mlpy_test_logistic_regression.ipynb

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   "source": [
    "import numpy as np\n",
    "from mlpy.regression import LogisticRegression\n",
    "from mlpy.tools import accuracy_score"
   ]
  },
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   "execution_count": 2,
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   "source": [
    "x1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])[:, np.newaxis]\n",
    "x2 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])[:, np.newaxis]\n",
    "x_data = np.hstack((x1, x2))\n",
    "y_data = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])"
   ]
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   "source": [
    "logr = LogisticRegression(intercept=True, lmb=1e-10)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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     "text": [
      "iteration 100: cost: 3.992370453076267\n",
      "iteration 200: cost: 3.066418693644593\n",
      "iteration 300: cost: 2.5681224891884704\n",
      "iteration 400: cost: 2.2552866208550815\n",
      "iteration 500: cost: 2.0382612257855808\n",
      "iteration 600: cost: 1.8772108369480995\n",
      "iteration 700: cost: 1.7518495497375486\n",
      "iteration 800: cost: 1.650755853597201\n",
      "iteration 900: cost: 1.566992574267283\n",
      "iteration 1000: cost: 1.4960906355938466\n"
     ]
    }
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   "source": [
    "costs = logr.fit(x_data, y_data, verbose=True)"
   ]
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    "preds = logr.predict(x_data)"
   ]
  },
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       "[0, 0, 0, 0, 0, 1, 1, 1, 1, 1]"
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     "execution_count": 6,
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   "source": [
    "[int(p) for p in preds]"
   ]
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   "cell_type": "code",
   "execution_count": 7,
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     "execution_count": 7,
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   "source": [
    "list(y_data)"
   ]
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   "execution_count": null,
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