def xperiment():
df = pull_merged_data(feature=FEATURE).dropna()
X, Y = df[PARAM].as_matrix(), df[FEATURE].as_matrix()
inspection.category_frequencies(Y)
Y, X = drop_lowNs(10, Y, X)
inspection.correlation(X, names=PARAM)
pairwise_T2(X, Y, dumproot=projectroot, xpid=f"PairwiseT2_{FEATURE}.xlsx")
F, p = manova(X, Y)
print("-"*50)
lda = LDA(n_components=2).fit(X, Y) # type: LDA
smexvar = lda.explained_variance_ratio_
scat = scatter.Scatter2D(lda.transform(X), Y, title=f"LDA ({smexvar.sum():.2%})\nMANOVA: F = {F:.4f}, p = {p:.4f}",
axlabels=[f"Latent0{i} ({ev:.2%})" for i, ev in enumerate(smexvar, start=1)])
is_many = len(np.unique(Y)) > 5
scat.split_scatter(legend=not is_many, show=True, center=is_many, label=is_many)
def normality():
paramnames = df.columns[1:]
full(X, names=paramnames)
for i, colname in enumerate(paramnames):
outpath = f"{projectroot}N27.Results/{colname}.png"
fullplot(X[:, i],
colname,
histbins=7,
show=False,
dumppath=outpath,
histlabels=(r"$\delta^{13}C$ izotóparány",
"Előfordulási valószínűség"),
problabels=("Elméleti Z-érték",
r"$\delta^{13}C$ izotóparány"))
correlation(X, paramnames)
def inspect_classes():
from csxdata.stats import normaltest, inspection
from csxdata.visual.histogram import fullplot
names = []
for l in "YP":
for i in range(10):
names.append(l + str(i))
X, Y = load_dataset(as_matrix=False, as_string=True)
inspection.category_frequencies(Y)
inspection.correlation(X, names=names)
normaltest.full(X, names=names)
for name, column in zip(names, X.T):
fullplot(column, name)
import numpy as np
from matplotlib import pyplot as plt
from csxdata.stats.inspection import correlation
from SciProjects.rich.stockshu.data_util import pull_data
Y, header = pull_data()
X = np.arange(1, len(Y))
correlation(Y, names=header)
for y, col in zip(Y.T, header):
plt.plot(X, y, label=col)
plt.legend()
plt.show()
from csxdata.stats.inspection import correlation from rich.currency.util import pull_data X, Y, header = pull_data() correlation(X, Y)
from SciProjects.sophie import projectroot
from csxdata.utilities.parser import parse_csv
from csxdata.utilities.vectorop import dropna
from csxdata.stats.inspection import category_frequencies, correlation
from csxdata.stats.normaltest import full
X, Y, head = parse_csv(projectroot + "01GEO.csv",
indeps=2,
headers=1,
decimal=True)
category_frequencies(Y)
X, Y = dropna(X, Y)
correlation(X, ["X", "Y", "DH1", "DH2"])
full(X)
def correlations():
from csxdata.stats.inspection import correlation
correlation(X, names)