def generate_die_roll_sum_distribution(number_of_rolls, number_of_dice):
values = []
die_or_dice = "Die"
if number_of_dice > 1: die_or_dice = "Dice"
logging.info("Rolling " + str(number_of_dice) + " " + die_or_dice + " " +
str(number_of_rolls) + " times")
# execute number_of_rolls rolls of number_of_dice dice and accumulate the sum of the values
for i in range(0, number_of_rolls):
sum = 0
for d in range(0, number_of_dice):
sum += random.randint(1, 6)
values.extend([sum])
# plot the output
charting.plot_distribution("dice_rolls.png",
"Roll Distribution - " + str(number_of_dice) +
" " + die_or_dice + ", " +
str(number_of_rolls) + " rolls",
"Sum of Values",
bucket_size=1,
data=values,
show_bucket_values=True,
color='#59799e',
normalize=True)
def generate_die_roll_sum_distribution(number_of_rolls, number_of_dice):
values = []
die_or_dice = "Die"
if number_of_dice > 1: die_or_dice = "Dice"
logging.info("Rolling " + str(number_of_dice) + " " + die_or_dice + " " + str(number_of_rolls) + " times");
# execute number_of_rolls rolls of number_of_dice dice and accumulate the sum of the values
for i in range(0, number_of_rolls):
sum = 0
for d in range(0, number_of_dice):
sum += random.randint(1,6)
values.extend([sum])
# plot the output
charting.plot_distribution("dice_rolls.png",
"Roll Distribution - " + str(number_of_dice) + " " + die_or_dice + ", " + str(number_of_rolls) + " rolls",
"Sum of Values",
bucket_size=1,
data=values,
show_bucket_values=True,
color='#59799e',
normalize=True)
def generate_poisson_distributed_pdf(number_of_samples, lam):
# generate number_of_samples random numbers with a Poisson dist
values = numpy.random.poisson(lam, number_of_samples).tolist()
# plot the distribution
charting.plot_distribution("poisson_distribution.png",
"Poisson Distribution - " + str(number_of_samples) + ", lambda = " + str(lam),
"Likelihoods",
bucket_size=1,
data=values,
show_bucket_values=True,
color='#59799e',
normalize=True);
def generate_uniformly_distributed_pdf(number_of_samples):
values = []
# generate uniformly distributed random values
for i in range(0, number_of_samples):
values.extend([random.random()])
# plot the distribution
charting.plot_distribution("uniform_distribution.png",
"Uniform Distribution (" + str(number_of_samples) + ")",
"Likelihoods",
num_buckets=10,
data=values,
show_bucket_values=True,
color='#59799e',
normalize=True);
def generate_gaussian_distributed_pdf(number_of_samples, mean, std_dev):
values = []
# generate gaussian distribution with mean and standard deviation
for i in range(0, number_of_samples):
values.extend([random.gauss(mean, std_dev)])
# chart the output
charting.plot_distribution("gaussian_distribution.png",
"Gaussian Distribution (" + str(number_of_samples) + ", mean = " + str(mean) + ", stnd dev = " + str(std_dev) + ")",
"Likelihoods",
num_buckets=10+int(std_dev*10),
data=values,
show_bucket_values=True,
color='#59799e',
normalize=True);
def generate_poisson_distributed_pdf(number_of_samples, lam):
# generate number_of_samples random numbers with a Poisson dist
values = numpy.random.poisson(lam, number_of_samples).tolist()
# plot the distribution
charting.plot_distribution("poisson_distribution.png",
"Poisson Distribution - " +
str(number_of_samples) + ", lambda = " +
str(lam),
"Likelihoods",
bucket_size=1,
data=values,
show_bucket_values=True,
color='#59799e',
normalize=True)
def generate_uniformly_distributed_pdf(number_of_samples):
values = []
# generate uniformly distributed random values
for i in range(0, number_of_samples):
values.extend([random.random()])
# plot the distribution
charting.plot_distribution("uniform_distribution.png",
"Uniform Distribution (" +
str(number_of_samples) + ")",
"Likelihoods",
num_buckets=10,
data=values,
show_bucket_values=True,
color='#59799e',
normalize=True)
def generate_gaussian_distributed_pdf(number_of_samples, mean, std_dev):
values = []
# generate gaussian distribution with mean and standard deviation
for i in range(0, number_of_samples):
values.extend([random.gauss(mean, std_dev)])
# chart the output
charting.plot_distribution(
"gaussian_distribution.png",
"Gaussian Distribution (" + str(number_of_samples) + ", mean = " +
str(mean) + ", stnd dev = " + str(std_dev) + ")",
"Likelihoods",
num_buckets=10 + int(std_dev * 10),
data=values,
show_bucket_values=True,
color='#59799e',
normalize=True)
