1VZJUu3Ztff/992rZsqX++OMPZWRkeLI0AEAlMGjtICWdTip2f4fToZNnTrqGnaZT03dM16vfviq7zV7s+YQHhGvFHSuK3b9///5uw2+99ZZq1aqlffv2aceOHTpx4oR27typsLAwSVKjRo1cfZ977jndc889mjlzpqutVatWxV52nvHjx7vtOZSkxx9/3PX4scce0yeffKKVK1fqhhtuUGpqql555RXNmzdPw4cPlyRdeeWVuummm1zP6bHHHtO///1vDRw4UJK0ZMkSjRgxgq9yqwQ8EgBjY2N17bXXqlOnTtqwYYNatmypgQMHaty4cdq0aZM2bNigbt26eaI0AEAlknQ6SYkZiSWeT/5QWBYOHjyoadOm6auvvlJSUpJr715cXJxiY2PVunVrV/g7X2xsrB544IES13D+XkOHw6EXXnhBK1as0K+//qrMzExlZmYqKChIkrR//35lZmYWuT329/fXvffeq8WLF2vgwIGKjY3Vd999V+BwNComjwTA6667Tq1bt1bfvn01ePBgSbm7mX19fbVt2zb169dP06ZN80RpAIBKJDwg/JL6n78HME+NKjUueQ/gpYiJiVHdunX1xhtvKDo6Wk6nUy1atFBWVpYCAgIuOO3FxhuGIdM03doKu8gjL9jleemll/Tyyy9r7ty5atmypYKCgjR+/HjXN3JcbLlS7mHga6+9VseOHdPixYvVrVs31a9f/6LTwfM8EgC3b9+uxYsX629/+5tmzZqlfv36aeTIkZo0aZImTZrkiZIAAJXQpRyGzbP6wGrN/HKmnKZTNsOm6e2nq1/jfhef8DKdPHlS+/fv18KFC9WpUydJ0rZt21zjr7nmGr355pv6/fffC90LeM0112jjxo3605/+VOj8a9asqfj4eNfwgQMHinUa1datW9WnTx/de++9knIv+Dhw4ICaNm0qSWrcuLECAgK0ceNGjRo1qtB5tGzZUm3bttUbb7yh5cuX67XXXrvoclExeCQAtm/fXu3bt9err76q9957T0uWLNGtt96qBg0a6P7779fw4cNVp04dT5QGAPBy/Rr3U4foDjqaelR1g+sqMiiyTJdXvXp11ahRQ4sWLVJUVJTi4uI0ZcoU1/jBgwfr+eefV9++fTVr1ixFRUVp9+7dio6OVvv27TV9+nR169ZNV155pe655x7l5OToP//5j2uHSdeuXTVv3jzdeOONcjqdmjx5snx9fS9aV6NGjbRq1Srt2LFD1atX15w5c5SQkOAKgFWqVNHkyZM1adIk+fn5qWPHjjpx4oT27t2rkSNHuuYzatQoPfroowoMDNRdd91VymsPZcWjVwEHBARo+PDh+vzzz/XTTz9p8ODBWrhwoRo2bKjevXt7sjQAgBeLDIpUu8h2ZR7+JMlms+ndd9/Vrl271KJFC02YMEF//etfXeP9/Pz06aefqlatWurdu7datmypF154QXZ77iHpW265RStXrtSaNWt07bXXqmvXrm5X6r700kuqW7eubr75Zg0ZMkSPP/64AgMDL1rXtGnTdN1116lnz5665ZZbFBkZqb59+xbo8+c//1lPP/20mjZtqkGDBikx0f2cy8GDB8vHx0dDhgxRlSpVSrCmUJ4M8/wTBzwoLS1Ny5Yt05NPPqk//vhDDofD0yVdUEpKikJDQ5WcnKyQkBBPlwMAXuvMmTM6dOiQGjZsSMioYI4ePaoGDRpo586duu666zxdjsuFXjNsvyvAT8FJ0pYtW7R48WKtWrVKdrtdAwcOdNu9DAAAKpbs7GzFx8drypQpuvHGGytU+MPFeSwAHj16VEuXLtXSpUt16NAhdejQQa+99poGDhxY4EolAABQsWzfvl1dunRRkyZN9P7773u6HFwijwTA7t27a/PmzapZs6aGDRum+++/X1dddZUnSgEAAJfhlltuKfD1M6g8PBIAAwICtGrVKt1xxx2uk1wBAABQPjwSANesWeOJxQIAAEAe/hoYAAAAlD8CIAAAgMUQAAEAACyGAAgAAGAxBEAAACqwBg0aaO7cuZ4uA16GAAgAAGAxBEAAAFAmHA6HnE6np8tAIQiAAADLyU5IUPpX/1V2QkKZLmfhwoWqXbt2gRB05513avjw4Tp48KD69OmjiIgIVa1aVe3atdNnn3122cubM2eOWrZsqaCgINWtW1cPP/yw0tLS3Pps375dnTt3VmBgoKpXr66ePXvq1KlTkiSn06nZs2erUaNG8vf3V7169fTcc89Jkj7//HMZhqE//vjDNa/Y2FgZhqHDhw9LkpYuXapq1app7dq1atasmfz9/XXkyBHt3LlT3bt3V3h4uEJDQ9W5c2d9++23bnX98ccfevDBBxUREaEqVaqoRYsWWrt2rdLT0xUSElLg5+Y++ugjBQUFKTU19bLXl5URAAEAlvLH++/r567dFDdihH7u2k1/lOHv2N59991KSkrS5s2bXW2nTp3S+vXrNXToUKWlpal379767LPPtHv3bvXs2VMxMTGKi4u7rOXZbDa9+uqr+v777/WPf/xDmzZt0qRJk1zjY2Nj1a1bNzVv3lxffvmltm3bppiYGDkcDknS1KlTNXv2bE2bNk379u3T8uXLFRERcUk1ZGRkaNasWXrzzTe1d+9e1apVS6mpqRo+fLi2bt2qr776So0bN1bv3r1d4c3pdKpXr17asWOH/vWvf2nfvn164YUXZLfbFRQUpHvuuUdLlixxW86SJUs0YMAABQcHX9a6sjrD9KIf8ps/f77++te/Kj4+Xs2bN9fcuXPVqVOni06X92moRYsWio2NLfbyUlJSFBoaquTkZIWEhJSgcgDAhZw5c0aHDh1Sw4YNVaVKFVf7of4DlJOUVOz5mA6HHIX0t4eHy7iEnyb1CQ9Xw1XFC459+vRReHi43nrrLUnSokWLNH36dB07dqzQn0Nt3ry5HnroIT366KOSci8CGT9+vMaPH1/s+vKsXLlSDz30kJLOPuchQ4YoLi5O27ZtK9A3NTVVNWvW1Lx58zRq1KgC4z///HN16dJFp06dUrVq1STlBsrWrVvr0KFDatCggZYuXao//elPio2NVatWrYqsy+FwqHr16lq+fLnuuOMOffrpp+rVq5f279+vJk2aFOj/9ddfq0OHDoqLi1N0dLSSkpIUHR2tDRs2qHPnzoUuo6jXjMT2W/KiPYArVqzQ+PHj9dRTT2n37t3q1KmTevXqddFPUcnJyRo2bJi6detWTpUCAEpLTlKScn77rdi3wsKfJDkucT6XEjqHDh2qVatWKTMzU5K0bNky3XPPPbLb7UpPT9ekSZPUrFkzVatWTVWrVtUPP/xw2XsAN2/erO7du6t27doKDg7WsGHDdPLkSaWnp0s6twewMPv371dmZmaJt4d+fn665ppr3NoSExM1ZswYNWnSRKGhoQoNDVVaWprrecbGxqpOnTqFhj9Juv7669W8eXO9/fbbkqR//vOfqlevnm6++eYS1WplHvkt4LIwZ84cjRw50vWpZe7cuVq/fr0WLFigWbNmFTnd6NGjNWTIENntdn344YflVC0AoDT4hIdfUv/S3ANYXDExMXI6nfr444/Vrl07bd26VXPmzJEkPfHEE1q/fr3+9re/qVGjRgoICNCAAQOUlZVV7PnnOXLkiHr37q0xY8bo//7v/xQWFqZt27Zp5MiRys7OliQFBAQUOf2Fxkm5h5clKf+Bw7z5nj8fwzDc2kaMGKETJ05o7ty5ql+/vvz9/dW+fXvX87zYsiVp1KhRmjdvnqZMmaIlS5boT3/6U4HloPi8IgBmZWVp165dmjJlilt7jx49tGPHjiKnW7JkiQ4ePKh//etfevbZZy+6nMzMTNcnOCl3FzIAwHOKexg2vz/ef1/xT0+XnE7JZlPUMzNVbcCAMqguV0BAgPr166dly5bp559/VpMmTdSmTRtJ0tatWzVixAjdddddkqS0tDTXBRWX6ptvvlFOTo5eeuklV1h777333Ppcc8012rhxo2bOnFlg+saNGysgIEAbN24s9BBwzZo1JUnx8fGqXr26JBX7tKmtW7dq/vz56t27tyTp6NGjrsPSeXUdO3ZMP/30U5F7Ae+9915NmjRJr776qvbu3avhw4cXa9konFcEwKSkJDkcjgInqkZERCihiCu8Dhw4oClTpmjr1q3y8Sneapg1a1ah/zQAgMqj2oABCrrpJmUdiZNf/XryjYws82UOHTpUMTEx2rt3r+69915Xe6NGjbR69WrFxMTIMAxNmzbtsr825corr1ROTo5ee+01xcTEaPv27Xr99dfd+kydOlUtW7bUww8/rDFjxsjPz0+bN2/W3XffrfDwcE2ePFmTJk2Sn5+fOnbsqBMnTmjv3r0aOXKkGjVqpLp162rGjBl69tlndeDAAb300kvFqq1Ro0b65z//qbZt2yolJUVPPPGE216/zp076+abb1b//v01Z84cNWrUSD/88IMMw9Btt90mSapevbr69eunJ554Qj169FCdOnUuaz0hl9ecAyipwK5g0zQL3T3scDg0ZMgQzZw5s8hPGoWZOnWqkpOTXbejR4+WuGYAQPnzjYxU0A3Xl0v4k6SuXbsqLCxMP/74o4YMGeJqf/nll1W9enV16NBBMTEx6tmzp6677rrLWsa1116rOXPmaPbs2WrRooWWLVtW4BSoJk2a6NNPP9V3332n66+/Xu3bt9e///1v146QadOm6c9//rOefvppNW3aVIMGDVJiYqIkydfXV++8845++OEHtWrVSrNnzy7W0TNJWrx4sU6dOqXWrVvrvvvu09ixY1WrVi23PqtWrVK7du00ePBgNWvWTJMmTXJdnZxn5MiRysrK0v33339Z6wjneMVVwFlZWQoMDNTKlStdu9Elady4cYqNjdWWLVvc+v/xxx+qXr2629VXTqdTpmnKbrfr008/VdeuXS+6XK4iAoDycaErOmEdy5Yt07hx43T8+HH5+fldsC9XAV+YVxwC9vPzU5s2bbRhwwa3ALhhwwb16dOnQP+QkBDt2bPHrW3+/PnatGmT3n//fTVs2LDMawYAAMWTkZGhQ4cOadasWRo9evRFwx8uzmsOAU+cOFFvvvmmFi9erP3792vChAmKi4vTmDFjJOUevh02bJik3CuZWrRo4XarVauW65vHg4KCPPlUAAAoYNmyZapatWqht+bNm3u6vDL14osv6tprr1VERISmTp3q6XK8glfsAZSkQYMG6eTJk3rmmWcUHx+vFi1aaN26dapfv76k3KuWLvd7lQAA8LQ777xTN9xwQ6HjfH19y7ma8jVjxgzNmDHD02V4Fa84B9BTOIcAAMoH5wDiUnEO4IV5zSFgAAAAFA8BEABQaXDQCsXFa+XCCIAAgAov7xy3jIwMD1eCyiLvteLt50deLq+5CAQA4L3sdruqVavm+lLiwMBAfgcWhTJNUxkZGUpMTFS1atXcvvMX5xAAAQCVQuTZX+3IC4HAhVSrVs31mkFBBEAAQKVgGIaioqJUq1YtZWdne7ocVGC+vr7s+bsIAiAAoFKx2+1s3IES4iIQAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAi/GqADh//nw1bNhQVapUUZs2bbR169Yi+65evVrdu3dXzZo1FRISovbt22v9+vXlWC0AAIBneE0AXLFihcaPH6+nnnpKu3fvVqdOndSrVy/FxcUV2v+LL75Q9+7dtW7dOu3atUtdunRRTEyMdu/eXc6VAwAAlC/DNE3T00WUhhtuuEHXXXedFixY4Gpr2rSp+vbtq1mzZhVrHs2bN9egQYP09NNPF6t/SkqKQkNDlZycrJCQkMuqGwAAlC+2316yBzArK0u7du1Sjx493Np79OihHTt2FGseTqdTqampCgsLK7JPZmamUlJS3G4AAACVjVcEwKSkJDkcDkVERLi1R0REKCEhoVjzeOmll5Senq6BAwcW2WfWrFkKDQ113erWrVuiugEAADzBKwJgHsMw3IZN0yzQVph33nlHM2bM0IoVK1SrVq0i+02dOlXJycmu29GjR0tcMwAAQHnz8XQBpSE8PFx2u73A3r7ExMQCewXPt2LFCo0cOVIrV67UrbfeesG+/v7+8vf3L3G9AAAAnuQVewD9/PzUpk0bbdiwwa19w4YN6tChQ5HTvfPOOxoxYoSWL1+u22+/vazLBAAAqBC8Yg+gJE2cOFH33Xef2rZtq/bt22vRokWKi4vTmDFjJOUevv3111/19ttvS8oNf8OGDdMrr7yiG2+80bX3MCAgQKGhoR57HgAAAGXNawLgoEGDdPLkST3zzDOKj49XixYttG7dOtWvX1+SFB8f7/adgAsXLlROTo4eeeQRPfLII6724cOHa+nSpeVdPgAAQLnxmu8B9AS+RwgAgMqH7beXnAMIAACA4iMAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAKyg4pNPa8fBJMUnn/Z0KQAAwMv4eLoAFPTX9T9q/uafZUqyGdKsfi01qF09T5cFAAC8BHsAK5j45NP6+9nwJ0lOU5q6eg97AgEAQKkhAFYwX//ye4E2pym9+MkPOpWe5YGKAACAtyEAVjDXXxEmm1Gw/YPdx3XT7E164T8/KCkts/wLAwAAXoMAWMFEhQZoVr+Wshu5KdCQXIEwPcuh17ccVKfZm/Xs2n1KTD3juUIBAEClZZimaV68GwqTkpKi0NBQJScnKyQkpFTnHZ98WoeTMtQgPFAOp6nXtxzUezuPKcvhdPXx97Fp8PX1NKbzlYoMrVKqywcAwFuV5fa7siAAlkB5v4ASks/o9S0H9c7XccrMORcE/ew2DWxXR2M6X6k61QPLvA4AACozAiABsEQ89QJKTD2jN774Rf/6Kk6nsx2udh+boQFt6ujhWxqpXg2CIAAAhSEAEgBLxNMvoJNpmXpz2yG9veOw0rPOBUG7zVDfa2vrkS5X6oqaVcu9LgAAKjJPb78rAgJgCVSUF9Cp9Cwt3n5IS7cfVmpmjqvdZkgxraL1aJdGahwR7LH6AACoSCrK9tuTCIAlUNFeQMmns7V0+2Et3n5IyaezXe2GIfVuEaVHuzZS0yjP1wkAgCdVtO23JxAAS6CivoBSz2Tr7S+P6M2tv+hURrbbuB7NIjS2W2O1qB3qoeoAAPCsirr9Lk8EwBKo6C+g9MwcLfvvES364hclpbn/iki3q2vpsW6NdW3dap4pDgAAD6no2+/yQAAsgcryAjqd5dA7X8fp9S0HlZjq/isiNzepqbFdG6ltgzAPVQcAQPmqLNvvskQALIHK9gI6k+3Qym+OasHnB3U82f1XRDpcWUNjuzXWjVfU8FB1AACUj8q2/S4LBMASKNMXUPKv0u8HpbArpdDapTrrrBynVn17TH/f/LOOnTrtNu76BmEa262xOjaqIcMo5EeJAQCo5AiABMASKbMX0D/6SIc+PztgSC3vlpreIQXVkoJqSlVrSv4huZf3lkC2w6kPd/+qv2/+WYdPZriNa12vmsZ2a6xbmtQkCAIAvAoBkABYImXyAkr+VXq52cX72f1zw2BQuFT1bDDMf6ua97iWFFhDsvsUOasch1Nr/xev1zYd0MET6W7jrqkTqse6NtatTWtVziBYhntSAQCVEwGQAFgiZfICOvSF9I+Y0pmXiyEFhp0XEGvlhkfXXsVacgTU0PrDDr3yxXH9+Fuq2xyaRoVobNdG6tk8Ujbb2SBY0cPVrqXS2gmS6ZQMmxTzinTdME9XVVBFX48AvI/F33cIgF4WAOfPn6+//vWvio+PV/PmzTV37lx16tSpyP5btmzRxIkTtXfvXkVHR2vSpEkaM2ZMsZdXZnsA57bIDS15DJvUYZzkyJTST0hpiVJ6kpSeKGWcdO9bCkzfIJ32q64jZ4J0LKuqTpghOqlQJZmh8guppVvatFB7nx9lbHlBhumUadhkdP2LdPUdkiPr7C079z4nq2DbZT0ubt+85WZKKuSl7RMg+fhJdj/J5pu7Z9T1+Owt/2O7n2Tzyff4QuPOzsvu6/74/HnafM+NO7Be+uJv50LqrTOlVvcU7Ofpva+VYWNR0Wus6PVJ1FhaPFWj01HEe+J575U/rJV2vCKZpiRDav+IdFVvyWbPfe8ybLn3Np9C2vKG7Wcf5x/2kWy20ns+ZbgeCYBeFABXrFih++67T/Pnz1fHjh21cOFCvfnmm9q3b5/q1atXoP+hQ4fUokULPfDAAxo9erS2b9+uhx9+WO+884769+9frGWW2Qvo27elj8ZLpiP3nypmbtF7rpwOKeP33GCYfjYYpiW6D6efkNLODuecKXw+qNgMe+Hh8qJB80IB1KeIx+dNE7dD5jdLZciUKUPG9Q9IV3Y7G0qNfPc6b/hi97ZSmMfZ+/3/lrn5+dwPJLLJ6DxJuuo2yenM/T9yOgreF9ZmOiVnTiFtecP5x50/75zCl+d0SEk/yTz2tQzlfiwxareVajQ6tw7OXyeGLd/w+W3GuWG3fudPZxTRVsiwYZOO/lfmnvfP/Z1b9Jdqtznv+RS1PovZ7lqXzsubx5lTMtOTzq3HoJpSQHX3MGLkvz8bSIzzg4ot33j7ubb8waaweRZn+rj/yvzunXPr8ZqBuevRLZid94HVWUhIu2CfIsJdYR96PcEtEJ7/9yhsuJCAmXZC5u8/n/tb3/laqR7BIQB6UQC84YYbdN1112nBggWutqZNm6pv376aNWtWgf6TJ0/WmjVrtH//flfbmDFj9N133+nLL78s1jLL/irgX6SwK0rvk49pSllp+QJhYaEx3+30qdJZbhlwGHY5DV85bedups1XTpufTJuvDEMKTv5R+febmZJOBzeQIclw5sgwc2RzZMsws2U4s2U4c2RzZhexRACApzgNm2zjvy+17SEBUCr6yoBKJCsrS7t27dKUKVPc2nv06KEdO3YUOs2XX36pHj16uLX17NlTb731lrKzs+Xr61tgmszMTGVmnvsi5ZSUlFKovgihtUv/0IFhSP7BubewKy7ePycr9xBzem44NNMSdTjuiPZ9H6vemZ+4HZV0mtI6xw1KUZCy5KPss7cs+SjLPDec15btarOf62/mG5+/v3l+m12mLn6YYaB9s573eUs+hlM5pk1P5ozUeye6XGQqUz5yyEcO+Snn3GMjRz7Kka8c8j3b7qsc+RrnDcuR2y9fe1HzCVWa7rZvke289bjN2VJO2QrM59z8HfI18i0rX00+RumeDgB4So5pk0M2OZV7b8hUkJFZoF+66SfJkF1O2eSUj5yyGV6xX8PFaRr53hvt594LTbvb++L576M5+fpmmbnDvsrRQPvn573vGFrm6KZM+eWuQ8PhWpd2OWSXM98t37DhlI/O9c29Lzic29chu8x80xec74X+djbTqZNH96tGRT3sXwl5RQBMSkqSw+FQRESEW3tERIQSEhIKnSYhIaHQ/jk5OUpKSlJUVFSBaWbNmqWZM2eWXuEVnY+fFBKVe1Pu0bmG10pVOp/WlBen6bl84eqpnJGKv+Ju2W2Gsh2msh3Oszf3xzkOp7LOtuWcbctylE1oec/RRV84rlED22867IxQgorzJdeGciOaj87I/1xzUduTEm5ndplNCoZUx8VCatEMOc8LqWcfG+eH17z2vIB6Xlg92x6mFE3web/AxmJRTm+lKyB3b6pMGYZ5dm+rmTuc1+72uOB4m2vYPFt/0dPrvHnljQ80MtXb9t8CH0hWOzopVYGuAJF7byhHdjnPCxd5j3MKaXOYecP2s22G+/i8cefNM+++mlK0zG+W7Pk2bA7T0D1Zf9FJhbqeh+28+/zthbUZMmUzCmkrdHqn2zrL31cyVV2pmu77T7eNr9M0ND17uE4qpNDndf76KXx8vnVlnt/HKHK6wj7gReqktvuPdVuPOaZN3TJfKuR/2ywQWGxnh33yPbYZ5wKKzTXNuWByLlAWDEA2Oc8LME5VN1L0pM87BdbjjOxhOqFqrkB2/ofg/B9484e6vA++zmJ84L0U35qNS/V9pzQZcqq2krTFf0KBv/VhZ2Sx3sVRPF4RAPOc/zUlpmle8KtLCutfWHueqVOnauLEia7hlJQU1a1b93LLrbSiQgN0Xd+x6ry6leoaCTpqRmpsv84a1K7guZbFYZqmHE4zNyw6ncrOKRgcC4bIwh9nnw2Yv6dlav7nB5WgGkpw5r5lGJKGt2+goCp2mWZudss7AcJUboN5tp5z7Trb91xbXs35x8n1OG+e+eaRb/r8y0zPytF737uH1N9UQz2bRSjAz+6WLd2Wfd66K6xd+Z9XYfNwm9+5gUxTynS1S3HZDk35pXqBjcXxhgNUxddeZF0Xqq04z+V8RdVrmlJmjkNbjn1QoMafa98lfx97wXkVM7VfyskxZ0+lL/QN9UyOU1OPjypQX3bt9gr3KXzDXpwaTUk5l1hjUQ7lODUlwb9Ajfsi+8iviBrPZzt7KytZOSGa+lvB9VgjuqGiilnjpXJKyrpor/w1OjXlt6ACNX4fUfR69D17Ky9ZOU69F1/ww3HTqJBi/63LWlZONU1NdP9b/yVnlMbVv9LTpXkVrwiA4eHhstvtBfb2JSYmFtjLlycyMrLQ/j4+PqpRo/DPGP7+/vL39y90nNUMaldPNzcZoMNJGWoQHqio0IDLnpdhGPKxG/KxSwEquMG+XPVqBOrJ1d/LYZqyG4ae79fiskNqWVmxM05Prv5eCc4ashuGXqiQNUYVCPsvVrga61boGit6fZK0Yme9SlBjfWosBee/78yukO87BddjSbYzKMirLgJp06aN5s+f72pr1qyZ+vTpU+RFIB999JH27dvnanvooYcUGxtbMS4CQamITz5dKiG1LFFj6ajoNVb0+iRqLC3UWDrKska2314UAPO+Bub1119X+/bttWjRIr3xxhvau3ev6tevr6lTp+rXX3/V22+/Lenc18CMHj1aDzzwgL788kuNGTOmYnwNDAAAKDNsv73kELAkDRo0SCdPntQzzzyj+Ph4tWjRQuvWrVP9+vUlSfHx8YqLi3P1b9iwodatW6cJEybo73//u6Kjo/Xqq68WO/wBAABUVl6zB9AT+AQBAEDlw/a7bC/aAgAAQAVEAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABbjNT8F5wl5P6KSkpLi4UoAAEBx5W23rfxjaATAEkhNTZUk1a1b18OVAACAS5WamqrQ0FBPl+ER/BZwCTidTh0/flzBwcEyDMPT5ZS7lJQU1a1bV0ePHrXsbymWBtZj6WA9lhzrsHSwHktHWa5H0zSVmpqq6Oho2WzWPBuOPYAlYLPZVKdOHU+X4XEhISG8yZUC1mPpYD2WHOuwdLAeS0dZrUer7vnLY83YCwAAYGEEQAAAAIshAOKy+fv7a/r06fL39/d0KZUa67F0sB5LjnVYOliPpYP1WLa4CAQAAMBi2AMIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgLgks2bNUrt27RQcHKxatWqpb9+++vHHHz1dVqU3a9YsGYah8ePHe7qUSufXX3/Vvffeqxo1aigwMFDXXnutdu3a5emyKpWcnBz95S9/UcOGDRUQEKArrrhCzzzzjJxOp6dLq9C++OILxcTEKDo6WoZh6MMPP3Qbb5qmZsyYoejoaAUEBOiWW27R3r17PVNsBXah9Zidna3JkyerZcuWCgoKUnR0tIYNG6bjx497rmAvQQDEJdmyZYseeeQRffXVV9qwYYNycnLUo0cPpaene7q0Smvnzp1atGiRrrnmGk+XUumcOnVKHTt2lK+vr/7zn/9o3759eumll1StWjVPl1apzJ49W6+//rrmzZun/fv368UXX9Rf//pXvfbaa54urUJLT09Xq1atNG/evELHv/jii5ozZ47mzZunnTt3KjIyUt27d3f9jjxyXWg9ZmRk6Ntvv9W0adP07bffavXq1frpp5905513eqBS78LXwKBETpw4oVq1amnLli26+eabPV1OpZOWlqbrrrtO8+fP17PPPqtrr71Wc+fO9XRZlcaUKVO0fft2bd261dOlVGp33HGHIiIi9NZbb7na+vfvr8DAQP3zn//0YGWVh2EY+uCDD9S3b19JuXv/oqOjNX78eE2ePFmSlJmZqYiICM2ePVujR4/2YLUV1/nrsTA7d+7U9ddfryNHjqhevXrlV5yXYQ8gSiQ5OVmSFBYW5uFKKqdHHnlEt99+u2699VZPl1IprVmzRm3bttXdd9+tWrVqqXXr1nrjjTc8XValc9NNN2njxo366aefJEnfffedtm3bpt69e3u4ssrr0KFDSkhIUI8ePVxt/v7+6ty5s3bs2OHByiq/5ORkGYbBnv4S8vF0Aai8TNPUxIkTddNNN6lFixaeLqfSeffdd/Xtt99q586dni6l0vrll1+0YMECTZw4UU8++aS+/vprjR07Vv7+/ho2bJiny6s0Jk+erOTkZF199dWy2+1yOBx67rnnNHjwYE+XVmklJCRIkiIiItzaIyIidOTIEU+U5BXOnDmjKVOmaMiQIQoJCfF0OZUaARCX7dFHH9X//vc/bdu2zdOlVDpHjx7VuHHj9Omnn6pKlSqeLqfScjqdatu2rZ5//nlJUuvWrbV3714tWLCAAHgJVqxYoX/9619avny5mjdvrtjYWI0fP17R0dEaPny4p8ur1AzDcBs2TbNAG4onOztb99xzj5xOp+bPn+/pcio9AiAuy2OPPaY1a9boiy++UJ06dTxdTqWza9cuJSYmqk2bNq42h8OhL774QvPmzVNmZqbsdrsHK6wcoqKi1KxZM7e2pk2batWqVR6qqHJ64oknNGXKFN1zzz2SpJYtW+rIkSOaNWsWAfAyRUZGSsrdExgVFeVqT0xMLLBXEBeXnZ2tgQMH6tChQ9q0aRN7/0oB5wDikpimqUcffVSrV6/Wpk2b1LBhQ0+XVCl169ZNe/bsUWxsrOvWtm1bDR06VLGxsYS/YurYsWOBryH66aefVL9+fQ9VVDllZGTIZnPfHNjtdr4GpgQaNmyoyMhIbdiwwdWWlZWlLVu2qEOHDh6srPLJC38HDhzQZ599pho1ani6JK/AHkBckkceeUTLly/Xv//9bwUHB7vOcwkNDVVAQICHq6s8goODC5w3GRQUpBo1anA+5SWYMGGCOnTooOeff14DBw7U119/rUWLFmnRokWeLq1SiYmJ0XPPPad69eqpefPm2r17t+bMmaP777/f06VVaGlpafr5559dw4cOHVJsbKzCwsJUr149jR8/Xs8//7waN26sxo0b6/nnn1dgYKCGDBniwaorngutx+joaA0YMEDffvut1q5dK4fD4druhIWFyc/Pz1NlV34mcAkkFXpbsmSJp0ur9Dp37myOGzfO02VUOh999JHZokUL09/f37z66qvNRYsWebqkSiclJcUcN26cWa9ePbNKlSrmFVdcYT711FNmZmamp0ur0DZv3lzo++Hw4cNN0zRNp9NpTp8+3YyMjDT9/f3Nm2++2dyzZ49ni66ALrQeDx06VOR2Z/PmzZ4uvVLjewABAAAshnMAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAJQiwzD04YcferoMALggAiAArzFixAgZhlHgdtttt3m6NACoUPgtYABe5bbbbtOSJUvc2vz9/T1UDQBUTOwBBOBV/P39FRkZ6XarXr26pNzDswsWLFCvXr0UEBCghg0bauXKlW7T79mzR127dlVAQIBq1KihBx98UGlpaW59Fi9erObNm8vf319RUVF69NFH3cYnJSXprrvuUmBgoBo3bqw1a9aU7ZMGgEtEAARgKdOmTVP//v313Xff6d5779XgwYO1f/9+SVJGRoZuu+02Va9eXTt37tTKlSv12WefuQW8BQsW6JFHHtGDDz6oPXv2aM2aNWrUqJHbMmbOnKmBAwfqf//7n3r37q2hQ4fq999/L9fnCQAXZAKAlxg+fLhpt9vNoKAgt9szzzxjmqZpSjLHjBnjNs0NN9xgPvTQQ6ZpmuaiRYvM6tWrm2lpaa7xH3/8sWmz2cyEhATTNE0zOjrafOqpp4qsQZL5l7/8xTWclpZmGoZh/uc//ym15wkAJcU5gAC8SpcuXbRgwQK3trCwMNfj9u3bu41r3769YmNjJUn79+9Xq1atFBQU5BrfsWNHOZ1O/fjjjzIMQ8ePH1e3bt0uWMM111zjehwUFKTg4GAlJiZe7lMCgFJHAATgVYKCggockr0YwzAkSaZpuh4X1icgIKBY8/P19S0wrdPpvKSaAKAscQ4gAEv56quvCgxfffXVkqRmzZopNjZW6enprvHbt2+XzWZTkyZNFBwcrAYNGmjjxo3lWjMAlDb2AALwKpmZmUpISHBr8/HxUXh4uCRp5cqVatu2rW666SYtW7ZMX3/9td566y1J0tChQzV9+nQNHz5cM2bM0IkTJ/TYY4/pvvvuU0REhCRpxowZGjNmjGrVqqVevXopNTVV27dv12OPPVa+TxQASoAACMCrfPLJJ4qKinJru+qqq/TDDz9Iyr1C991339XDDz+syMhILVu2TM2aNZMkBQYGav369Ro3bpzatWunwMBA9e/fX3PmzHHNa/jw4Tpz5oxefvllPf744woPD9eAAQPK7wkCQCkwTNM0PV0EAJQHwzD0wQcfqG/fvp4uBQA8inMAAQAALIYACAAAYDGcAwjAMjjjBQBysQcQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYv4f9Abz84uxItAAAAAASUVORK5CYII=" alt="Learning Curve"> | |
| </div> | |
| </div> | |
| <div style="display: flex;"> | |
| <div class="section class_dist"> | |
| <h2>Class Distribution</h2> | |
| <pre> Count Percentage | |
| class | |
| 1 2504 50.08 | |
| 0 2496 49.92</pre> | |
| <h3>Additional Metrics</h3> | |
| <ul> | |
| <li>Total Samples: 5000</li> | |
| <li>Imbalance Ratio: 1.00</li> | |
| </ul> | |
| </div> | |
| <div class="section"> | |
| <h2>Classification Report</h2> | |
| <pre> precision recall f1-score support | |
| Class 0 0.9980 1.0000 0.9990 499 | |
| Class 1 1.0000 0.9980 0.9990 501 | |
| accuracy 0.9990 1000 | |
| macro avg 0.9990 0.9990 0.9990 1000 | |
| weighted avg 0.9990 0.9990 0.9990 1000 | |
| </pre> | |
| <h3>Metrics</h3> | |
| <div class="metrics"> | |
| <ul> | |
| <li>True Positives (TP): 500</li> | |
| <li>True Negatives (TN): 499</li> | |
| </ul> | |
| <ul> | |
| <li>False Positives (FP): 0</li> | |
| <li>False Negatives (FN): 1</li> | |
| </ul> | |
| </div> | |
| </div> | |
| <div class="section confusion-matrix"> | |
| <h2>Confusion Matrix</h2> | |
| <img src="data:image/png;base64,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" alt="Confusion Matrix"> | |
| </div> | |
| </div> | |
| </div> | |
| </body> | |
| </html> | |