diff --git a/kmedoids.ipynb b/kmedoids.ipynb index d93041c..33794fa 100644 --- a/kmedoids.ipynb +++ b/kmedoids.ipynb @@ -171,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 118, "id": "409d4187", "metadata": {}, "outputs": [], @@ -246,7 +246,7 @@ " final_clusters = self.get_clustered_data(self.data, self.centroids)\n", " cluster_labels = self.get_clusters_label(self.data, final_clusters)\n", " silhouette_avg = silhouette_score(self.data, cluster_labels)\n", - " print(\"Sylwetka (ang.silhouette) dla metody k-medoid i dla k =\", self.k_value, round(silhouette_avg,2))\n", + " print(\"Sylwetka (ang.silhouette) dla metody k-średnich i dla k =\", self.k_value, round(silhouette_avg,2))\n", " \n", " return final_clusters, round(silhouette_avg,2)\n", " \n", @@ -256,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 130, "id": "b42a9194", "metadata": {}, "outputs": [ @@ -264,7 +264,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 3 0.44\n" + "Sylwetka (ang.silhouette) dla metody k-średnich i dla k = 3 0.44\n" ] } ], @@ -279,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 131, "id": "15541704", "metadata": {}, "outputs": [ @@ -287,7 +287,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 4 0.34\n" + "Sylwetka (ang.silhouette) dla metody k-średnich i dla k = 4 0.33\n" ] } ], @@ -302,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 132, "id": "c29dca2b", "metadata": {}, "outputs": [ @@ -310,7 +310,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 3 0.3\n" + "Sylwetka (ang.silhouette) dla metody k-średnich i dla k = 3 0.3\n" ] } ], @@ -3681,7 +3681,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 133, "id": "c5909200", "metadata": {}, "outputs": [], @@ -3695,13 +3695,13 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 134, "id": "a8983527", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -3736,7 +3736,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 135, "id": "74fc7277", "metadata": {}, "outputs": [ @@ -3770,7 +3770,7 @@ " \n", " k-średnich\n", " 0.44\n", - " 0.34\n", + " 0.33\n", " 0.30\n", " \n", " \n", @@ -3796,7 +3796,7 @@ "k-medoid + PCA 0.50 \n", "\n", " Glass \\\n", - "k-średnich 0.34 \n", + "k-średnich 0.33 \n", "k-medoid 0.35 \n", "k-medoid + PCA 0.50 \n", "\n", @@ -3806,7 +3806,7 @@ "k-medoid + PCA 0.45 " ] }, - "execution_count": 105, + "execution_count": 135, "metadata": {}, "output_type": "execute_result" } @@ -3825,7 +3825,7 @@ }, { "cell_type": "markdown", - "id": "05d43597", + "id": "f5a71343", "metadata": {}, "source": [ "### K-medoids na innych zbiorach danych" @@ -3834,7 +3834,7 @@ { "cell_type": "code", "execution_count": 106, - "id": "ce6fa598", + "id": "963d94ab", "metadata": {}, "outputs": [], "source": [ @@ -3853,7 +3853,7 @@ { "cell_type": "code", "execution_count": 107, - "id": "9af6e339", + "id": "03fdd9f0", "metadata": {}, "outputs": [], "source": [ @@ -3871,7 +3871,7 @@ { "cell_type": "code", "execution_count": 108, - "id": "c0d9a1c3", + "id": "c5740cd9", "metadata": {}, "outputs": [ { @@ -3911,7 +3911,7 @@ { "cell_type": "code", "execution_count": 109, - "id": "a756e584", + "id": "7e39c874", "metadata": {}, "outputs": [ { @@ -3950,7 +3950,7 @@ { "cell_type": "code", "execution_count": 110, - "id": "ca919f2d", + "id": "c76108ff", "metadata": {}, "outputs": [ { @@ -3988,7 +3988,7 @@ }, { "cell_type": "markdown", - "id": "e42aee29", + "id": "b5e15802", "metadata": {}, "source": [ "### Przykłady z syntetycznymi zbiorami danych" @@ -3997,7 +3997,7 @@ { "cell_type": "code", "execution_count": 111, - "id": "47339051", + "id": "23475798", "metadata": {}, "outputs": [ { @@ -4020,7 +4020,7 @@ { "cell_type": "code", "execution_count": 112, - "id": "437665cb", + "id": "df756b46", "metadata": {}, "outputs": [ { @@ -4048,7 +4048,7 @@ { "cell_type": "code", "execution_count": 113, - "id": "6d4c1c3e", + "id": "98d981cc", "metadata": {}, "outputs": [ { @@ -4076,7 +4076,7 @@ { "cell_type": "code", "execution_count": 114, - "id": "0a90a853", + "id": "bce6689e", "metadata": {}, "outputs": [ { @@ -4104,7 +4104,7 @@ { "cell_type": "code", "execution_count": 115, - "id": "85bc48d6", + "id": "5b6502bf", "metadata": {}, "outputs": [ { @@ -4143,7 +4143,7 @@ { "cell_type": "code", "execution_count": 116, - "id": "7fb14dda", + "id": "f902c182", "metadata": {}, "outputs": [ { @@ -4171,7 +4171,7 @@ { "cell_type": "code", "execution_count": 117, - "id": "11960f33", + "id": "c4675813", "metadata": {}, "outputs": [ {