Esempio n. 1
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import random
from colornames.convex_hull_classifier import ConvexHullClassifier
from colornames.diagnostics import write_diagnostic_html, random_cie_colors, make_cie_gradient
import common

coords, names = common.read_old_data("../../data/old_data.csv")

thresholds = [2.0, 0.0, 0.0]
clf = ConvexHullClassifier(thresholds)
clf.fit(coords, names)

y, dist, thresh, ynames, inhull, numhulls = clf.predict(coords)
write_diagnostic_html("../color_page.html", coords, names, y, ynames, dist, thresh, inhull, numhulls)

random.seed(123456)
rcie = random_cie_colors(200)
yr, distr, threshr, ynamesr, inhullr, numhullsr = clf.predict(rcie)
write_diagnostic_html("../color_rcie.html", rcie, None, yr, ynamesr, distr, threshr, inhullr, numhullsr)

gcie = make_cie_gradient(200, clf.hull_centroid("RED"), clf.hull_centroid("ORANGE"))
yg, distg, threshg, ynamesg, inhullg, numhullsg = clf.predict(gcie)
write_diagnostic_html("../color_gcie.html", gcie, None, yg, ynamesg, distg, threshg, inhullg, numhullsg)
Esempio n. 2
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thresholds = [2.0, 0.0, 0.0]
clf = ConvexHullClassifier(thresholds)
clf.fit(coords, names)

new_data_filename = '../../data/new_data.csv'
new_data = pd.read_csv(new_data_filename)
new_coords = new_data.ix[:, 2:5]

y, dist, thresh, ynames, inhull, numhulls = clf.predict(new_coords)

dist_sort = dist.values
dist_sort.sort(axis=1)
dist_sort = pd.DataFrame(dist_sort, dist.index)
dist_sort.columns = ['dist_sort_%s' % c for c in dist_sort.columns]

dist0 = dist_sort.iloc[:, 0].copy()
dist1 = dist_sort.iloc[:, 1].copy()

selected = (
    (numhulls < 1) |
    (numhulls > 1) |
    ((ynames == 'NEUTRAL') & (dist0 < 1.0) & (dist1 < 10.0)) |
    ((ynames != 'NEUTRAL') & (dist0 < 3.0) & (dist1 < 10.0)))

write_diagnostic_html('../color_new.html', new_coords, None, y, ynames, dist, thresh, inhull, numhulls, selected)

subset = pd.concat([new_data, selected, inhull, numhulls, dist_sort.iloc[:, 0:3]], axis=1)
subset.columns = ['line_num', 'color_patch', 'cie_lstar', 'cie_astar', 'cie_bstar',
                  'selected', 'inhull', 'numhulls', 'dist0', 'dist1', 'dist2']
subset.to_csv('../subset.csv', index=False)
Esempio n. 3
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clf.fit(coords, names)

new_data_filename = '../../data/new_data.csv'
new_data = pd.read_csv(new_data_filename)
new_coords = new_data.ix[:, 2:5]

y, dist, thresh, ynames, inhull, numhulls = clf.predict(new_coords)

dist_sort = dist.values
dist_sort.sort(axis=1)
dist_sort = pd.DataFrame(dist_sort, dist.index)
dist_sort.columns = ['dist_sort_%s' % c for c in dist_sort.columns]

dist0 = dist_sort.iloc[:, 0].copy()
dist1 = dist_sort.iloc[:, 1].copy()

selected = ((numhulls < 1) | (numhulls > 1) | ((ynames == 'NEUTRAL') &
                                               (dist0 < 1.0) & (dist1 < 10.0))
            | ((ynames != 'NEUTRAL') & (dist0 < 3.0) & (dist1 < 10.0)))

write_diagnostic_html('../color_new.html', new_coords, None, y, ynames, dist,
                      thresh, inhull, numhulls, selected)

subset = pd.concat(
    [new_data, selected, inhull, numhulls, dist_sort.iloc[:, 0:3]], axis=1)
subset.columns = [
    'line_num', 'color_patch', 'cie_lstar', 'cie_astar', 'cie_bstar',
    'selected', 'inhull', 'numhulls', 'dist0', 'dist1', 'dist2'
]
subset.to_csv('../subset.csv', index=False)
Esempio n. 4
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import random
from colornames.convex_hull_classifier import ConvexHullClassifier
from colornames.diagnostics import write_diagnostic_html, random_cie_colors, make_cie_gradient
import common

coords, names = common.read_old_data('../../data/old_data.csv')

thresholds = [2.0, 0.0, 0.0]
clf = ConvexHullClassifier(thresholds)
clf.fit(coords, names)

y, dist, thresh, ynames, inhull, numhulls = clf.predict(coords)
write_diagnostic_html('../color_page.html', coords, names, y, ynames, dist,
                      thresh, inhull, numhulls)

random.seed(123456)
rcie = random_cie_colors(200)
yr, distr, threshr, ynamesr, inhullr, numhullsr = clf.predict(rcie)
write_diagnostic_html('../color_rcie.html', rcie, None, yr, ynamesr, distr,
                      threshr, inhullr, numhullsr)

gcie = make_cie_gradient(200, clf.hull_centroid('RED'),
                         clf.hull_centroid('ORANGE'))
yg, distg, threshg, ynamesg, inhullg, numhullsg = clf.predict(gcie)
write_diagnostic_html('../color_gcie.html', gcie, None, yg, ynamesg, distg,
                      threshg, inhullg, numhullsg)