diff --git a/Chapter-4/Chapter4.ipynb b/Chapter-4/Chapter4.ipynb
index 4d0be0e..8af6f0f 100644
--- a/Chapter-4/Chapter4.ipynb
+++ b/Chapter-4/Chapter4.ipynb
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    "source": [
     "import mglearn\n",
     "mglearn.plots.plot_logistic_regression_graph()"
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-     "text": [
-      "X es:\n",
-      "[[-1 -1]\n",
-      " [-1  1]\n",
-      " [ 1 -1]\n",
-      " [ 1  1]]\n",
-      "Y es:\n",
-      "[-1 -1 -1  1]\n"
-     ]
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+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "X = np.array([[-1, -1], [-1, 1], [1, -1], [1, 1]])\n",
@@ -237,45 +135,17 @@
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-      "-- Epoch 1\n",
-      "Norm: 1.41, NNZs: 2, Bias: -1.000000, T: 4, Avg. loss: 0.750000\n",
-      "Total training time: 0.00 seconds.\n",
-      "-- Epoch 2\n",
-      "Norm: 1.41, NNZs: 2, Bias: -1.000000, T: 8, Avg. loss: 0.000000\n",
-      "Total training time: 0.00 seconds.\n",
-      "-- Epoch 3\n",
-      "Norm: 1.41, NNZs: 2, Bias: -1.000000, T: 12, Avg. loss: 0.000000\n",
-      "Total training time: 0.00 seconds.\n",
-      "-- Epoch 4\n",
-      "Norm: 1.41, NNZs: 2, Bias: -1.000000, T: 16, Avg. loss: 0.000000\n",
-      "Total training time: 0.00 seconds.\n",
-      "-- Epoch 5\n",
-      "Norm: 1.41, NNZs: 2, Bias: -1.000000, T: 20, Avg. loss: 0.000000\n",
-      "Total training time: 0.00 seconds.\n",
-      "Rendimiento del entrenamiento: 1.0\n"
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-     "name": "stderr",
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-      "/home/leninml/anaconda3/envs/MachineLearning/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:144: FutureWarning: max_iter and tol parameters have been added in Perceptron in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
-      "  FutureWarning)\n"
-     ]
+    "pycharm": {
+     "is_executing": true
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+   },
+   "outputs": [],
    "source": [
     "from sklearn.linear_model import Perceptron\n",
     "percept_and = Perceptron(verbose=1, shuffle=True)\n",
@@ -285,24 +155,16 @@
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    "source": [
     "y_pred = percept_and.predict(X)\n",
     "y_pred"
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-       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"309.8752,-104.0285 320.2897,-105.9749 313.3621,-97.9588 309.8752,-104.0285\"/>\n",
-       "</g>\n",
-       "<!-- h2[1]&#45;&gt;y -->\n",
-       "<g id=\"edge23\" class=\"edge\">\n",
-       "<title>h2[1]&#45;&gt;y</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" d=\"M257.9458,-159.8396C272.6188,-151.4105 294.6007,-138.7826 311.5017,-129.0735\"/>\n",
-       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"313.3621,-132.0412 320.2897,-124.0251 309.8752,-125.9715 313.3621,-132.0412\"/>\n",
-       "</g>\n",
-       "<!-- h2[2]&#45;&gt;y -->\n",
-       "<g id=\"edge24\" class=\"edge\">\n",
-       "<title>h2[2]&#45;&gt;y</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" d=\"M260.1241,-115C273.6484,-115 292.3808,-115 307.8486,-115\"/>\n",
-       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"307.9315,-118.5001 317.9315,-115 307.9315,-111.5001 307.9315,-118.5001\"/>\n",
-       "</g>\n",
-       "</g>\n",
-       "</svg>\n"
-      ],
-      "text/plain": [
-       "<graphviz.dot.Digraph at 0x7eff6e28f2e8>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "mglearn.plots.plot_two_hidden_layer_graph()"
    ]
@@ -830,24 +263,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1 1 0 1 1 1 1 0 0 0 1 0 1 1 0 1 1 0 1 0 1 0 0 0 1 1 0 1 0 1 0 0 0 0 1 0 1\n",
-      " 1 0 0 0 0 0 0 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 0 0 0 0 0 1 1 0\n",
-      " 1 1 0 1 1 0 1 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 0]\n",
-      "100\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.neural_network import MLPClassifier\n",
     "from sklearn.datasets import make_moons\n",
@@ -870,26 +295,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "from matplotlib.colors import ListedColormap\n",
@@ -901,21 +316,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "70\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.3, random_state=42) #, stratify=y\n",
@@ -933,48 +343,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.31702684, -0.2525239 ],\n",
-       "       [ 1.15536561, -0.50593577],\n",
-       "       [ 1.31311917, -0.69665985],\n",
-       "       [ 0.8729088 ,  0.08643291],\n",
-       "       [ 1.72532644,  0.53367598],\n",
-       "       [-0.4993884 ,  0.13192906],\n",
-       "       [ 0.35940317,  0.84867003],\n",
-       "       [-0.16955317,  0.60660877],\n",
-       "       [ 1.50917461, -0.06701048],\n",
-       "       [ 0.36877983, -0.34894509],\n",
-       "       [-0.7280717 ,  0.3259131 ],\n",
-       "       [ 0.77145295, -0.69709227],\n",
-       "       [ 1.00549331,  0.38686701],\n",
-       "       [-0.74872343, -0.06972957],\n",
-       "       [ 1.89948318,  0.79928869],\n",
-       "       [-0.87006365,  0.70686285],\n",
-       "       [ 1.12856036,  0.33191968],\n",
-       "       [ 0.97370054, -0.08631168],\n",
-       "       [ 0.89715307,  0.94175457],\n",
-       "       [-0.51699811,  0.74457804],\n",
-       "       [-0.59385445,  0.46769065],\n",
-       "       [-0.46333991,  0.86330772],\n",
-       "       [ 0.55039452,  1.16554689],\n",
-       "       [-0.60690411,  0.50000529],\n",
-       "       [ 0.42598043, -0.3006242 ]])"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "X_test"
    ]
@@ -992,24 +370,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.0"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "acc = red.score(X_train, y_train)\n",
     "acc"
@@ -1028,24 +398,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.8666666666666667"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "acc_test = red.score(X_test, y_test)\n",
     "acc_test"
@@ -1053,24 +415,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import mglearn\n",
     "mglearn.plots.plot_2d_separator(red, X_train, fill=True, alpha=.3)\n",
@@ -1094,10 +448,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
    "outputs": [],
@@ -1121,10 +478,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
    "outputs": [],
@@ -1149,87 +509,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "scrolled": false,
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/leninml/anaconda3/envs/MachineLearning/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:916: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
-      "  y = column_or_1d(y, warn=True)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "MLPClassifier(activation='tanh', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
-       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
-       "       hidden_layer_sizes=(2, 2), learning_rate='constant',\n",
-       "       learning_rate_init=0.001, max_iter=1500, momentum=0.9,\n",
-       "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
-       "       random_state=None, shuffle=True, solver='lbfgs', tol=0.0001,\n",
-       "       validation_fraction=0.1, verbose=1, warm_start=False)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "red_xor.fit(X, Y)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.0"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "red_xor.score(X, Y)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-1,  1,  1, -1])"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "y_pred = red_xor.predict(X)\n",
     "y_pred"
@@ -1237,10 +559,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
    "outputs": [],
@@ -1250,26 +575,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure()\n",
     "plt.scatter(X[:,0],X[:,1], c=y_grafico, cmap=cm_bright)\n",
@@ -1278,24 +593,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "mglearn.plots.plot_2d_separator(red_xor, X, fill=True, alpha=.3)\n",
     "mglearn.discrete_scatter(X[:, 0], X[:, 1], y_grafico) \n",
@@ -1332,14 +639,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
    "outputs": [],
    "source": [
+    "import numpy as np\n",
     "x = np.linspace(-5,5, num=2000)\n",
     "# x = np.arange(10)\n",
     "y = x*x-2*x+3\n"
@@ -1347,35 +658,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-5.       , -4.9949975, -4.989995 , ...,  4.989995 ,  4.9949975,\n",
-       "        5.       ])"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "x"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
    "outputs": [],
@@ -1385,10 +690,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
    "outputs": [],
@@ -1398,26 +706,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure()\n",
     "plt.plot(x,y)\n",
@@ -1426,252 +724,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/leninml/anaconda3/envs/MachineLearning/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:1316: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
-      "  y = column_or_1d(y, warn=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iteration 1, loss = 102.72146739\n",
-      "Iteration 2, loss = 91.67507298\n",
-      "Iteration 3, loss = 80.79855813\n",
-      "Iteration 4, loss = 70.21359290\n",
-      "Iteration 5, loss = 59.52873038\n",
-      "Iteration 6, loss = 49.17332621\n",
-      "Iteration 7, loss = 39.17911589\n",
-      "Iteration 8, loss = 30.05589102\n",
-      "Iteration 9, loss = 22.13170099\n",
-      "Iteration 10, loss = 15.68461454\n",
-      "Iteration 11, loss = 10.80280250\n",
-      "Iteration 12, loss = 7.31788942\n",
-      "Iteration 13, loss = 5.30866319\n",
-      "Iteration 14, loss = 4.26833852\n",
-      "Iteration 15, loss = 3.76771028\n",
-      "Iteration 16, loss = 3.53851576\n",
-      "Iteration 17, loss = 3.42237284\n",
-      "Iteration 18, loss = 3.32479950\n",
-      "Iteration 19, loss = 3.23269111\n",
-      "Iteration 20, loss = 3.14430071\n",
-      "Iteration 21, loss = 3.06049025\n",
-      "Iteration 22, loss = 2.98391399\n",
-      "Iteration 23, loss = 2.90511158\n",
-      "Iteration 24, loss = 2.83270154\n",
-      "Iteration 25, loss = 2.76281149\n",
-      "Iteration 26, loss = 2.69675328\n",
-      "Iteration 27, loss = 2.63276435\n",
-      "Iteration 28, loss = 2.56992920\n",
-      "Iteration 29, loss = 2.50998481\n",
-      "Iteration 30, loss = 2.45241838\n",
-      "Iteration 31, loss = 2.39688771\n",
-      "Iteration 32, loss = 2.34260634\n",
-      "Iteration 33, loss = 2.29003072\n",
-      "Iteration 34, loss = 2.23855751\n",
-      "Iteration 35, loss = 2.19154494\n",
-      "Iteration 36, loss = 2.14108414\n",
-      "Iteration 37, loss = 2.09733350\n",
-      "Iteration 38, loss = 2.05079276\n",
-      "Iteration 39, loss = 2.00536804\n",
-      "Iteration 40, loss = 1.96142333\n",
-      "Iteration 41, loss = 1.92041004\n",
-      "Iteration 42, loss = 1.87895965\n",
-      "Iteration 43, loss = 1.83968009\n",
-      "Iteration 44, loss = 1.80110220\n",
-      "Iteration 45, loss = 1.76328102\n",
-      "Iteration 46, loss = 1.72733003\n",
-      "Iteration 47, loss = 1.69119277\n",
-      "Iteration 48, loss = 1.65670389\n",
-      "Iteration 49, loss = 1.62334729\n",
-      "Iteration 50, loss = 1.59059187\n",
-      "Iteration 51, loss = 1.55914786\n",
-      "Iteration 52, loss = 1.52830292\n",
-      "Iteration 53, loss = 1.49773725\n",
-      "Iteration 54, loss = 1.46855568\n",
-      "Iteration 55, loss = 1.44150889\n",
-      "Iteration 56, loss = 1.41249967\n",
-      "Iteration 57, loss = 1.38582988\n",
-      "Iteration 58, loss = 1.36058208\n",
-      "Iteration 59, loss = 1.33341582\n",
-      "Iteration 60, loss = 1.30797419\n",
-      "Iteration 61, loss = 1.28269430\n",
-      "Iteration 62, loss = 1.25914355\n",
-      "Iteration 63, loss = 1.23508937\n",
-      "Iteration 64, loss = 1.21231723\n",
-      "Iteration 65, loss = 1.18834292\n",
-      "Iteration 66, loss = 1.16699686\n",
-      "Iteration 67, loss = 1.14366561\n",
-      "Iteration 68, loss = 1.12205057\n",
-      "Iteration 69, loss = 1.09979041\n",
-      "Iteration 70, loss = 1.07911158\n",
-      "Iteration 71, loss = 1.05776047\n",
-      "Iteration 72, loss = 1.03731743\n",
-      "Iteration 73, loss = 1.01666299\n",
-      "Iteration 74, loss = 0.99793791\n",
-      "Iteration 75, loss = 0.97575707\n",
-      "Iteration 76, loss = 0.95726795\n",
-      "Iteration 77, loss = 0.93694778\n",
-      "Iteration 78, loss = 0.91837142\n",
-      "Iteration 79, loss = 0.89892520\n",
-      "Iteration 80, loss = 0.88043703\n",
-      "Iteration 81, loss = 0.86065399\n",
-      "Iteration 82, loss = 0.84295136\n",
-      "Iteration 83, loss = 0.82429669\n",
-      "Iteration 84, loss = 0.80565979\n",
-      "Iteration 85, loss = 0.78805012\n",
-      "Iteration 86, loss = 0.77138673\n",
-      "Iteration 87, loss = 0.75185955\n",
-      "Iteration 88, loss = 0.73565640\n",
-      "Iteration 89, loss = 0.71953590\n",
-      "Iteration 90, loss = 0.70067858\n",
-      "Iteration 91, loss = 0.68436221\n",
-      "Iteration 92, loss = 0.66819687\n",
-      "Iteration 93, loss = 0.65171251\n",
-      "Iteration 94, loss = 0.63594252\n",
-      "Iteration 95, loss = 0.62021160\n",
-      "Iteration 96, loss = 0.60516413\n",
-      "Iteration 97, loss = 0.59038901\n",
-      "Iteration 98, loss = 0.57621455\n",
-      "Iteration 99, loss = 0.56143158\n",
-      "Iteration 100, loss = 0.54675315\n",
-      "Iteration 101, loss = 0.53377003\n",
-      "Iteration 102, loss = 0.51935108\n",
-      "Iteration 103, loss = 0.50684153\n",
-      "Iteration 104, loss = 0.49358092\n",
-      "Iteration 105, loss = 0.48074202\n",
-      "Iteration 106, loss = 0.46846204\n",
-      "Iteration 107, loss = 0.45687594\n",
-      "Iteration 108, loss = 0.44479298\n",
-      "Iteration 109, loss = 0.43373647\n",
-      "Iteration 110, loss = 0.42288257\n",
-      "Iteration 111, loss = 0.41130332\n",
-      "Iteration 112, loss = 0.40172343\n",
-      "Iteration 113, loss = 0.39216068\n",
-      "Iteration 114, loss = 0.38031346\n",
-      "Iteration 115, loss = 0.37095995\n",
-      "Iteration 116, loss = 0.36201353\n",
-      "Iteration 117, loss = 0.35190584\n",
-      "Iteration 118, loss = 0.34250765\n",
-      "Iteration 119, loss = 0.33421027\n",
-      "Iteration 120, loss = 0.32539846\n",
-      "Iteration 121, loss = 0.31715134\n",
-      "Iteration 122, loss = 0.30919114\n",
-      "Iteration 123, loss = 0.30106225\n",
-      "Iteration 124, loss = 0.29419971\n",
-      "Iteration 125, loss = 0.28635944\n",
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-      "Iteration 127, loss = 0.27188335\n",
-      "Iteration 128, loss = 0.26595344\n",
-      "Iteration 129, loss = 0.25996664\n",
-      "Iteration 130, loss = 0.25171637\n",
-      "Iteration 131, loss = 0.24575853\n",
-      "Iteration 132, loss = 0.23953071\n",
-      "Iteration 133, loss = 0.23380421\n",
-      "Iteration 134, loss = 0.22757049\n",
-      "Iteration 135, loss = 0.22248892\n",
-      "Iteration 136, loss = 0.21626585\n",
-      "Iteration 137, loss = 0.21212836\n",
-      "Iteration 138, loss = 0.20523178\n",
-      "Iteration 139, loss = 0.20071245\n",
-      "Iteration 140, loss = 0.19558895\n",
-      "Iteration 141, loss = 0.19103498\n",
-      "Iteration 142, loss = 0.18608032\n",
-      "Iteration 143, loss = 0.18133562\n",
-      "Iteration 144, loss = 0.17724838\n",
-      "Iteration 145, loss = 0.17288177\n",
-      "Iteration 146, loss = 0.16904197\n",
-      "Iteration 147, loss = 0.16413811\n",
-      "Iteration 148, loss = 0.16009184\n",
-      "Iteration 149, loss = 0.15621559\n",
-      "Iteration 150, loss = 0.15270345\n",
-      "Iteration 151, loss = 0.14869934\n",
-      "Iteration 152, loss = 0.14616290\n",
-      "Iteration 153, loss = 0.14169750\n",
-      "Iteration 154, loss = 0.13818625\n",
-      "Iteration 155, loss = 0.13486399\n",
-      "Iteration 156, loss = 0.13132768\n",
-      "Iteration 157, loss = 0.12833580\n",
-      "Iteration 158, loss = 0.12565324\n",
-      "Iteration 159, loss = 0.12253017\n",
-      "Iteration 160, loss = 0.12000857\n",
-      "Iteration 161, loss = 0.11635006\n",
-      "Iteration 162, loss = 0.11350800\n",
-      "Iteration 163, loss = 0.11131935\n",
-      "Iteration 164, loss = 0.10810058\n",
-      "Iteration 165, loss = 0.10583669\n",
-      "Iteration 166, loss = 0.10295002\n",
-      "Iteration 167, loss = 0.10075140\n",
-      "Iteration 168, loss = 0.09836778\n",
-      "Iteration 169, loss = 0.09613109\n",
-      "Iteration 170, loss = 0.09368638\n",
-      "Iteration 171, loss = 0.09146311\n",
-      "Iteration 172, loss = 0.08914574\n",
-      "Iteration 173, loss = 0.08722990\n",
-      "Iteration 174, loss = 0.08517794\n",
-      "Iteration 175, loss = 0.08347261\n",
-      "Iteration 176, loss = 0.08137914\n",
-      "Iteration 177, loss = 0.07944792\n",
-      "Iteration 178, loss = 0.07774641\n",
-      "Iteration 179, loss = 0.07618717\n",
-      "Iteration 180, loss = 0.07420839\n",
-      "Iteration 181, loss = 0.07235059\n",
-      "Iteration 182, loss = 0.07078334\n",
-      "Iteration 183, loss = 0.06923895\n",
-      "Iteration 184, loss = 0.06755768\n",
-      "Iteration 185, loss = 0.06592068\n",
-      "Iteration 186, loss = 0.06473508\n",
-      "Iteration 187, loss = 0.06348529\n",
-      "Iteration 188, loss = 0.06171428\n",
-      "Iteration 189, loss = 0.06043116\n",
-      "Iteration 190, loss = 0.05921929\n",
-      "Iteration 191, loss = 0.05885309\n",
-      "Iteration 192, loss = 0.05678889\n",
-      "Iteration 193, loss = 0.05524177\n",
-      "Iteration 194, loss = 0.05409928\n",
-      "Iteration 195, loss = 0.05295601\n",
-      "Iteration 196, loss = 0.05204670\n",
-      "Iteration 197, loss = 0.05073245\n",
-      "Iteration 198, loss = 0.04978785\n",
-      "Iteration 199, loss = 0.04870070\n",
-      "Iteration 200, loss = 0.04760799\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/leninml/anaconda3/envs/MachineLearning/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "MLPRegressor(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
-       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
-       "       hidden_layer_sizes=(500,), learning_rate='constant',\n",
-       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
-       "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
-       "       random_state=None, shuffle=True, solver='adam', tol=0.0001,\n",
-       "       validation_fraction=0.1, verbose=1, warm_start=False)"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.neural_network import MLPRegressor\n",
     "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42)\n",
@@ -1681,34 +743,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.9989574284085255"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "red_funcion.score(x_test,y_test)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
    "outputs": [],
@@ -1724,26 +781,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure()\n",
     "plt.plot(x_test, y_real, color='blue')\n",
@@ -1764,24 +811,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "227.0"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "x_num = 16.\n",
     "y_num = x_num*x_num-2*x_num+3\n",
@@ -1790,24 +829,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([83.6692473])"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "x_num = np.asarray(x_num)\n",
     "x_num = x_num.reshape(-1,1)\n",
@@ -1829,21 +860,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.datasets import load_iris\n",
     "\n",
@@ -1864,28 +890,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[5.1, 3.5, 1.4, 0.2],\n",
-       "       [4.9, 3. , 1.4, 0.2],\n",
-       "       [4.7, 3.2, 1.3, 0.2],\n",
-       "       [4.6, 3.1, 1.5, 0.2],\n",
-       "       [5. , 3.6, 1.4, 0.2]])"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "iris_dataset['data'][0:5,:]\n"
    ]
@@ -1903,24 +917,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 0, 0, 0, 0])"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "iris_dataset['target'][0:5]"
    ]
@@ -1938,10 +944,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
    "outputs": [],
@@ -1963,180 +972,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
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-       "       [0., 0., 1.],\n",
-       "       [0., 0., 1.],\n",
-       "       [0., 0., 1.],\n",
-       "       [0., 0., 1.],\n",
-       "       [0., 0., 1.],\n",
-       "       [0., 0., 1.],\n",
-       "       [0., 0., 1.],\n",
-       "       [0., 0., 1.],\n",
-       "       [0., 0., 1.],\n",
-       "       [0., 0., 1.],\n",
-       "       [0., 0., 1.],\n",
-       "       [0., 0., 1.],\n",
-       "       [0., 0., 1.],\n",
-       "       [0., 0., 1.],\n",
-       "       [0., 0., 1.],\n",
-       "       [0., 0., 1.],\n",
-       "       [0., 0., 1.]], dtype=float32)"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from keras.utils import np_utils\n",
     "Y_one_hot = np_utils.to_categorical(Y)\n",
@@ -2156,29 +1001,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "La media [5.84333333 3.05733333 3.758      1.19933333]\n",
-      "Escalado de X, se muestra primeras 5 filas \n",
-      "[[5.1 3.5 1.4 0.2]\n",
-      " [4.9 3.  1.4 0.2]\n",
-      " [4.7 3.2 1.3 0.2]\n",
-      " [4.6 3.1 1.5 0.2]\n",
-      " [5.  3.6 1.4 0.2]]\n",
-      "min 0.1\n",
-      "max 7.9\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.preprocessing import StandardScaler\n",
     "scaler = StandardScaler()\n",
@@ -2205,28 +1037,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[6.2, 2.8, 4.8, 1.8],\n",
-       "       [5.1, 3.3, 1.7, 0.5],\n",
-       "       [5.6, 2.9, 3.6, 1.3],\n",
-       "       [7.7, 3.8, 6.7, 2.2],\n",
-       "       [5.4, 3. , 4.5, 1.5]])"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "X_train, X_test, Y_train, Y_test = train_test_split(X_escalado, Y_one_hot, test_size=0.2, random_state=2)\n",
@@ -2235,28 +1055,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0., 0., 1.],\n",
-       "       [1., 0., 0.],\n",
-       "       [0., 1., 0.],\n",
-       "       [0., 0., 1.],\n",
-       "       [0., 1., 0.]], dtype=float32)"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Y_train[0:5]\n"
    ]
@@ -2274,10 +1082,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
    "outputs": [],
@@ -2295,6 +1106,9 @@
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
    "outputs": [],
@@ -2304,1562 +1118,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iteration 1, loss = 2.72180080\n",
-      "Iteration 2, loss = 2.71866401\n",
-      "Iteration 3, loss = 2.71408659\n",
-      "Iteration 4, loss = 2.70815287\n",
-      "Iteration 5, loss = 2.70094656\n",
-      "Iteration 6, loss = 2.69255084\n",
-      "Iteration 7, loss = 2.68304850\n",
-      "Iteration 8, loss = 2.67252214\n",
-      "Iteration 9, loss = 2.66105434\n",
-      "Iteration 10, loss = 2.64872782\n",
-      "Iteration 11, loss = 2.63562552\n",
-      "Iteration 12, loss = 2.62183057\n",
-      "Iteration 13, loss = 2.60742612\n",
-      "Iteration 14, loss = 2.59249500\n",
-      "Iteration 15, loss = 2.57711914\n",
-      "Iteration 16, loss = 2.56137878\n",
-      "Iteration 17, loss = 2.54535152\n",
-      "Iteration 18, loss = 2.52911111\n",
-      "Iteration 19, loss = 2.51272620\n",
-      "Iteration 20, loss = 2.49625906\n",
-      "Iteration 21, loss = 2.47976438\n",
-      "Iteration 22, loss = 2.46328834\n",
-      "Iteration 23, loss = 2.44686792\n",
-      "Iteration 24, loss = 2.43053071\n",
-      "Iteration 25, loss = 2.41429510\n",
-      "Iteration 26, loss = 2.39817099\n",
-      "Iteration 27, loss = 2.38216090\n",
-      "Iteration 28, loss = 2.36626136\n",
-      "Iteration 29, loss = 2.35046465\n",
-      "Iteration 30, loss = 2.33476051\n",
-      "Iteration 31, loss = 2.31913794\n",
-      "Iteration 32, loss = 2.30358671\n",
-      "Iteration 33, loss = 2.28809882\n",
-      "Iteration 34, loss = 2.27266949\n",
-      "Iteration 35, loss = 2.25729788\n",
-      "Iteration 36, loss = 2.24198750\n",
-      "Iteration 37, loss = 2.22674617\n",
-      "Iteration 38, loss = 2.21158581\n",
-      "Iteration 39, loss = 2.19652190\n",
-      "Iteration 40, loss = 2.18157273\n",
-      "Iteration 41, loss = 2.16675864\n",
-      "Iteration 42, loss = 2.15210109\n",
-      "Iteration 43, loss = 2.13762175\n",
-      "Iteration 44, loss = 2.12334172\n",
-      "Iteration 45, loss = 2.10928078\n",
-      "Iteration 46, loss = 2.09545675\n",
-      "Iteration 47, loss = 2.08188505\n",
-      "Iteration 48, loss = 2.06857843\n",
-      "Iteration 49, loss = 2.05554671\n",
-      "Iteration 50, loss = 2.04279687\n",
-      "Iteration 51, loss = 2.03033305\n",
-      "Iteration 52, loss = 2.01815676\n",
-      "Iteration 53, loss = 2.00626717\n",
-      "Iteration 54, loss = 1.99466137\n",
-      "Iteration 55, loss = 1.98333476\n",
-      "Iteration 56, loss = 1.97228134\n",
-      "Iteration 57, loss = 1.96149406\n",
-      "Iteration 58, loss = 1.95096520\n",
-      "Iteration 59, loss = 1.94068657\n",
-      "Iteration 60, loss = 1.93064984\n",
-      "Iteration 61, loss = 1.92084675\n",
-      "Iteration 62, loss = 1.91126928\n",
-      "Iteration 63, loss = 1.90190979\n",
-      "Iteration 64, loss = 1.89276116\n",
-      "Iteration 65, loss = 1.88381681\n",
-      "Iteration 66, loss = 1.87507081\n",
-      "Iteration 67, loss = 1.86651781\n",
-      "Iteration 68, loss = 1.85815306\n",
-      "Iteration 69, loss = 1.84997236\n",
-      "Iteration 70, loss = 1.84197203\n",
-      "Iteration 71, loss = 1.83414876\n",
-      "Iteration 72, loss = 1.82649961\n",
-      "Iteration 73, loss = 1.81902187\n",
-      "Iteration 74, loss = 1.81171299\n",
-      "Iteration 75, loss = 1.80457046\n",
-      "Iteration 76, loss = 1.79759180\n",
-      "Iteration 77, loss = 1.79077440\n",
-      "Iteration 78, loss = 1.78411552\n",
-      "Iteration 79, loss = 1.77761223\n",
-      "Iteration 80, loss = 1.77126135\n",
-      "Iteration 81, loss = 1.76505949\n",
-      "Iteration 82, loss = 1.75900297\n",
-      "Iteration 83, loss = 1.75308791\n",
-      "Iteration 84, loss = 1.74731019\n",
-      "Iteration 85, loss = 1.74166551\n",
-      "Iteration 86, loss = 1.73614942\n",
-      "Iteration 87, loss = 1.73075738\n",
-      "Iteration 88, loss = 1.72548477\n",
-      "Iteration 89, loss = 1.72032699\n",
-      "Iteration 90, loss = 1.71527947\n",
-      "Iteration 91, loss = 1.71033772\n",
-      "Iteration 92, loss = 1.70549737\n",
-      "Iteration 93, loss = 1.70075420\n",
-      "Iteration 94, loss = 1.69610419\n",
-      "Iteration 95, loss = 1.69154349\n",
-      "Iteration 96, loss = 1.68706849\n",
-      "Iteration 97, loss = 1.68267577\n",
-      "Iteration 98, loss = 1.67836215\n",
-      "Iteration 99, loss = 1.67412465\n",
-      "Iteration 100, loss = 1.66996048\n",
-      "Iteration 101, loss = 1.66586707\n",
-      "Iteration 102, loss = 1.66184197\n",
-      "Iteration 103, loss = 1.65788294\n",
-      "Iteration 104, loss = 1.65398782\n",
-      "Iteration 105, loss = 1.65015462\n",
-      "Iteration 106, loss = 1.64638139\n",
-      "Iteration 107, loss = 1.64266632\n",
-      "Iteration 108, loss = 1.63900763\n",
-      "Iteration 109, loss = 1.63540361\n",
-      "Iteration 110, loss = 1.63185258\n",
-      "Iteration 111, loss = 1.62835294\n",
-      "Iteration 112, loss = 1.62490306\n",
-      "Iteration 113, loss = 1.62150140\n",
-      "Iteration 114, loss = 1.61814642\n",
-      "Iteration 115, loss = 1.61483659\n",
-      "Iteration 116, loss = 1.61157045\n",
-      "Iteration 117, loss = 1.60834653\n",
-      "Iteration 118, loss = 1.60516343\n",
-      "Iteration 119, loss = 1.60201977\n",
-      "Iteration 120, loss = 1.59891420\n",
-      "Iteration 121, loss = 1.59584543\n",
-      "Iteration 122, loss = 1.59281221\n",
-      "Iteration 123, loss = 1.58981335\n",
-      "Iteration 124, loss = 1.58684769\n",
-      "Iteration 125, loss = 1.58391414\n",
-      "Iteration 126, loss = 1.58101166\n",
-      "Iteration 127, loss = 1.57813926\n",
-      "Iteration 128, loss = 1.57529600\n",
-      "Iteration 129, loss = 1.57248100\n",
-      "Iteration 130, loss = 1.56969343\n",
-      "Iteration 131, loss = 1.56693248\n",
-      "Iteration 132, loss = 1.56419744\n",
-      "Iteration 133, loss = 1.56148758\n",
-      "Iteration 134, loss = 1.55880226\n",
-      "Iteration 135, loss = 1.55614084\n",
-      "Iteration 136, loss = 1.55350274\n",
-      "Iteration 137, loss = 1.55088740\n",
-      "Iteration 138, loss = 1.54829429\n",
-      "Iteration 139, loss = 1.54572290\n",
-      "Iteration 140, loss = 1.54317274\n",
-      "Iteration 141, loss = 1.54064336\n",
-      "Iteration 142, loss = 1.53813431\n",
-      "Iteration 143, loss = 1.53564517\n",
-      "Iteration 144, loss = 1.53317552\n",
-      "Iteration 145, loss = 1.53072498\n",
-      "Iteration 146, loss = 1.52829315\n",
-      "Iteration 147, loss = 1.52587969\n",
-      "Iteration 148, loss = 1.52348422\n",
-      "Iteration 149, loss = 1.52110641\n",
-      "Iteration 150, loss = 1.51874594\n",
-      "Iteration 151, loss = 1.51640247\n",
-      "Iteration 152, loss = 1.51407572\n",
-      "Iteration 153, loss = 1.51176537\n",
-      "Iteration 154, loss = 1.50947116\n",
-      "Iteration 155, loss = 1.50719280\n",
-      "Iteration 156, loss = 1.50493005\n",
-      "Iteration 157, loss = 1.50268264\n",
-      "Iteration 158, loss = 1.50045034\n",
-      "Iteration 159, loss = 1.49823293\n",
-      "Iteration 160, loss = 1.49603019\n",
-      "Iteration 161, loss = 1.49384190\n",
-      "Iteration 162, loss = 1.49166788\n",
-      "Iteration 163, loss = 1.48950793\n",
-      "Iteration 164, loss = 1.48736187\n",
-      "Iteration 165, loss = 1.48522953\n",
-      "Iteration 166, loss = 1.48311074\n",
-      "Iteration 167, loss = 1.48100536\n",
-      "Iteration 168, loss = 1.47891322\n",
-      "Iteration 169, loss = 1.47683419\n",
-      "Iteration 170, loss = 1.47476812\n",
-      "Iteration 171, loss = 1.47271488\n",
-      "Iteration 172, loss = 1.47067435\n",
-      "Iteration 173, loss = 1.46864640\n",
-      "Iteration 174, loss = 1.46663092\n",
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-      "Iteration 176, loss = 1.46263687\n",
-      "Iteration 177, loss = 1.46065808\n",
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-      "Iteration 184, loss = 1.44713754\n",
-      "Iteration 185, loss = 1.44525215\n",
-      "Iteration 186, loss = 1.44337803\n",
-      "Iteration 187, loss = 1.44151507\n",
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-      "Iteration 189, loss = 1.43782234\n",
-      "Iteration 190, loss = 1.43599240\n",
-      "Iteration 191, loss = 1.43417329\n",
-      "Iteration 192, loss = 1.43236496\n",
-      "Iteration 193, loss = 1.43056730\n",
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-      "Iteration 195, loss = 1.42700375\n",
-      "Iteration 196, loss = 1.42523771\n",
-      "Iteration 197, loss = 1.42348205\n",
-      "Iteration 198, loss = 1.42173671\n",
-      "Iteration 199, loss = 1.42000162\n",
-      "Iteration 200, loss = 1.41827671\n",
-      "Iteration 201, loss = 1.41656191\n",
-      "Iteration 202, loss = 1.41485715\n",
-      "Iteration 203, loss = 1.41316238\n",
-      "Iteration 204, loss = 1.41147751\n",
-      "Iteration 205, loss = 1.40980249\n",
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-      "Iteration 1421, loss = 0.96730287\n",
-      "Iteration 1422, loss = 0.96700192\n",
-      "Iteration 1423, loss = 0.96670001\n",
-      "Iteration 1424, loss = 0.96639715\n",
-      "Iteration 1425, loss = 0.96609332\n",
-      "Iteration 1426, loss = 0.96578852\n",
-      "Iteration 1427, loss = 0.96548275\n",
-      "Iteration 1428, loss = 0.96517598\n",
-      "Iteration 1429, loss = 0.96486822\n",
-      "Iteration 1430, loss = 0.96455946\n",
-      "Iteration 1431, loss = 0.96424969\n",
-      "Iteration 1432, loss = 0.96393891\n",
-      "Iteration 1433, loss = 0.96362710\n",
-      "Iteration 1434, loss = 0.96331425\n",
-      "Iteration 1435, loss = 0.96300036\n",
-      "Iteration 1436, loss = 0.96268542\n",
-      "Iteration 1437, loss = 0.96236943\n",
-      "Iteration 1438, loss = 0.96205237\n",
-      "Iteration 1439, loss = 0.96173423\n",
-      "Iteration 1440, loss = 0.96141500\n",
-      "Iteration 1441, loss = 0.96109469\n",
-      "Iteration 1442, loss = 0.96077327\n",
-      "Iteration 1443, loss = 0.96045074\n",
-      "Iteration 1444, loss = 0.96012709\n",
-      "Iteration 1445, loss = 0.95980232\n",
-      "Iteration 1446, loss = 0.95947640\n",
-      "Iteration 1447, loss = 0.95914934\n",
-      "Iteration 1448, loss = 0.95882111\n",
-      "Iteration 1449, loss = 0.95849172\n",
-      "Iteration 1450, loss = 0.95816116\n",
-      "Iteration 1451, loss = 0.95782940\n",
-      "Iteration 1452, loss = 0.95749645\n",
-      "Iteration 1453, loss = 0.95716229\n",
-      "Iteration 1454, loss = 0.95682691\n",
-      "Iteration 1455, loss = 0.95649031\n",
-      "Iteration 1456, loss = 0.95615246\n",
-      "Iteration 1457, loss = 0.95581337\n",
-      "Iteration 1458, loss = 0.95547301\n",
-      "Iteration 1459, loss = 0.95513138\n",
-      "Iteration 1460, loss = 0.95478847\n",
-      "Iteration 1461, loss = 0.95444427\n",
-      "Iteration 1462, loss = 0.95409876\n",
-      "Iteration 1463, loss = 0.95375193\n",
-      "Iteration 1464, loss = 0.95340378\n",
-      "Iteration 1465, loss = 0.95305428\n",
-      "Iteration 1466, loss = 0.95270343\n",
-      "Iteration 1467, loss = 0.95235122\n",
-      "Iteration 1468, loss = 0.95199762\n",
-      "Iteration 1469, loss = 0.95164264\n",
-      "Iteration 1470, loss = 0.95128626\n",
-      "Iteration 1471, loss = 0.95092846\n",
-      "Iteration 1472, loss = 0.95056923\n",
-      "Iteration 1473, loss = 0.95020856\n",
-      "Iteration 1474, loss = 0.94984644\n",
-      "Iteration 1475, loss = 0.94948285\n",
-      "Iteration 1476, loss = 0.94911778\n",
-      "Iteration 1477, loss = 0.94875121\n",
-      "Iteration 1478, loss = 0.94838314\n",
-      "Iteration 1479, loss = 0.94801354\n",
-      "Iteration 1480, loss = 0.94764240\n",
-      "Iteration 1481, loss = 0.94726971\n",
-      "Iteration 1482, loss = 0.94689546\n",
-      "Iteration 1483, loss = 0.94651962\n",
-      "Iteration 1484, loss = 0.94614219\n",
-      "Iteration 1485, loss = 0.94576315\n",
-      "Iteration 1486, loss = 0.94538248\n",
-      "Iteration 1487, loss = 0.94500017\n",
-      "Iteration 1488, loss = 0.94461620\n",
-      "Iteration 1489, loss = 0.94423056\n",
-      "Iteration 1490, loss = 0.94384323\n",
-      "Iteration 1491, loss = 0.94345420\n",
-      "Iteration 1492, loss = 0.94306344\n",
-      "Iteration 1493, loss = 0.94267094\n",
-      "Iteration 1494, loss = 0.94227669\n",
-      "Iteration 1495, loss = 0.94188067\n",
-      "Iteration 1496, loss = 0.94148286\n",
-      "Iteration 1497, loss = 0.94108324\n",
-      "Iteration 1498, loss = 0.94068180\n",
-      "Iteration 1499, loss = 0.94027852\n",
-      "Iteration 1500, loss = 0.93987338\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/leninml/anaconda3/envs/MachineLearning/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1500) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "MLPClassifier(activation='tanh', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
-       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
-       "       hidden_layer_sizes=(3,), learning_rate='constant',\n",
-       "       learning_rate_init=0.001, max_iter=1500, momentum=0.95,\n",
-       "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
-       "       random_state=None, shuffle=True, solver='sgd', tol=0.0001,\n",
-       "       validation_fraction=0.1, verbose=1, warm_start=False)"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "red_iris.fit(X_train, Y_train)"
    ]
@@ -3877,24 +1145,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.8583333333333333"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "red_iris.score(X_train, Y_train)"
    ]
@@ -3912,24 +1172,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.8333333333333334"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "red_iris.score(X_test, Y_test)"
    ]
@@ -3947,41 +1199,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "pycharm": {
+     "name": "#%%\n",
+     "is_executing": true
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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p6x7gpc2a0y8iyUfBP4YrF1VSWZjND9/cGXYpIiJxp+AfQ0Z6GjecV8dLm1tp3t8XdjkiInGl4B/HLRfNxYAHX9kedikiInGl4B/H7JJcrltcw2NvvE9n38GwyxERiRsF/zHc8aH59A4O84MV74ddiohI3Cj4j+GM2mIuPbmcB1/dTt+grscrIslBwT+Bu35vIW3dAzz8+o6wSxERiQsF/wQumFfG5Qsrue/X79LVr7F+ETnxKfhj8D+vWsT+Awf53svbwi5FRGTKFPwxOLOumOsW17Ds5W3s7DgQdjkiIlOi4I/RvdeeSpoZ//jTTWGXIiIyJQr+GNWW5PKZD5/Mz97azStb2sMuR0TkuCn4J+GOD81jbnke/+vp9ZreKSInLAX/JGRnpPPPf3QmO/Ye4Csvbg67HBGR46Lgn6RLTqrglovm8sCr21m5o2PiJ4iIJBgF/3G455pTqC3J5XOPr9OQj4iccBT8xyE/O4N/uWExO/b28nfPvBV2OSIik6LgP06XnFTBp684iR+u3MlPdH1eETmBxHKx9QfNrNXMNoyz/Aoz6zSzNcHtC1HLrjazzWa21czuiWfhieCvPrKQxrmlfP7J9Wxv7534CSIiCSCWPf6HgKsn6PMbdz87uH0RwMzSgW8B1wCnATeZ2WlTKTbRZKSn8Y2bziEzI40///dVHBgcCrskEZEJTRj87v4ycDzTVy4Atrr7NncfBB4Drj+O35PQZpfk8rU/PZvNe7r53I/X4e5hlyQickzxGuO/2MzWmtnzZnZ60FYLRF+tvCloSzpXLKrinqtP4afrW/jmL7eGXY6IyDFlxOF3rAbmunuPmV0LPA0smOwvMbOlwFKA+vr6OJQ1s5ZeNp+3d3fzleXvsLC6kI+eXh12SSIiY5ryHr+7d7l7T3D/OSDTzCqAZmBOVNe6oG2837PM3RvdvbGysnKqZc04M+Of/+hMzppTwl2P/Y5V7+0LuyQRkTFNOfjNrNrMLLh/QfA79wJvAgvMbJ6ZZQE3As9M9fUSWU5mOg/c1kh1UQ63P/wmW1t7wi5JROQosUznfBR4HVhkZk1mdruZ3WlmdwZdbgA2mNla4BvAjR4xBHwGeAHYBPzI3ZP+bKeKgmwe/tQFZKQZtz34Bnu6+sMuSUTkMJaIs1AaGxt95cqVYZcxJeubOrlx2evUluby6H+9iPKC7LBLEpEkZmar3L0xlr46c3eanFlXzPdua+T9jgPcfP8KOnoHwy5JRARQ8E+rS06q4P5bz2d7ey9/dv8K9h9Q+ItI+BT802zJggq+d2sjW9t6uPn+FbT3DIRdkoikOAX/DLhsYSXLbjmPd9t6+JP7Xqdpny7YLiLhUfDPkCsWVfHvt19Ie88AN3zndbbs6Q67JBFJUQr+GdTYUMYP/9vFDLvzx999XVfwEpFQKPhn2Kk1RTxx5yWU5mXxX763gidWNYVdkoikGAV/COrL83jq05fQ2FDK3T9eyz8/v4nhQ4l3PoWIJCcFf0hK8rJ4+FMXcPOF9Xz319tY+v2VdB44GHZZIpICFPwhykxP4x/+8Ay+eP3pvLyljWu/8RvW7NwfdlkikuQU/CEzM269uIEf33kJAH9832s8+Mp2XdBFRKaNgj9BnD2nhJ/+9yVcvrCSLz67kTseXkmrvuBNRKaBgj+BlORl8b1bG/nCdafxytZ2rvray/zH2l1hlyUiSUbBn2DMjE8tmcdzd32IhvJ8/vLR3/EXj6zWVz2ISNwo+BPUSZUFPH7nxfz11Yt4ceNuPvyvv+Lffvuepn2KyJQp+BNYRnoan77iZJ6/6zLOqC3mfz+9gY9/+1XWauaPiEyBgv8EcHJVAY/ccSFfv/FsWjr7+cNvv8rnfryWls6+sEsTkROQgv8EYWZcf3Ytv7j7cu5YMo+frNnFFV/+FV/62dt09unELxGJnS69eILa2XGAr7y4mafX7KIkL5NPX3ESN184l/zsjLBLE5EQxPXSi2b2oJm1mtmGcZbfbGbrzGy9mb1mZmdFLdsRtK8xMyV5HM0py+NrN57Ds3+5hDNri/mn595myZd+ybde2kpXvz4BiMj4JtzjN7PLgB7g++5+xhjLLwE2ufs+M7sG+Dt3vzBYtgNodPf2yRSlPf7JW/XePr75yy28tLmNopwMPnFJA7dc3EBloS7yLpIKJrPHH9NQj5k1AM+OFfxH9CsFNrh7bfB4Bwr+GbWhuZP/+8stvPDWHrLS0/jYWbP55KUNnFFbHHZpIjKNJhP88R4Qvh14PuqxAy+amQPfdfdlcX49OcIZtcV895ZG3m3r4eHXdvD4qiaeWN3EBQ1l3HZJAx85rYrsjPSwyxSREMVtj9/MrgS+DSxx971BW627N5tZFbAc+Et3f3mc5y8FlgLU19ef9957703yT5GxdPYd5Edv7uSh13bQvL+Psvws/vDsWv70/Dksqi4MuzwRiZMZH+oxs8XAU8A17v7OOH3+Duhx93+d6PU01BN/w4ecl7e08aM3d/LzTXs4OOycNaeEP2ms45ozaijLzwq7RBGZghkNfjOrB34J3Orur0W15wNp7t4d3F8OfNHdfzbR6yn4p9fengGe+l0zP1q5k3f29JCeZiw5uYKPnTWbq06fRVFOZtglisgkxTX4zexR4AqgAtgD/C2QCeDu95nZ/cB/BkbGZobcvdHM5hP5FACRYwk/cPd/jKUoBf/McHc2tnTx7LoW/mPtLpr29ZGVnsZlCyv56Omz+PApVZQXaFaQyIkg7nv8M03BP/PcnbVNnfzH2l08t76Fls5+zODc+lI+cuosPnJqFSdXFWBmYZcqImNQ8MuUuDtv7eri55v28PNNe9jQ3AXA3PI8PrSggiUnV3Dx/AqK8zQkJJIoFPwSVy2dffxiUysvvd3Kb7ftpXdwmDSDM+tKWHJyOZeeXMG59aXkZGqaqEhYFPwybQ4OH2Ltzv28srWdV7e287v39zN0yMlKT2NxXTHnNZRy/twyzptbSqlmConMGAW/zJiegSFWbNvLG9s7eHNHB+ubOzk4HPk/dXJVAY1zSzl3bimL64o5ubKAjHR9IazIdFDwS2j6Dw6zrqmTN3d0sOq9fazc0UFX/xAAOZlpnD67mDNri1lcF7nNqyggPU0HjEWmSsEvCePQIWf73l7WN3WyrqmT9c372dDcRd/BYQDys9I5paaIRdWFnFJdyKJZhZxSXaQDxyKTpOCXhDZ8yHm3rSeyIWjaz6aWbt7e3TX6yQCgpjiHRdWFoxuEkyoLmF9ZQIGuNyAypjC/pE1kQulpxsJZhSycVcgN59UBkSmku7v6eXt3N5uD26aWLl7d2j56zACgqjCb+ZX5zK8sYH5FfrBByKeuNE9DRiIxUvBLQjAzaopzqSnO5cpFVaPtB4cPsaO9l3fbeni3rZdtbb1sa+/hp+taDrvkZFZ6GnPL86gvy2NOcKsvG3mcS16W/quLjNC7QRJaZnoaC2YVsmDW4d8k6u509A6yrb2XbcFGYXt7Lzs7DoyeaxCtoiBrdGMwpzSyMagpzmV2SQ41xbm6ZKWkFP1vlxOSmVFekE15QTbnN5Qdtmxko7BzXx/vdxxgZ3B7v+MAq9/fx7PrWhg+dPixraKcDGaX5FJTnENNSS6ziyMbhJqSHGYX51JdnKMT1CRpKPgl6URvFM6eU3LU8oPDh9jd2U9LZz8tnX3s2j/yM3J/zc797Dtw9HWLy/KzqCrMprIwm6rCHKqKsplVmE1VUQ5VUW3aQEiiU/BLyslMTxs9DjCevsFhWjr7aOnsZ9f+yM/dXf20dg3Q1t3P1tYe2roHGDp09Ky4wpyMwzYEI/crCrOoKMimPD+bioIsyvKzdEKbhELBLzKG3Kz0yMyhyoJx+xw65HQcGKS1a4DW7n5auwdo6x6gtStyf09XP6ve20dr9wCDQ4fG/B2leZmRTyf5WVQUZlORn0V5QXZkA1GQRUVBsLEoyCY/K13fjipxoeAXOU5paUZFENKnUTRuP3enq2+I9t4B9vYMsrdngPbeQdq7B9g72jbIpl1dtPcMHHY+Q7TsjLTg9bIO21iU52dFbSgiP8vy9GlCxqfgF5lmZkZxXibFeZmcVDlx/8GhQ3T0DtLeM0B7T7Bh6B2gvWdw9HFrdz8bd3Wxt3fgsPMcPnhNKM3Lojw/a3SDUHHExqK8IJvKYEORp08TKUXBL5JgsjLSqC7Oobo4Z8K+7k5X/9AHG4hgY9E+srHojvzcuKuLtp4Busf5NJGTmTY6pFQRtbEoDz5hjHySKM/Ppiw/SyfLneAU/CInMDOjODeT4tzYPk0MDA1HPk10D44OPUU2GpH7bT0DtHT2s2FXJ3t7Bsc8eG0GZXmHbxwiw00ffLKIHNTOoaJAQ06JSMEvkkKyM9JHz5CeyKFDTlf/wQ8+QURtJD44RjHI+qb9tPcM0jNw9KcJMyjPzzpqhtPo/WAqbGVhNtkZmgY7U2IKfjN7ELgOaHX3M8ZYbsDXgWuBA8An3H11sOw24H8FXf/B3R+OR+EiMr3S0oySvCxK8rI4uWri/v0Hh9nbO3jYzKbWw+5Hjku09wwwxgcJSvIyqSrMZlZRzgfnShRGPj3MKsqhpjiHWUU5ZOoTxJTFusf/EPBN4PvjLL8GWBDcLgS+A1xoZmXA3wKNgAOrzOwZd983laJFJPHkZKZTW5JLbcmxP00MH3L29g58MA22a2B0wzBy/93WHtp6jj5wbQaVBdmRM6yDM6pnl+RQXRw527paG4eYxBT87v6ymTUco8v1wPc98h3PvzWzEjOrAa4Alrt7B4CZLQeuBh6dStEicuJKT7Ngbz4HKB6336FDzv6+g7R297O7s/+ws61bOvvZ2tbDb7a0HfW9TGaRb3GN3hjUluRSV5pLXWnku5pS/XoP8RrjrwV2Rj1uCtrGaz+KmS0FlgLU19fHqSwROVGlpRll+ZEznE+pHv88ia7+g+wOzrDe3dnPrs5+dgcbhy2tPbz8ztEbh8KcjNEv65tTmkddae7o2dx1pcn/ba4J89e5+zJgGUQuxBJyOSJygijKyaQoJ5OFR3yD64iRE+h27jtA074D7OzoY+e+yBf3bWvr5dfvtNF/8PAzq0e+zXVeeT7zKvKZVxn8rMhPio1CvP6CZmBO1OO6oK2ZyHBPdPuv4vSaIiIT+uAEumLOqD16aMndae8ZHN0YNO3ro2nfAd7bG/mK7yd/13xY/+qiHOZV5NNQkc/8YGMwvzKfueX5J8z5DfEK/meAz5jZY0QO7na6e4uZvQD8k5mVBv2uAu6N02uKiEyZmVEZTCk9t770qOV9g8Ps2Bu53sP29sjFgLa39/DCW7vp6B0c7ZeVkcaCqgIWzSpkYXD96IXVhcwuzkm4s6Jjnc75KJE99wozayIyUycTwN3vA54jMpVzK5HpnJ8MlnWY2d8Dbwa/6osjB3pFRE4EuVnpnFpTxKk1Rx9n2H9gkO3tvWxt7WFLaw+bd3fz+hGfEgqyM1g4q4BF1YWcPruYxXXFLKouDPW8BV1sXUQkzjr7DrJlTzdv7+7mnT2Ra0i/vbt79HKhmemR604vrosMP51ZO/WNwWQutq7gFxGZAe5O074+1jd3sq6pkw3Nnaxv7hzdGGSlp3H2nBIeW3oRacdxrGAywX/iH54WETkBmNnolNFrz6wBIhuDnR0jG4P9dPYdPK7QnywFv4hISMyM+vI86svz+E+La2bsdXVes4hIilHwi4ikGAW/iEiKUfCLiKQYBb+ISIpR8IuIpBgFv4hIilHwi4ikmIT8ygYzawPeO86nVwDtcSwn3hK9PlCN8ZDo9UHi15jo9UFi1TjX3Stj6ZiQwT8VZrYy1u+rCEOi1weqMR4SvT5I/BoTvT44MWoci4Z6RERSjIJfRCTFJGPwLwu7gAkken2gGuMh0euDxK8x0euDE6PGoyTdGL+IiBxbMu7xi4jIMSRN8JvZ1Wa22cy2mtk9IdYxx8xeMrONZvaWmd0VtJeZ2XIz2xL8LA3azcy+EdS9zszOnaE6083sd2b2bPB4npmtCOr4oZllBe3ZweOtwfKGGaqvxMweN7O3zWyTmV2cSOvQzP4q+PfdYGaPmllO2OvQzB40s1Yz2xDVNul1Zma3Bf23mNltM1Djl4N/53Vm9pTCsb5rAAAEP0lEQVSZlUQtuzeocbOZfTSqfdre72PVGLXsbjNzM6sIHoeyHqfM3U/4G5AOvAvMB7KAtcBpIdVSA5wb3C8E3gFOA/4FuCdovwf4UnD/WuB5wICLgBUzVOf/AH4APBs8/hFwY3D/PuDPg/ufBu4L7t8I/HCG6nsYuCO4nwWUJMo6BGqB7UBu1Lr7RNjrELgMOBfYENU2qXUGlAHbgp+lwf3Saa7xKiAjuP+lqBpPC97L2cC84D2ePt3v97FqDNrnAC8QOceoIsz1OOW/MewC4vQPdTHwQtTje4F7w64rqOUnwO8Dm4GaoK0G2Bzc/y5wU1T/0X7TWFMd8Avgw8CzwX/a9qg33+j6DP6jXxzczwj62TTXVxwEqx3RnhDrkEjw7wze1BnBOvxoIqxDoOGIUJ3UOgNuAr4b1X5Yv+mo8YhlHwceCe4f9j4eWY8z8X4fq0bgceAsYAcfBH9o63Eqt2QZ6hl5I45oCtpCFXykPwdYAcxy95Zg0W5gVnA/jNq/Bvw1cCh4XA7sd/ehMWoYrS9Y3hn0n07zgDbg/wXDUfebWT4Jsg7dvRn4V+B9oIXIOllFYq3DEZNdZ2G/lz5FZA+aY9Qy4zWa2fVAs7uvPWJRwtQ4GckS/AnHzAqAJ4DPuntX9DKP7AKEMp3KzK4DWt19VRivH6MMIh+1v+Pu5wC9RIYpRoW8DkuB64lsoGYD+cDVYdQyGWGus1iY2d8AQ8AjYdcSzczygM8DXwi7lnhJluBvJjL+NqIuaAuFmWUSCf1H3P3JoHmPmdUEy2uA1qB9pmu/FPgDM9sBPEZkuOfrQImZZYxRw2h9wfJiYO801geRvaMmd18RPH6cyIYgUdbhR4Dt7t7m7geBJ4ms10RahyMmu85CeS+Z2SeA64Cbgw1UItV4EpGN/NrgfVMHrDaz6gSqcVKSJfjfBBYEsyqyiBxAeyaMQszMgAeATe7+1ahFzwAjR/ZvIzL2P9J+azA74CKgM+qjedy5+73uXufuDUTW0y/d/WbgJeCGceobqfuGoP+07jW6+25gp5ktCpp+D9hIgqxDIkM8F5lZXvDvPVJfwqzDKJNdZy8AV5lZafDJ5qqgbdqY2dVEhh7/wN0PHFH7jcGsqHnAAuANZvj97u7r3b3K3RuC900TkQkcu0mg9TgpYR9kiNeNyNH1d4gc7f+bEOtYQuTj9DpgTXC7lsiY7i+ALcDPgbKgvwHfCupeDzTOYK1X8MGsnvlE3lRbgR8D2UF7TvB4a7B8/gzVdjawMliPTxOZGZEw6xD4P8DbwAbg34jMPAl1HQKPEjnmcJBION1+POuMyDj71uD2yRmocSuR8fCR98t9Uf3/JqhxM3BNVPu0vd/HqvGI5Tv44OBuKOtxqjeduSsikmKSZahHRERipOAXEUkxCn4RkRSj4BcRSTEKfhGRFKPgFxFJMQp+EZEUo+AXEUkx/x/yRBHKsiigaAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "cost_function = red_iris.loss_curve_\n",
     "plt.figure()\n",
     "plt.plot(cost_function)\n",
-    "plt.show()"
+    "plt.show()\n",
+    "\n",
+    "\n"
    ]
   }
  ],
  "metadata": {
   "celltoolbar": "Slideshow",
   "kernelspec": {
-   "display_name": "Python 3",
+   "name": "pycharm-813d94b2",
    "language": "python",
-   "name": "python3"
+   "display_name": "PyCharm (Chapter-4)"
   },
   "language_info": {
    "codemirror_mode": {
@@ -3994,8 +1239,17 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.4"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "source": [],
+    "metadata": {
+     "collapsed": false
+    }
+   }
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file
diff --git a/Programs/Perceptron/.idea/Perceptron.iml b/Programs/Perceptron/.idea/Perceptron.iml
index e1c70e3..22d6a92 100644
--- a/Programs/Perceptron/.idea/Perceptron.iml
+++ b/Programs/Perceptron/.idea/Perceptron.iml
@@ -4,7 +4,7 @@
     <content url="file://$MODULE_DIR$">
       <excludeFolder url="file://$MODULE_DIR$/data" />
     </content>
-    <orderEntry type="inheritedJdk" />
+    <orderEntry type="jdk" jdkName="Python 3.7" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="PyDocumentationSettings">
diff --git a/Programs/Perceptron/.idea/misc.xml b/Programs/Perceptron/.idea/misc.xml
index 6f15a3e..8656114 100644
--- a/Programs/Perceptron/.idea/misc.xml
+++ b/Programs/Perceptron/.idea/misc.xml
@@ -3,5 +3,5 @@
   <component name="JavaScriptSettings">
     <option name="languageLevel" value="ES6" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6 (machineL)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7" project-jdk-type="Python SDK" />
 </project>
\ No newline at end of file
diff --git a/Programs/Perceptron/.idea/vcs.xml b/Programs/Perceptron/.idea/vcs.xml
new file mode 100644
index 0000000..b2bdec2
--- /dev/null
+++ b/Programs/Perceptron/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$/../.." vcs="Git" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/Programs/Perceptron/.idea/workspace.xml b/Programs/Perceptron/.idea/workspace.xml
index f5cf92c..989cab3 100644
--- a/Programs/Perceptron/.idea/workspace.xml
+++ b/Programs/Perceptron/.idea/workspace.xml
@@ -1,23 +1,37 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
   <component name="ChangeListManager">
-    <list default="true" id="df5189b2-c790-491e-b2eb-789c56c727c9" name="Default Changelist" comment="" />
+    <list default="true" id="df5189b2-c790-491e-b2eb-789c56c727c9" name="Default Changelist" comment="">
+      <change afterPath="$PROJECT_DIR$/../Perceptron_OOP/.idea/.gitignore" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/../Perceptron_OOP/.idea/Perceptron_OOP.iml" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/../Perceptron_OOP/.idea/inspectionProfiles/profiles_settings.xml" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/../Perceptron_OOP/.idea/misc.xml" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/../Perceptron_OOP/.idea/modules.xml" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/../Perceptron_OOP/.idea/vcs.xml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
+    </list>
     <option name="EXCLUDED_CONVERTED_TO_IGNORED" value="true" />
     <option name="SHOW_DIALOG" value="false" />
     <option name="HIGHLIGHT_CONFLICTS" value="true" />
     <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
     <option name="LAST_RESOLUTION" value="IGNORE" />
   </component>
+  <component name="Git.Settings">
+    <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$/../.." />
+  </component>
   <component name="ProjectId" id="1Py3oyA447jL6lZdNERklOq9Uoa" />
+  <component name="ProjectLevelVcsManager" settingsEditedManually="true" />
   <component name="PropertiesComponent">
+    <property name="SHARE_PROJECT_CONFIGURATION_FILES" value="true" />
     <property name="WebServerToolWindowFactoryState" value="false" />
+    <property name="last_opened_file_path" value="$PROJECT_DIR$/../Perceptron_OOP" />
     <property name="node.js.detected.package.eslint" value="true" />
     <property name="node.js.detected.package.tslint" value="true" />
     <property name="node.js.path.for.package.eslint" value="project" />
     <property name="node.js.path.for.package.tslint" value="project" />
     <property name="node.js.selected.package.eslint" value="(autodetect)" />
     <property name="node.js.selected.package.tslint" value="(autodetect)" />
-    <property name="settings.editor.selected.configurable" value="editor.preferences.fonts.default" />
+    <property name="settings.editor.selected.configurable" value="com.jetbrains.python.configuration.PyActiveSdkModuleConfigurable" />
   </component>
   <component name="RunDashboard">
     <option name="ruleStates">
@@ -36,6 +50,9 @@
       <module name="Perceptron" />
       <option name="INTERPRETER_OPTIONS" value="" />
       <option name="PARENT_ENVS" value="true" />
+      <envs>
+        <env name="PYTHONUNBUFFERED" value="1" />
+      </envs>
       <option name="SDK_HOME" value="" />
       <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
       <option name="IS_MODULE_SDK" value="false" />
@@ -85,10 +102,39 @@
       <option name="presentableId" value="Default" />
       <updated>1566832687732</updated>
       <workItem from="1566832689007" duration="7754000" />
+      <workItem from="1567044019409" duration="133000" />
+      <workItem from="1567044802091" duration="2518000" />
+      <workItem from="1567087796515" duration="1290000" />
+      <workItem from="1567116569759" duration="819000" />
     </task>
+    <task id="LOCAL-00001" summary="Implementacion de las funciones solicitadas">
+      <created>1567116750081</created>
+      <option name="number" value="00001" />
+      <option name="presentableId" value="LOCAL-00001" />
+      <option name="project" value="LOCAL" />
+      <updated>1567116750082</updated>
+    </task>
+    <option name="localTasksCounter" value="2" />
     <servers />
   </component>
   <component name="TypeScriptGeneratedFilesManager">
     <option name="version" value="1" />
   </component>
+  <component name="Vcs.Log.Tabs.Properties">
+    <option name="TAB_STATES">
+      <map>
+        <entry key="MAIN">
+          <value>
+            <State>
+              <option name="COLUMN_ORDER" />
+            </State>
+          </value>
+        </entry>
+      </map>
+    </option>
+  </component>
+  <component name="VcsManagerConfiguration">
+    <MESSAGE value="Implementacion de las funciones solicitadas" />
+    <option name="LAST_COMMIT_MESSAGE" value="Implementacion de las funciones solicitadas" />
+  </component>
 </project>
\ No newline at end of file
diff --git a/Programs/Perceptron/main.py b/Programs/Perceptron/main.py
index 9552fdf..4676be5 100644
--- a/Programs/Perceptron/main.py
+++ b/Programs/Perceptron/main.py
@@ -10,7 +10,15 @@
 def signo(net):
     fsigno = [1. if elem >=0 else -1. for elem in net]
     return fsigno
-
+def paso(net):
+    fpaso = [1. if elem >= 0 else 0. for elem in net]
+    return fpaso
+def sigmoide(net):
+    fsigmoide=1/(1+np.exp(elem) for elem in net)
+    return fsigmoide
+def tanhip(net):
+    ftanhip=((np.exp(elem)-np.exp(-elem))/(np.exp(elem)+np.exp(-elem)) for elem in net )
+    return ftanhip
 def signo_escalar(net_escalar):
     y_pred = 1 if net_escalar >=0 else -1
     return y_pred
@@ -31,6 +39,11 @@ def funcion_error(y_verdadero, y_predicho):
         if y_i != y_verdadero[indice]:
             cont = cont+1
     return cont
+def predecir(x,w_new):
+    #Sacar la salida del perceptron para un x dado como deber
+    net_final=net(x_input=x,w=w_new)
+    y_final=signo_escalar(net_final)
+    return y_final
 
 #%% programa
 # Definiendo el dataset de entrada
@@ -100,5 +113,7 @@ def funcion_error(y_verdadero, y_predicho):
     else:
         epoca += 1
         continue
-
-print('El modelo es: {}'.format(W_new))
\ No newline at end of file
+X_new=np.array([1,-1,-1],dtype=np.float)
+print('El modelo es: {}'.format(W_new))
+y_predicha=predecir(X_new,W_new)
+print('La y predicha es: {}'.format(y_predicha))
diff --git a/Programs/Perceptron_OOP/.idea/.gitignore b/Programs/Perceptron_OOP/.idea/.gitignore
new file mode 100644
index 0000000..0e40fe8
--- /dev/null
+++ b/Programs/Perceptron_OOP/.idea/.gitignore
@@ -0,0 +1,3 @@
+
+# Default ignored files
+/workspace.xml
\ No newline at end of file
diff --git a/Programs/Perceptron_OOP/.idea/Perceptron_OOP.iml b/Programs/Perceptron_OOP/.idea/Perceptron_OOP.iml
new file mode 100644
index 0000000..7c9d48f
--- /dev/null
+++ b/Programs/Perceptron_OOP/.idea/Perceptron_OOP.iml
@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="TestRunnerService">
+    <option name="projectConfiguration" value="pytest" />
+    <option name="PROJECT_TEST_RUNNER" value="pytest" />
+  </component>
+</module>
\ No newline at end of file
diff --git a/Programs/Perceptron_OOP/.idea/inspectionProfiles/profiles_settings.xml b/Programs/Perceptron_OOP/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000..105ce2d
--- /dev/null
+++ b/Programs/Perceptron_OOP/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file
diff --git a/Programs/Perceptron_OOP/.idea/misc.xml b/Programs/Perceptron_OOP/.idea/misc.xml
new file mode 100644
index 0000000..8656114
--- /dev/null
+++ b/Programs/Perceptron_OOP/.idea/misc.xml
@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="JavaScriptSettings">
+    <option name="languageLevel" value="ES6" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file
diff --git a/Programs/Perceptron_OOP/.idea/modules.xml b/Programs/Perceptron_OOP/.idea/modules.xml
new file mode 100644
index 0000000..904c4fe
--- /dev/null
+++ b/Programs/Perceptron_OOP/.idea/modules.xml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/Perceptron_OOP.iml" filepath="$PROJECT_DIR$/.idea/Perceptron_OOP.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file
diff --git a/Programs/Perceptron_OOP/.idea/vcs.xml b/Programs/Perceptron_OOP/.idea/vcs.xml
new file mode 100644
index 0000000..b2bdec2
--- /dev/null
+++ b/Programs/Perceptron_OOP/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$/../.." vcs="Git" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/Programs/Perceptron_OOP/Clase_perceptron.py b/Programs/Perceptron_OOP/Clase_perceptron.py
new file mode 100644
index 0000000..8293133
--- /dev/null
+++ b/Programs/Perceptron_OOP/Clase_perceptron.py
@@ -0,0 +1,60 @@
+class perceptron:
+
+    def insert_matriz(self):
+        # La mas sencilla e intuitiva
+        self.numero_filas=2
+        numero_columnas=2
+        matriz = []
+        for i in range(numero_filas):
+            matriz.append([])
+            for j in range(numero_columnas):
+                print("Ingrese elemento:")
+                matriz[i].append(input())
+        print(matriz)
+
+    def insert_arreglo(self):
+        arr = []
+        self.numero_filas = 2
+        for i in range(self.numero_filas):
+            print("Ingrese elemento:")
+            arr.append(input())
+        print(arr)
+    def learn1(self):
+        learn_rate = 0.1
+        return learn_rate
+    def learn2(self):
+        learn_rate = 0.01
+        return learn_rate
+    def learn3(self):
+        learn_rate = 0.001
+        return learn_rate
+    def numbers_learn(self,argument):
+        switcher = {
+            0: self.learn1,
+            1: self.learn2,
+            2: self.learn3,
+        }
+        return switcher.get(argument,lambda: "Invalid")
+
+    def signo(self,net):
+        fsigno = [1. if elem >=0 else -1. for elem in net]
+        return fsigno
+    def paso(self,net):
+        fpaso = [1. if elem >= 0 else 0. for elem in net]
+        return fpaso
+    def sigmoide(self,net):
+        fsigmoide=1/(1+np.exp(elem) for elem in net)
+        return fsigmoide
+    def tanhip(self,net):
+        ftanhip=((np.exp(elem)-np.exp(-elem))/(np.exp(elem)+np.exp(-elem)) for elem in net )
+        return ftanhip
+
+    def funcion(self,argumento):
+        switcher = {
+            1: self.signo(),
+            2: self.paso(),
+            3: self.sigmoide(),
+            4: self.tanhip(),
+        }
+    # Get the function from switcher dictionary
+        return switcher.get(argumento, lambda: "Invalid")
diff --git a/Programs/PythonBasics/AlgoritmosOrdenamiento/Algoritmo_Ordenamiento.py b/Programs/PythonBasics/AlgoritmosOrdenamiento/Algoritmo_Ordenamiento.py
new file mode 100644
index 0000000..a94db17
--- /dev/null
+++ b/Programs/PythonBasics/AlgoritmosOrdenamiento/Algoritmo_Ordenamiento.py
@@ -0,0 +1,60 @@
+import time
+import random
+
+def listaAleatorios():
+    print("Ingrese cuantos numeros aleatorios desea obtener:")
+    n = int(input())
+    lista= []
+    for i in range(n):
+        lista.append(random.randint(1, 10000))
+    return lista
+
+def bubbleSort(arr):
+    time_ini=time.time()
+    n = len(arr)
+    # Traverse through all array elements
+    for i in range(n):
+
+        # Last i elements are already in place
+        for j in range(0, n - i - 1):
+
+            # traverse the array from 0 to n-i-1
+            # Swap if the element found is greater
+            # than the next element
+            if arr[j] > arr[j + 1]:
+                arr[j], arr[j + 1] = arr[j + 1], arr[j]
+    time_fin=time.time()
+    time_dif=time_fin-time_ini
+    return arr,time_dif
+
+def insertionSort(arr):
+    # Traverse through 1 to len(arr)
+    time_ini = time.time()
+    for i in range(1, len(arr)):
+
+        key = arr[i]
+
+        # Move elements of arr[0..i-1], that are
+        # greater than key, to one position ahead
+        # of their current position
+        j = i - 1
+        while j >= 0 and key < arr[j]:
+            arr[j + 1] = arr[j]
+            j -= 1
+        arr[j + 1] = key
+    time_fin = time.time()
+    time_dif = time_fin - time_ini
+    # Driver code to test above
+    return arr,time_dif
+
+def main():
+    arreglo=listaAleatorios()
+    print("El arreglo a ordenar es: {} ".format(arreglo))
+    ordenamiento_bubble,time_bubble=bubbleSort(arreglo)
+    ordenamiento_insertion,time_insertion=insertionSort(arreglo)
+    print("El arreglo ordenado por bubblesort es: {}".format(ordenamiento_bubble))
+    print("El tiempo_bubble es : {}".format(time_bubble))
+    print("El arreglo ordenado por insertion sort es: {} ".format(ordenamiento_insertion))
+    print("El tiempo_insertion es : {}".format(time_insertion))
+if __name__=='__main__':
+    main()