def draw_tries_log_solved_plot(dirname):
level, solved, tries = read_csvs(dirname)
plot_trend(np.array(np.log10(solved)),
np.array(tries),
xlbl="log10(solved)",
ylbl="avg_tries",
xlim=(None, None),
ylim=(0, 20),
alphas=[0.01])
def draw_log_solved_level_plot(dirname):
level, solved, tries = read_csvs(dirname)
plot_trend(np.log10(np.array(solved)),
np.array(level),
xlbl="log10(solved)",
ylbl="level",
alphas=[0.05],
trend_func=axb,
ttl="level - log(solved)")
def draw_tries_solved_plot(dirname):
level, solved, tries = read_csvs(dirname)
plot_trend(np.array(solved),
np.array(tries),
xlbl="solved",
ylbl="avg_tries",
xlim=(0, 15000),
ylim=(0, 20),
alphas=[0.01])
def draw_solved_level_plot(dirname):
level, solved, tries = read_csvs(dirname)
plot_trend(np.array(solved),
np.array(level),
xlbl="solved",
ylbl="level",
xlim=(None, 50000),
alphas=[0.03],
ttl="level - solved")
def draw_log_tries_log_solved_plot(dirname):
level, solved, tries = read_csvs(dirname)
plot_trend(np.array(np.log10(solved)),
np.array(np.log10(tries)),
xlbl="log10(solved)",
ylbl="log10(avg_tries)",
xlim=(None, 5),
ylim=(None, 1.5),
alphas=[0.02])
def draw_log_tries_level_plot(dirname):
level, solved, tries = read_csvs(dirname)
plot_trend(np.array(np.log10(tries)),
np.array(level),
xlbl="log10(avg_tries)",
ylbl="level",
ylim=(0, 30),
alphas=[0.02],
trend_func=axb,
ttl="level - log(avg_tries)")
def draw_tries_level_plot(dirname):
level, solved, tries = read_csvs(dirname)
plot_trend(np.array(tries),
np.array(level),
xlbl="avg_tries",
ylbl="level",
xlim=(0, 15),
ylim=(0, 30),
alphas=[0.02],
ttl="level - avg_tries")
def plot_total_level_calculated_level(dirname,
f,
popt,
alpha=0.01,
unzip=True,
**kwargs):
level, solved, tries = read_csvs(dirname)
plot_level_calculated_level(np.array(level),
(np.array(solved), np.array(tries)),
f,
popt,
alpha,
unzip=unzip,
**kwargs)
def get_trend_level_log_solved_log_tries(dirname, needed_ppl=1):
level, solved, tries = read_csvs(dirname, needed_ppl)
popt, r_squared = get_popt_R2(axbyc_log, (solved, tries), level)
calculated_level = np.array(axbyc_log((solved, tries), *popt))
new_calculated_level = calculated_level * 2 - 15
print("popt :", popt, ", R^2 :", r_squared)
print("Average difference: " +
str(np.mean(abs(np.array(level) - calculated_level))))
#print("Average difference(new): " + str(np.mean(abs(np.array(level) - new_calculated_level))))
#print("Average difference(mean):" + str(np.mean(abs(np.array(level) - np.mean(np.array(level))))))
print("Average square: " +
str(np.mean(np.square(np.array(level) - calculated_level))))
#print("Average square(new): " + str(np.mean(np.square(np.array(level) - new_calculated_level))))
#print("Average square(mean): " + str(np.mean(np.square(np.array(level) - np.mean(np.array(level))))))
lnspace = np.arange(1, 30, 29 / 100)
plot_xys([level, lnspace], [calculated_level, lnspace],
styles=['o', '-'],
xlbl="Real level",
ylbl="Calculated level",
xlim=(0, 31),
ylim=(0, 31),
alphas=[0.01, 1])
def draw_calculated_level_log_solved_log_tries(dirname, a, b, c):
K = 100
xmax = 5
ymax = 1.4
level, solved, tries = read_csvs(dirname)
calculated_level = np.zeros([K, K])
for i in range(K):
for j in range(K):
x = i * xmax / K
y = j * ymax / K
calculated_level[j][i] = a * x + b * y + c
#print(level_avg)
plt.imshow(calculated_level,
cmap=plt.get_cmap('gist_ncar'),
norm=Normalize(vmin=-4, vmax=42, clip=True),
origin='lower',
extent=[0, xmax, 0, ymax],
aspect='auto')
plt.title("level per log(avg_tries), log(solved)")
plt.xlabel("log10(solved)")
plt.ylabel("log10(avg_tries)")
plt.colorbar()
plt.show()