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