def runExperiment(table, gamma):
print("(Expected payoff, variance) over 1000 trials is %r" % (payoffStats(table, gamma),))
reward, Gmax, weights, bestStock, tickers = exp3Stocks(table, gamma)
print("For a single run: ")
print("Payoff was %.2f" % reward)
print("Regret was %.2f" % (Gmax - reward))
print("Best stock was %s at %.2f" % (bestStock, Gmax))
print("weights: %r" % (prettyList(distr(weights)),))
def runExperiment(table, gamma):
print("(Expected payoff, variance) over 1000 trials is %r" %
(payoffStats(table, gamma), ))
reward, Gmax, weights, bestStock, tickers = exp3Stocks(table, gamma)
print("For a single run: ")
print("Payoff was %.2f" % reward)
print("Regret was %.2f" % (Gmax - reward))
print("Best stock was %s at %.2f" % (bestStock, Gmax))
print("weights: %r" % (prettyList(distr(weights)), ))
def weightsStats(table, gamma):
weightDs = [] # dictionaries of final weights across all rounds
for i in range(1000):
reward, Gmax, weights, bestStock, tickers = exp3Stocks(table, gamma)
weightDs.append(dict(zip(tickers, distr(weights))))
weightMatrix = []
for key in tickers:
print("weight stats for %s: %r" % (key, prettyList(stats(d[key] for d in weightDs))))
def weightsStats(table, gamma):
weightDs = [] # dictionaries of final weights across all rounds
for i in range(1000):
reward, Gmax, weights, bestStock, tickers = exp3Stocks(table, gamma)
weightDs.append(dict(zip(tickers, distr(weights))))
weightMatrix = []
for key in tickers:
print("weight stats for %s: %r" %
(key, prettyList(stats(d[key] for d in weightDs))))