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You are an AI assistant designed to help modify and generate code in existing projects following very strict instructions. | |
The user will specify a change they want to make in their codebase, and you will assist them in making that change. | |
YOUR RESPONSE SHOULD STRICTLY FOLLOW ONE OF THE FOLLOWING FORMATS: | |
``` | |
1. REQUEST ACCESS <path>: If you need to read the contents of a file to understand the codebase better. | |
The path should be the relative path to the file from the project root. | |
2. REQUEST CHANGE <path> followed by the new code block: If you have a code change to propose. |
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;;; package --- Summary | |
;;; emacs-from-scratch https://github.com/daviwil/emacs-from-scratch | |
;;; Commentary: | |
;;; Code: | |
(setq inhibit-startup-message t) | |
(scroll-bar-mode -1) ; Disable visible scrollbar | |
(tool-bar-mode -1) ; Disable the toolbar | |
(tooltip-mode -1) ; Disable tooltips |
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00:00:00: Lex Fridman The development of the COVID-19 vaccine was one of the greatest accomplishments of science in recent history no matter what that should give people hope for the future and yet it is more of a source of division. I hope we can discuss both the inspiring and the difficult ideas in this conversation so that we can do our small part in healing this division. | |
00:00:23: Albert Bourla I hope so. | |
00:00:24: Lex Fridman Take me through the day of november 8th 2020 when the fighter team, we're waiting for the results of the phase three clinical trials | |
00:00:31: Albert Bourla we had assembled in a very small office that we're having in Connecticut. Very few people. There were five I think. And in another place what we call the data monitoring committee which is a group of experts, independent experts, their own fighter. We're going to have the opportunity to um blind the data and then tell us if the study needs to continue or if it is successful or if it fails. And we were waiting for their call. So |
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import random | |
def play(bankroll, wager_perc, prob_win, payout_perc, num_turns, seed): | |
""" Plays a coin flipping game and returns the final balance | |
Inputs: | |
start (number): starting bankroll | |
wager_perc (number): percent of bankroll you want to wager at each turn | |
prob_win (number): probability of a win (e.g. 0.5 is 50% win probability) | |
payout_perc (number): the payout multiple in case of win (payout = payout_perc * wager) | |
num_turns (number): the number of turns in the game |
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Investor | Key Fund/Vehicle | Period | Annualized Returns | |
---|---|---|---|---|
Jim Simons | Medallion Fund | 1988 - 2018 | 39.10% | |
George Soros | Quantum Fund | 1969 - 2000 | 32% | |
Steven Cohen | SAC | 1992 - 2003 | 30% | |
Peter Lynch | Magellan Fund | 1977 - 1990 | 29% | |
Warren Buffet | Berkshire Hathaway | 1965 - 2018 | 20.50% | |
Ray Dalio | Pure Alpha | 1991 - 2018 | 12% |
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Year | Net Returns | Management Fee | Performance Fee | Return Before Fees | Size of Fund | Medallion Trading Profits | |
---|---|---|---|---|---|---|---|
1988 | 9.00% | 5% | 20% | 16.30% | $20 million | $3 million | |
1989 | -4.00% | 5% | 20% | 1.00% | $20 million | $0 | |
1990 | 55.00% | 5% | 20% | 77.80% | $30 million | $23 million | |
1991 | 39.40% | 5% | 20% | 54.30% | $42 million | $23 million | |
1992 | 33.60% | 5% | 20% | 47.00% | $74 million | $35 million | |
1993 | 39.10% | 5% | 20% | 53.90% | $122 million | $66 million | |
1994 | 70.70% | 5% | 20% | 93.40% | $276 million | $258 million | |
1995 | 38.30% | 5% | 20% | 52.90% | $462 million | $244 million | |
1996 | 31.50% | 5% | 20% | 44.40% | $637 million | $283 million |
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fig, axes = plt.subplots(nrows=4,ncols=1, figsize=(12, 24)) | |
axes[0].axis('off') | |
axes[0].imshow(cumberbatch, cmap=plt.cm.gray) | |
axes[0].set_title('Input image') | |
pixels_per_cell = np.array([16,16]) | |
for ax in axes.ravel()[1:]: | |
fd, hog_image = hog(cumberbatch, orientations=8, pixels_per_cell=pixels_per_cell, |
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def CaseClass(case_class_name, **kwargs): | |
types = {} | |
for k, v in kwargs.items(): | |
if type(v) is not type: | |
raise ValueError("{} is not a type".format(v)) | |
types[k] = v | |
class CaseClassImpl(): | |
initialized = False |
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import numpy as np | |
from collections import Counter | |
from matplotlib import pyplot as plt | |
import argparse | |
parser = argparse.ArgumentParser() | |
parser.add_argument("-n", "--num-trials", help="number of trials to run", type=int, default=1000) | |
parser.add_argument("-s", "--std", help="standard deviation of num of skittles", type=float, default=2) | |
parser.add_argument("-o", "--out", help="historgram out location", type=str, default="out.png") |
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import boto3 | |
from utils import download_to_s3, delete_from_s3 | |
s3_bucket = "spypy-images" | |
client=boto3.client('rekognition') | |
s3_client=boto3.client('s3') | |
def detect_images_from_urls(urls): | |
if type(urls) is str: |
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