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"<div align=\"right\" style=\"text-align: right\"><i>Peter Norvig<br>July 2021</i></div>\n",
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"<div align=\"right\" style=\"text-align: right\"><i>Peter Norvig<br>July 2021</i></div>\n",
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"\n",
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"\n",
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"# Climbing Wall\n",
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"# Olympic Climbing Wall\n",
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"\n",
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"\n",
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"From the 538 Riddler on [23 July 2021](https://fivethirtyeight.com/features/can-you-hop-across-the-chessboard/) (slightly rephrased):\n",
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"From the 538 Riddler on [23 July 2021](https://fivethirtyeight.com/features/can-you-hop-across-the-chessboard/) (rephrased):\n",
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"\n",
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"\n",
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">Today marks the beginning of the Summer Olympics! One of the brand-new events this year is [sport climbing](https://olympics.com/tokyo-2020/en/sports/sport-climbing/).\n",
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">Today marks the beginning of the Summer Olympics! One of the brand-new events this year is [sport climbing](https://olympics.com/tokyo-2020/en/sports/sport-climbing/).\n",
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">\n",
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">\n",
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">Suppose the organizers place climbing holds randomly on the 10-by-10 meter square climbing wall until there are no vertical gaps more than 1 meter between climbing holds (including the bottom and top of the wall). The climbers can handle horizontal gaps of any size. On average, how many holds have to be placed (not including the bottom and top of the wall)?\n",
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">Suppose the organizers place climbing holds randomly on a 10 meter tall climbing wall until there is a **path**: a series of moves from the bottom to a hold, and then to successive holds, and finally to the top, where each move is no more than 1 meter distance. For the first event, the vertical positions are chosen uniformly at random, and all the holds are aligned at the same horizontal position. On average, how many holds (not including the bottom and top of the wall) have to be placed to make a path?\n",
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"\n",
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"\n",
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"A hold can be represented by a single number, the vertical height off the ground (the horizontal position of the hold doesn't matter). I'll define `place_holds` as a Monte Carlo simulation that selects random hold heights (uniformly from 0 to 10 meters) until no gap is more than 1 meter and returns the list of holds:"
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"A hold can be represented by a single number, the vertical height off the ground (the horizontal positions are all the same and need not be represented). I'll define `place_holds` to randomly place holds until a path is formed (as detected by `is_path`). Internally to the function, the bottom and top of the wall are considered to be holds, but these are excluded from the output of the function."
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{
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{
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@@ -28,23 +28,24 @@
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"from statistics import mean\n",
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"from statistics import mean\n",
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"\n",
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"\n",
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"def place_holds(top=10) -> List[float]:\n",
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"def place_holds(top=10) -> List[float]:\n",
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" \"\"\"Randomly place holds on wall until no gap is more than 1.\"\"\"\n",
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" \"\"\"Randomly place holds on wall until there is a path from bottom to top.\"\"\"\n",
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" holds = [0, top]\n",
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" holds = [0, top]\n",
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" while max(gaps(holds)) > 1:\n",
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" while not is_path(holds):\n",
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" holds.append(random.uniform(0, top))\n",
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" holds.append(random.uniform(0, top))\n",
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" holds.sort()\n",
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" holds.sort()\n",
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" return holds[1:-1] # (not including the bottom and top of the wall)\n",
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" return holds[1:-1] # (not including the bottom and top of the wall)\n",
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" \n",
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" \n",
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"def gaps(numbers) -> Iterable[float]:\n",
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"def is_path(holds) -> bool:\n",
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" \"\"\"The differences between adjacent pairs of sorted numbers.\"\"\"\n",
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" \"\"\"Do the sorted holds form a path where each move has distance <= 1?\"\"\"\n",
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" return (numbers[i + 1] - numbers[i] for i in range(len(numbers) - 1))"
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" return all(holds[i + 1] - holds[i] <= 1\n",
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" for i in range(len(holds) - 1))"
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]
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]
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},
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},
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{
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{
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"cell_type": "markdown",
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {},
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"source": [
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"source": [
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"For example:"
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"For example, here are random holds that form a path on a 3 meter tall wall:"
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]
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]
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},
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{
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{
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@@ -55,11 +56,7 @@
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{
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{
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"data": {
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"data": {
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"text/plain": [
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"text/plain": [
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"[0.8490771290915906,\n",
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"[0.845553458582284, 1.384891681206423, 2.2900550596027087, 2.484136525301279]"
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" 1.043223518543252,\n",
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" 1.7988034611180812,\n",
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" 2.5516167786335906,\n",
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" 2.590651506408302]"
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]
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]
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},
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},
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"execution_count": 2,
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"execution_count": 2,
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@@ -71,59 +68,36 @@
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"place_holds(3)"
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"place_holds(3)"
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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": 3,
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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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"[0.1941463894516613,\n",
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" 0.7555799425748293,\n",
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" 0.7528133175155094,\n",
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" 0.03903472777471162]"
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]
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},
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"execution_count": 3,
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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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"list(gaps(_))"
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]
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},
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{
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{
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"cell_type": "markdown",
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {},
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"source": [
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"source": [
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"The Monte Carlo algorithm computes the expected number of holds created by `place_holds`:"
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"I can use a [Monte Carlo algorithm](https://en.wikipedia.org/wiki/Monte_Carlo_algorithm) to estimate the expected number of holds by averaging the `len` of repetitions of `place_holds`:"
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"def monte_carlo(fn, *args, repeat=50_000, key=len) -> float:\n",
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" \"\"\"Mean value of `repeat` repetitions of key(fn(*args)).\"\"\"\n",
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" return mean(key(fn(*args)) for _ in range(repeat))"
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]
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]
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 4,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"source": [
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"def monte_carlo(fn, *args, repeat=50_000, key=len) -> float:\n",
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" \"\"\"Mean value of key(fn(*args)), repeated `repeat` times.\"\"\"\n",
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" return mean(key(fn(*args)) for _ in range(repeat))"
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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": 5,
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"metadata": {},
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"outputs": [
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"outputs": [
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{
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{
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"data": {
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"data": {
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"text/plain": [
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"text/plain": [
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"43.05824"
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"43.03982"
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]
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]
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},
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},
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"execution_count": 5,
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -138,16 +112,16 @@
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"source": [
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"source": [
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"**Answer: The expected number of holds is about 43** (which I found surprisingly large).\n",
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"**Answer: The expected number of holds is about 43** (which I found surprisingly large).\n",
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"\n",
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"\n",
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"# Extra Credit\n",
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"# Second Event\n",
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"\n",
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"\n",
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"> Now suppose horizontal gaps *do* matter. The organizers randomly place climbing holds until there is a path from the bottom to a hold, and then to successive holds, and finally to the top, where each step in the path is no more than 1 meter distance. On average, how many holds have to be placed?\n",
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"> For the second event, holds are placed uniformly at random anywhere on a 10-by-10 meter wall. On average, how many holds have to be placed to make a path?\n",
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"\n",
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"\n",
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"This time a hold is represented by a point in 2-D space: an `(x, y)` tuple of two numbers:"
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"For this event a hold is represented by a point in 2-D space: an `(x, y)` tuple of two numbers:"
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]
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]
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 5,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@@ -156,6 +130,7 @@
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"def Y_(point): return point[1]\n",
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"def Y_(point): return point[1]\n",
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"\n",
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"\n",
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"def distance(A: Point, B: Point) -> float: \n",
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"def distance(A: Point, B: Point) -> float: \n",
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" \"\"\"Distance between two 2-D points.\"\"\"\n",
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" return abs(complex(*A) - complex(*B))"
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" return abs(complex(*A) - complex(*B))"
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]
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]
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},
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@@ -163,17 +138,20 @@
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"cell_type": "markdown",
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {},
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"source": [
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"source": [
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"To make it easier to determine if there is a path from bottom to top, I'll keep track, for every hold, of the highest hold that can be reached from that hold (in any number of steps). The data structure `Wall` will be a mapping of `{hold: highest_reachable_hold}`. When a new `hold` is added to the wall, we update the mapping as follows: \n",
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"To make it easier to determine when there is a path from bottom to top, I'll keep track, for every hold, of the highest hold that can be reached from that hold (in any number of moves). The data structure `Wall` will be a mapping of `{hold: highest_reachable_hold}`. A `Wall` also has an attribute, `wall.paths`, that is a dict whose entries are `{hold_near_bottom: hold_near_top}` pairs denoting paths from bottom to top.\n",
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"- Find all holds that are within 1 meter of the new `hold`.\n",
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"\n",
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"- For each of those holds, look up the highest hold they can reach. Call that set of holds `reachable_holds`.\n",
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"When a new `hold` is added to the wall, update the wall as follows: \n",
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"- Any hold on the wall that can reach one of these `reachable_holds` can reach all of them, and thus the highest of them.\n",
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"- Find all holds that are within 1 meter of the new `hold` (including the `hold` itself).\n",
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"- So update each such hold to reflect the fact that it can reach the `highest_hold`.\n",
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"- For each of those holds, look up the highest hold they can reach. That set of holds is called `reachable_holds`.\n",
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"- Also, if the `highest_hold` is within a meter of the top, and a hold `h` that can reach it is within a meter of the bottom, update the `paths` attribute of the wall to include the path `{h: highest_hold}`."
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"- The highest of the reachable holds is called `highest_hold`.\n",
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"- Any hold that can reach one of `reachable_holds` can reach all of them (via `hold`), and thus can reach `highest_hold`.\n",
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"- So update each such hold to say that it can reach `highest_hold`.\n",
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"- Also, if `highest_hold` is within a meter of the top, and a hold `h` that can reach it is within a meter of the bottom, update the `paths` attribute to include the path `{h: highest_hold}`."
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]
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]
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 6,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@@ -188,7 +166,7 @@
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" \n",
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" \n",
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" def add(self, hold: Point):\n",
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" def add(self, hold: Point):\n",
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" \"\"\"Add hold to this Wall, and merge groups of holds.\"\"\"\n",
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" \"\"\"Add hold to this Wall, and merge groups of holds.\"\"\"\n",
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" self[hold] = hold # A hold is always reachable from itself\n",
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" self[hold] = hold # A hold can at least reach itself\n",
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" self.merge({self[h] for h in self if distance(hold, h) <= 1})\n",
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" self.merge({self[h] for h in self if distance(hold, h) <= 1})\n",
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"\n",
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"\n",
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" def merge(self, reachable_holds):\n",
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" def merge(self, reachable_holds):\n",
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@@ -206,19 +184,19 @@
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"cell_type": "markdown",
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {},
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"source": [
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"source": [
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"*Note: This could be made more efficient with a [kd-tree](https://en.wikipedia.org/wiki/K-d_tree) to quickly find holds that are near each other, and a [union-find forest](https://en.wikipedia.org/wiki/Disjoint-set_data_structure) to merge groups of holds. But since the expected number of points is small, I opted for simplicity, not efficiency.*\n",
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"*Note: This could be made more efficient with an [integer lattice](https://en.wikipedia.org/wiki/Fixed-radius_near_neighbors) to quickly find holds within 1 meter, and a [union-find forest](https://en.wikipedia.org/wiki/Disjoint-set_data_structure) to quickly merge groups of holds. But since the expected number of points is small, I opted for simplicity, not efficiency.*\n",
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"\n",
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"\n",
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"I'll again do a Monte Carlo simulation, defining `place_holds_2d` to build a climbable wall:"
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"Now `place_holds_2d` is analagous to `place_holds`, but places holds in two dimensions:"
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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": 8,
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"execution_count": 7,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"def place_holds_2d(top=10) -> Wall:\n",
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"def place_holds_2d(top=10) -> Wall:\n",
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" \"\"\"Randomly place holds on wall until there is a path from bottom to top.\"\"\"\n",
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" \"\"\"Randomly place holds on a square wall until there is a path from bottom to top.\"\"\"\n",
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" wall = Wall(top)\n",
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" wall = Wall(top)\n",
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" while not wall.paths:\n",
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" while not wall.paths:\n",
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" wall.add((random.uniform(0, top), random.uniform(0, top)))\n",
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" wall.add((random.uniform(0, top), random.uniform(0, top)))\n",
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},
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{
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"execution_count": 9,
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{
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{
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"data": {
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"data": {
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"text/plain": [
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"text/plain": [
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"143.0992"
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"143.2666"
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]
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"execution_count": 9,
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"output_type": "execute_result"
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},
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"cell_type": "code",
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"def plot_wall(wall):\n",
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"def plot_wall(wall):\n",
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" \"\"\"Plot the holds on the wall, and the paths from bottom to top.\"\"\"\n",
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" \"\"\"Plot the holds on the wall, and the paths from bottom to top.\"\"\"\n",
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" plt.gca().set_aspect('equal', adjustable='box')\n",
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" plt.gca().set_aspect('equal', adjustable='box')\n",
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" plt.xlim(0, wall.top); plt.ylim(0, wall.top)\n",
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" ends = set(wall.paths.values())\n",
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" ends = set(wall.paths.values())\n",
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" for h in wall:\n",
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" for h in wall:\n",
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" if wall[h] in ends:\n",
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" if wall[h] in ends:\n",
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" if Y_(h) <= 1: # Plot vertical line from bottom\n",
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" if Y_(h) <= 1: # Plot vertical move from bottom\n",
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" plot_points([h, (X_(h), 0)], 'y-')\n",
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" plot_points([h, (X_(h), 0)], 'y-')\n",
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" if wall.top - Y_(h) <= 1: # Plot vertical line to top\n",
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" if wall.top - Y_(h) <= 1: # Plot vertical move to top\n",
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" plot_points([h, (X_(h), wall.top)], 'y-')\n",
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" plot_points([h, (X_(h), wall.top)], 'y-')\n",
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" for h2 in wall:\n",
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" for h2 in wall:\n",
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" if distance(h, h2) <= 1:\n",
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" if distance(h, h2) <= 1:\n",
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" plot_points([h, h2], 'y-') # Plot path\n",
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" plot_points([h, h2], 'y-') # Plot move between holds\n",
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" plot_points(wall, 'g.') # Plot all holds\n",
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" plot_points(wall, 'g.') # Plot all holds\n",
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" plt.title(f'holds: {len(wall)} starts: {len(wall.paths)}')\n",
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" plt.title(f'holds: {len(wall)} starts: {len(wall.paths)}')\n",
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"\n",
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"\n",
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@@ -294,12 +273,12 @@
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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": 11,
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"execution_count": 10,
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"metadata": {},
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"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": 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truncated
|
"image/png": "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 truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 432x288 with 1 Axes>"
|
"<Figure size 432x288 with 1 Axes>"
|
||||||
]
|
]
|
||||||
@@ -311,7 +290,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": 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truncated
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truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 432x288 with 1 Axes>"
|
"<Figure size 432x288 with 1 Axes>"
|
||||||
]
|
]
|
||||||
@@ -323,7 +302,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": 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truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 432x288 with 1 Axes>"
|
"<Figure size 432x288 with 1 Axes>"
|
||||||
]
|
]
|
||||||
@@ -335,7 +314,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": 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truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 432x288 with 1 Axes>"
|
"<Figure size 432x288 with 1 Axes>"
|
||||||
]
|
]
|
||||||
@@ -347,7 +326,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": 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truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 432x288 with 1 Axes>"
|
"<Figure size 432x288 with 1 Axes>"
|
||||||
]
|
]
|
||||||
@@ -359,7 +338,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": 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truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 432x288 with 1 Axes>"
|
"<Figure size 432x288 with 1 Axes>"
|
||||||
]
|
]
|
||||||
@@ -371,7 +350,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": 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truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 432x288 with 1 Axes>"
|
"<Figure size 432x288 with 1 Axes>"
|
||||||
]
|
]
|
||||||
@@ -383,7 +362,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": 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truncated
|
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truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 432x288 with 1 Axes>"
|
"<Figure size 432x288 with 1 Axes>"
|
||||||
]
|
]
|
||||||
@@ -395,7 +374,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": 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truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 432x288 with 1 Axes>"
|
"<Figure size 432x288 with 1 Axes>"
|
||||||
]
|
]
|
||||||
@@ -407,7 +386,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": "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 truncated
|
"image/png": "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 truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 432x288 with 1 Axes>"
|
"<Figure size 432x288 with 1 Axes>"
|
||||||
]
|
]
|
||||||
@@ -424,6 +403,86 @@
|
|||||||
" plt.show()"
|
" plt.show()"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"To get a feel for the internals of a `Wall`, let's look at a smaller one:"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 19,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"image/png": "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 truncated
|
||||||
|
"text/plain": [
|
||||||
|
"<Figure size 432x288 with 1 Axes>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {
|
||||||
|
"needs_background": "light"
|
||||||
|
},
|
||||||
|
"output_type": "display_data"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"wall = place_holds_2d(2)\n",
|
||||||
|
"plot_wall(wall)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 20,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"{(0.9934826941579458, 1.0572834992450997): (1.3314849992519568,\n",
|
||||||
|
" 1.3023468539111032),\n",
|
||||||
|
" (0.3583556329014106, 1.2190468817825952): (1.3314849992519568,\n",
|
||||||
|
" 1.3023468539111032),\n",
|
||||||
|
" (1.2499181582424967, 1.1234512122002231): (1.3314849992519568,\n",
|
||||||
|
" 1.3023468539111032),\n",
|
||||||
|
" (1.3314849992519568, 1.3023468539111032): (1.3314849992519568,\n",
|
||||||
|
" 1.3023468539111032),\n",
|
||||||
|
" (0.45747066866761976, 0.32822934854292884): (1.3314849992519568,\n",
|
||||||
|
" 1.3023468539111032)}"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 20,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"wall"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 21,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"{(0.45747066866761976, 0.32822934854292884): (1.3314849992519568,\n",
|
||||||
|
" 1.3023468539111032)}"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 21,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"wall.paths"
|
||||||
|
]
|
||||||
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "markdown",
|
"cell_type": "markdown",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
@@ -437,13 +496,13 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 12,
|
"execution_count": 14,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
"import numpy as np\n",
|
"import numpy as np\n",
|
||||||
"\n",
|
"\n",
|
||||||
"def fit(X, fn, key=len, repeat=1_000, degree=2) -> np.array:\n",
|
"def fit(X, fn, key=len, repeat=1000, degree=2) -> np.array:\n",
|
||||||
" \"\"\"Fit key(fn(x)) to a polynomial; plot; return polynomial coefficients.\"\"\"\n",
|
" \"\"\"Fit key(fn(x)) to a polynomial; plot; return polynomial coefficients.\"\"\"\n",
|
||||||
" Y = [monte_carlo(fn, x, key=key, repeat=repeat) for x in X]\n",
|
" Y = [monte_carlo(fn, x, key=key, repeat=repeat) for x in X]\n",
|
||||||
" coefs = np.polyfit(X, Y, 2)\n",
|
" coefs = np.polyfit(X, Y, 2)\n",
|
||||||
@@ -473,12 +532,12 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 13,
|
"execution_count": 15,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": "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 truncated
|
"image/png": "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 truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 432x288 with 1 Axes>"
|
"<Figure size 432x288 with 1 Axes>"
|
||||||
]
|
]
|
||||||
@@ -490,7 +549,7 @@
|
|||||||
}
|
}
|
||||||
],
|
],
|
||||||
"source": [
|
"source": [
|
||||||
"fit(range(1, 41), place_holds);"
|
"fit(range(2, 41), place_holds);"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@@ -502,12 +561,12 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 14,
|
"execution_count": 16,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": "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 truncated
|
"image/png": "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 truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 432x288 with 1 Axes>"
|
"<Figure size 432x288 with 1 Axes>"
|
||||||
]
|
]
|
||||||
@@ -519,7 +578,7 @@
|
|||||||
}
|
}
|
||||||
],
|
],
|
||||||
"source": [
|
"source": [
|
||||||
"fit(range(1, 26), place_holds_2d, repeat=100);"
|
"fit(range(2, 26), place_holds_2d, repeat=100);"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
|
|||||||
Reference in new issue
Block a user