Koha9
de066f3a65
Unity: No more detect Closest enemy info. Add different density sensor let agent get more state information on the center of view. Adjust Start Scene UI manager. Add in game visible rayCast & information that rayCast detect. Python: Start use mypy black and flake8 to format Python.
161 lines
3.1 KiB
Plaintext
161 lines
3.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import env"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"31\n",
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"5\n",
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"3\n"
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]
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}
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],
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"source": [
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"a=1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"包含None！\n",
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"[123 None]\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"def qa(aa = None):\n",
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" try:\n",
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" isaanone = aa.any() == None\n",
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" if aa.all() == None:\n",
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" print(\"包含None！\")\n",
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" except:\n",
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" isaanone =True\n",
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" if isaanone:\n",
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" print('none')\n",
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" else:\n",
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" print(aa)\n",
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"\n",
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"qa(np.array([123,None]))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[[1 1 1 1 1]\n",
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" [1 2 1 1 1]]\n",
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"\n",
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" [[2 1 3 4 5]\n",
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" [2 2 3 4 5]]\n",
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"\n",
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" [[3 1 3 4 5]\n",
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" [3 2 3 4 5]]]\n",
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"-\n",
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"tf.Tensor(\n",
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"[[1 1 1 1 1]\n",
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" [2 1 3 4 5]\n",
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" [3 1 3 4 5]], shape=(3, 5), dtype=int32)\n",
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"-\n",
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"tf.Tensor(\n",
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"[[1 2 1 1 1]\n",
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" [2 2 3 4 5]\n",
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" [3 2 3 4 5]], shape=(3, 5), dtype=int32)\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"import tensorflow as tf\n",
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"\n",
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"aa = np.array([[[1,1,1,1,1],[1,2,1,1,1],[1,3,1,1,1]],\n",
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" [[2,1,3,4,5],[2,2,3,4,5],[2,3,3,4,5]],\n",
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" [[3,1,3,4,5],[3,2,3,4,5],[3,3,3,4,5]]])\n",
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"tt = tf.constant(aa)\n",
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"bb = np.array([6,3,6,3,2,3])\n",
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"\n",
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"print(aa[:,0:2])\n",
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"aa[:,2:]\n",
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"\n",
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"for asd in tf.transpose(aa[:,0:2],perm=[1,0,2]):\n",
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" print('-')\n",
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" print(asd)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<tf.Tensor: shape=(), dtype=int32, numpy=1>"
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]
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},
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"execution_count": 35,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import tensorflow as tf\n",
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"aa = tf.constant(0)\n",
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"bb = aa+1\n",
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"bb\n"
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]
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "86e2db13b09bd6be22cb599ea60c1572b9ef36ebeaa27a4c8e961d6df315ac32"
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},
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"kernelspec": {
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"display_name": "Python 3.9.7 64-bit",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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