diff --git a/05_Regresja_wielomianowa.ipynb b/05_Regresja_wielomianowa.ipynb
index 48eef24..a055f58 100644
--- a/05_Regresja_wielomianowa.ipynb
+++ b/05_Regresja_wielomianowa.ipynb
@@ -40,6 +40,7 @@
"outputs": [],
"source": [
"# Przydatne funkcje\n",
+ "cost_functions = dict()\n",
"\n",
"def cost(theta, X, y):\n",
" \"\"\"Wersja macierzowa funkcji kosztu\"\"\"\n",
@@ -58,6 +59,7 @@
" while True:\n",
" theta = theta - alpha * fdJ(theta, X, y)\n",
" current_cost, prev_cost = fJ(theta, X, y), current_cost\n",
+ " # print(current_cost)\n",
" if abs(prev_cost - current_cost) > 10**15:\n",
" print('Algorithm does not converge!')\n",
" break\n",
@@ -80,6 +82,20 @@
" plt.xlim(np.min(X[:, 1]) - 1, np.max(X[:, 1]) + 1)\n",
" return fig\n",
"\n",
+ "def plot_data_cost(X, y, xlabel, ylabel):\n",
+ " \"\"\"Wykres danych (wersja macierzowa)\"\"\"\n",
+ " fig = plt.figure(figsize=(16 * .6, 9 * .6))\n",
+ " ax = fig.add_subplot(111)\n",
+ " fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9)\n",
+ " ax.scatter([X], [y], c='r', s=50, label='Dane')\n",
+ "\n",
+ " ax.set_xlabel(xlabel)\n",
+ " ax.set_ylabel(ylabel)\n",
+ " ax.margins(.05, .05)\n",
+ " plt.ylim(min(y) - 1, max(y) + 1)\n",
+ " plt.xlim(np.min(X) - 1, np.max(X) + 1)\n",
+ " return fig\n",
+ "\n",
"def plot_fun(fig, fun, X):\n",
" \"\"\"Wykres funkcji `fun`\"\"\"\n",
" ax = fig.axes[0]\n",
@@ -141,7 +157,7 @@
" \"\"\"Funkcja regresji wielomianowej\"\"\"\n",
" theta_start = np.matrix([0] * (n+1)).reshape(n+1, 1)\n",
" theta, logs = gradient_descent(cost, gradient, theta_start, X, y)\n",
- " return lambda x: h_poly(theta, x)"
+ " return lambda x: h_poly(theta, x), logs"
]
},
{
@@ -159,10 +175,12 @@
"\n",
"def plot_and_mse(data, data_test, n):\n",
" x, y = get_poly_data(np.array(data), n)\n",
- " model = polynomial_regression(x, y, n)\n",
+ " model, logs = polynomial_regression(x, y, n)\n",
+ " cost_function = [[element[0], i] for i, element in enumerate(logs)]\n",
+ " cost_functions[n] = cost_function\n",
" \n",
" fig = plot_data(x, y, xlabel='x', ylabel='y')\n",
- " plot_fun(fig, polynomial_regression(x, y, n), x)\n",
+ " plot_fun(fig, model, x)\n",
"\n",
" y_true, Y_pred, mse = predict_values(model, data_test, n)\n",
" print(f'Wielomian {n} stopnia, MSE = {mse}')"
@@ -204,29 +222,29 @@
" \n",
"
\n",
" \n",
- " 470 | \n",
- " 40 | \n",
- " 1140000.0 | \n",
+ " 160 | \n",
+ " 44 | \n",
+ " 349668.0 | \n",
"
\n",
" \n",
- " 1171 | \n",
- " 90 | \n",
- " 855000.0 | \n",
+ " 1066 | \n",
+ " 54 | \n",
+ " 260000.0 | \n",
"
\n",
" \n",
- " 1128 | \n",
- " 37 | \n",
- " 288405.0 | \n",
+ " 679 | \n",
+ " 65 | \n",
+ " 348000.0 | \n",
"
\n",
" \n",
- " 254 | \n",
- " 49 | \n",
- " 290000.0 | \n",
+ " 1589 | \n",
+ " 97 | \n",
+ " 579000.0 | \n",
"
\n",
" \n",
- " 508 | \n",
- " 91 | \n",
- " 375606.0 | \n",
+ " 132 | \n",
+ " 60 | \n",
+ " 295120.0 | \n",
"
\n",
" \n",
" ... | \n",
@@ -234,29 +252,29 @@
" ... | \n",
"
\n",
" \n",
- " 389 | \n",
- " 56 | \n",
- " 325000.0 | \n",
+ " 894 | \n",
+ " 68 | \n",
+ " 390000.0 | \n",
"
\n",
" \n",
- " 1403 | \n",
- " 69 | \n",
+ " 937 | \n",
+ " 78 | \n",
+ " 329000.0 | \n",
+ "
\n",
+ " \n",
+ " 368 | \n",
+ " 16 | \n",
" 399000.0 | \n",
"
\n",
" \n",
- " 957 | \n",
- " 94 | \n",
- " 595000.0 | \n",
+ " 1278 | \n",
+ " 51 | \n",
+ " 460499.0 | \n",
"
\n",
" \n",
- " 356 | \n",
+ " 1670 | \n",
" 53 | \n",
- " 339200.0 | \n",
- "
\n",
- " \n",
- " 160 | \n",
- " 44 | \n",
- " 349668.0 | \n",
+ " 339000.0 | \n",
"
\n",
" \n",
"\n",
@@ -264,18 +282,18 @@
""
],
"text/plain": [
- " sqrMetres price\n",
- "470 40 1140000.0\n",
- "1171 90 855000.0\n",
- "1128 37 288405.0\n",
- "254 49 290000.0\n",
- "508 91 375606.0\n",
- "... ... ...\n",
- "389 56 325000.0\n",
- "1403 69 399000.0\n",
- "957 94 595000.0\n",
- "356 53 339200.0\n",
- "160 44 349668.0\n",
+ " sqrMetres price\n",
+ "160 44 349668.0\n",
+ "1066 54 260000.0\n",
+ "679 65 348000.0\n",
+ "1589 97 579000.0\n",
+ "132 60 295120.0\n",
+ "... ... ...\n",
+ "894 68 390000.0\n",
+ "937 78 329000.0\n",
+ "368 16 399000.0\n",
+ "1278 51 460499.0\n",
+ "1670 53 339000.0\n",
"\n",
"[1674 rows x 2 columns]"
]
@@ -317,14 +335,14 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Wielomian 1 stopnia, MSE = 34232245972.377033\n",
- "Wielomian 2 stopnia, MSE = 84918416526.0791\n",
- "Wielomian 3 stopnia, MSE = 83924720748.3686\n"
+ "Wielomian 1 stopnia, MSE = 24385438317.4621\n",
+ "Wielomian 2 stopnia, MSE = 45053827695.02038\n",
+ "Wielomian 3 stopnia, MSE = 47184374447.91149\n"
]
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
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