|
147 | 147 | "classifier.predict(p)" |
148 | 148 | ] |
149 | 149 | }, |
| 150 | + { |
| 151 | + "cell_type": "markdown", |
| 152 | + "metadata": {}, |
| 153 | + "source": [ |
| 154 | + "* Modelo: w0 . x + w1 . y + e = 0\n", |
| 155 | + "* Ecuación del hiperplano en 2D: y = a . x + b " |
| 156 | + ] |
| 157 | + }, |
150 | 158 | { |
151 | 159 | "cell_type": "code", |
152 | | - "execution_count": 42, |
| 160 | + "execution_count": 43, |
153 | 161 | "metadata": {}, |
154 | 162 | "outputs": [ |
155 | 163 | { |
156 | 164 | "data": { |
157 | 165 | "text/plain": [ |
158 | | - "array([ 0. , 0.24489796, 0.48979592, 0.73469388, 0.97959184,\n", |
159 | | - " 1.2244898 , 1.46938776, 1.71428571, 1.95918367, 2.20408163,\n", |
160 | | - " 2.44897959, 2.69387755, 2.93877551, 3.18367347, 3.42857143,\n", |
161 | | - " 3.67346939, 3.91836735, 4.16326531, 4.40816327, 4.65306122,\n", |
162 | | - " 4.89795918, 5.14285714, 5.3877551 , 5.63265306, 5.87755102,\n", |
163 | | - " 6.12244898, 6.36734694, 6.6122449 , 6.85714286, 7.10204082,\n", |
164 | | - " 7.34693878, 7.59183673, 7.83673469, 8.08163265, 8.32653061,\n", |
165 | | - " 8.57142857, 8.81632653, 9.06122449, 9.30612245, 9.55102041,\n", |
166 | | - " 9.79591837, 10.04081633, 10.28571429, 10.53061224, 10.7755102 ,\n", |
167 | | - " 11.02040816, 11.26530612, 11.51020408, 11.75510204, 12. ])" |
| 166 | + "array([0.1380943 , 0.24462418])" |
168 | 167 | ] |
169 | 168 | }, |
170 | | - "execution_count": 42, |
| 169 | + "execution_count": 43, |
171 | 170 | "metadata": {}, |
172 | 171 | "output_type": "execute_result" |
173 | 172 | } |
174 | 173 | ], |
175 | 174 | "source": [ |
176 | | - "np.linspace(0,12)" |
| 175 | + "w = classifier.coef_[0]\n", |
| 176 | + "w" |
| 177 | + ] |
| 178 | + }, |
| 179 | + { |
| 180 | + "cell_type": "code", |
| 181 | + "execution_count": 44, |
| 182 | + "metadata": {}, |
| 183 | + "outputs": [ |
| 184 | + { |
| 185 | + "data": { |
| 186 | + "text/plain": [ |
| 187 | + "-0.5645161290322581" |
| 188 | + ] |
| 189 | + }, |
| 190 | + "execution_count": 44, |
| 191 | + "metadata": {}, |
| 192 | + "output_type": "execute_result" |
| 193 | + } |
| 194 | + ], |
| 195 | + "source": [ |
| 196 | + "a = -w[0]/w[1]\n", |
| 197 | + "a" |
| 198 | + ] |
| 199 | + }, |
| 200 | + { |
| 201 | + "cell_type": "code", |
| 202 | + "execution_count": 45, |
| 203 | + "metadata": {}, |
| 204 | + "outputs": [ |
| 205 | + { |
| 206 | + "data": { |
| 207 | + "text/plain": [ |
| 208 | + "6.734677437813051" |
| 209 | + ] |
| 210 | + }, |
| 211 | + "execution_count": 45, |
| 212 | + "metadata": {}, |
| 213 | + "output_type": "execute_result" |
| 214 | + } |
| 215 | + ], |
| 216 | + "source": [ |
| 217 | + "b = - classifier.intercept_[0]/w[1]\n", |
| 218 | + "b" |
| 219 | + ] |
| 220 | + }, |
| 221 | + { |
| 222 | + "cell_type": "code", |
| 223 | + "execution_count": 46, |
| 224 | + "metadata": {}, |
| 225 | + "outputs": [], |
| 226 | + "source": [ |
| 227 | + "xx = np.linspace(0,10)\n", |
| 228 | + "yy = a * xx + b" |
| 229 | + ] |
| 230 | + }, |
| 231 | + { |
| 232 | + "cell_type": "code", |
| 233 | + "execution_count": 47, |
| 234 | + "metadata": {}, |
| 235 | + "outputs": [ |
| 236 | + { |
| 237 | + "data": { |
| 238 | + "text/plain": [ |
| 239 | + "[]" |
| 240 | + ] |
| 241 | + }, |
| 242 | + "execution_count": 47, |
| 243 | + "metadata": {}, |
| 244 | + "output_type": "execute_result" |
| 245 | + }, |
| 246 | + { |
| 247 | + "data": { |
| 248 | + "image/png": 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\n", |
| 249 | + "text/plain": [ |
| 250 | + "<matplotlib.figure.Figure at 0x1a1e1e9160>" |
| 251 | + ] |
| 252 | + }, |
| 253 | + "metadata": {}, |
| 254 | + "output_type": "display_data" |
| 255 | + } |
| 256 | + ], |
| 257 | + "source": [ |
| 258 | + "plt.plot(xx, yy, 'k-', label = \"Hiperplano de separación\")\n", |
| 259 | + "plt.scatter(X, Y, c = target)\n", |
| 260 | + "plt.legend()\n", |
| 261 | + "plt.plot()" |
177 | 262 | ] |
178 | 263 | }, |
179 | 264 | { |
|
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