Searched refs:np (Results 1 – 8 of 8) sorted by relevance
| /Zephyr-Core-3.6.0/tests/kernel/timer/timer_behavior/pytest/ |
| D | saleae_logic2.py | 11 import numpy as np namespace
46 all_data = np.loadtxt(file_name, delimiter=',', skiprows=1, usecols=0)
55 diff = np.diff(data)
57 mean = np.mean(diff)
58 std = np.std(diff)
59 var = np.var(diff)
60 minimum = np.min(diff)
61 maximum = np.max(diff)
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| /Zephyr-Core-3.6.0/samples/modules/tflite-micro/magic_wand/train/ |
| D | data_augmentation_test.py | 26 import numpy as np namespace
35 original_data = np.random.rand(10, 3).tolist()
42 np.random.rand(128, 3).tolist(),
43 np.random.rand(66, 2).tolist(),
44 np.random.rand(9, 1).tolist()
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| D | data_augmentation.py | 26 import numpy as np namespace
53 new_data.append((np.array(data, dtype=np.float32) +
72 (np.array(data, dtype=np.float32) * molecule / denominator).tolist())
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| D | data_load.py | 26 import numpy as np namespace
71 tmp_data = (np.random.rand(seq_length, dim) - 0.5) * noise_level + data[0]
76 tmp_data = (np.random.rand(seq_length, dim) - 0.5) * noise_level + data[-1]
85 features = np.zeros((length, self.seq_length, self.dim))
86 labels = np.zeros(length)
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| D | train_test.py | 25 import numpy as np namespace
50 cnn_data = np.random.rand(60, 128, 3, 1)
51 lstm_data = np.random.rand(60, 128, 3)
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| D | train.py | 29 import numpy as np # pylint: disable=duplicate-code namespace
44 np.product(list(map(int, v.shape))) * v.dtype.size
132 test_labels = np.zeros(test_len)
148 pred = np.argmax(model.predict(test_data), axis=1)
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| /Zephyr-Core-3.6.0/drivers/spi/ |
| D | spi_xec_qmspi_ldma.c | 99 uint8_t np; /* number of data pins: 1, 2, or 4 */ member
375 qdata->np = npins_from_spi_config(config); in qmspi_configure()
378 qdata->np = 1u; in qmspi_configure()
453 regs->CTRL = encode_npins(qdata->np); in qmspi_xfr_cm_init()
458 if (qdata->np != 1) { in qmspi_xfr_cm_init()
934 qdata->np = cfg->width; in qmspi_xec_init()
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| /Zephyr-Core-3.6.0/samples/modules/tflite-micro/hello_world/train/ |
| D | train_hello_world_model.ipynb | 161 "import numpy as np\n",
171 "np.random.seed(seed)\n",
213 "x_values = np.random.uniform(\n",
214 " low=0, high=2*math.pi, size=SAMPLES).astype(np.float32)\n",
217 "np.random.shuffle(x_values)\n",
220 "y_values = np.sin(x_values).astype(np.float32)\n",
269 "y_values += 0.1 * np.random.randn(*y_values.shape)\n",
327 "# Use np.split to chop our data into three parts.\n",
328 "# The second argument to np.split is an array of indices where the data will be\n",
330 "x_train, x_test, x_validate = np.split(x_values, [TRAIN_SPLIT, TEST_SPLIT])\n",
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