Morten Hjorth-Jensen
108 lines
3.1 KiB
Plaintext
108 lines
3.1 KiB
Plaintext
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"import mglearn\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import os\n",
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"from scipy import signal\n",
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"from sklearn.datasets import load_boston\n",
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"from sklearn.preprocessing import MinMaxScaler, PolynomialFeatures\n",
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"from mglearn.make_blobs import make_blobs\n",
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"\n",
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"#DATA_PATH = os.path.join(os.path.dirname(__file__), \"data\")\n",
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"\n",
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"\n",
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"def make_forge():\n",
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" # a carefully hand-designed dataset lol\n",
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" X, y = make_blobs(centers=2, random_state=4, n_samples=30)\n",
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" y[np.array([7, 27])] = 0\n",
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" mask = np.ones(len(X), dtype=np.bool)\n",
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" mask[np.array([0, 1, 5, 26])] = 0\n",
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" X, y = X[mask], y[mask]\n",
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" return X, y\n",
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"\n",
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"\n",
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"def make_wave(n_samples=100):\n",
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" rnd = np.random.RandomState(42)\n",
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" x = rnd.uniform(-3, 3, size=n_samples)\n",
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" y_no_noise = (np.sin(4 * x) + x)\n",
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" y = (y_no_noise + rnd.normal(size=len(x))) / 2\n",
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" return x.reshape(-1, 1), y\n",
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"\n",
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"\n",
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"def load_extended_boston():\n",
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" boston = load_boston()\n",
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" X = boston.data\n",
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"\n",
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" X = MinMaxScaler().fit_transform(boston.data)\n",
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" X = PolynomialFeatures(degree=2, include_bias=False).fit_transform(X)\n",
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" return X, boston.target\n",
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"\n",
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"\n",
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"def load_citibike():\n",
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" data_mine = pd.read_csv(os.path.join(DATA_PATH, \"citibike.csv\"))\n",
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" data_mine['one'] = 1\n",
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" data_mine['starttime'] = pd.to_datetime(data_mine.starttime)\n",
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" data_starttime = data_mine.set_index(\"starttime\")\n",
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" data_resampled = data_starttime.resample(\"3h\").sum().fillna(0)\n",
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" return data_resampled.one\n",
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"\n",
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"\n",
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"def make_signals():\n",
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" # fix a random state seed\n",
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" rng = np.random.RandomState(42)\n",
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" n_samples = 2000\n",
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" time = np.linspace(0, 8, n_samples)\n",
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" # create three signals\n",
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" s1 = np.sin(2 * time) # Signal 1 : sinusoidal signal\n",
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" s2 = np.sign(np.sin(3 * time)) # Signal 2 : square signal\n",
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" s3 = signal.sawtooth(2 * np.pi * time) # Signal 3: saw tooth signal\n",
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"\n",
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" # concatenate the signals, add noise\n",
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" S = np.c_[s1, s2, s3]\n",
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" S += 0.2 * rng.normal(size=S.shape)\n",
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"\n",
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" S /= S.std(axis=0) # Standardize data\n",
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" S -= S.min()\n",
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" return S\n"
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]
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},
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