Tetris Rotation Representation - rust

I'm watching a tutorial on how to make Tetris and I'm completely lost trying to understand how to represent the different rotations of pieces.
The tutorial references the 2009 Tetris Design Guide, particularly the slide on Tetrimino Facings:
Relevant Code in the video starts at 1:10:27
The below code represent tuples indicating coordinates at which every piece rotates around.
Kind::O => &[(0, 0), (0, 1), (1, 0), (1, 1)],
Kind::I => &[(-1, 0), (0, 0), (1, 0), (2, 0)],
Kind::T => &[(-1, 0), (0, 0), (1, 0), (0, 1)],
Kind::L => &[(-1, 0), (0, 0), (1, 0), (1, 1)],
Kind::J => &[(-1, 1), (-1, 0), (0, 0), (1, 0)],
Kind::S => &[(-1, 0), (0, 0), (0, 1), (1, 1)],
Kind::Z => &[(-1, 1), (0, 1), (0, 0), (1, 0)],
Can someone explain to me this representation system please?


Example with J [(-1, 1), (-1, 0), (0, 0), (1, 0)]:
(y)
3
2
1 A
0 B C D
-1
-1 0 1 2 (x)
where
A = (-1, 1)
B = (-1, 0)
C = ( 0, 0)
D = ( 1, 0)
(0,0) (here C) is the center of rotation.
To rotate, do this transform for each block :
clockwise: (x, y) => (-y, x)
anticlockwise: (x, y) => (y, -x)

Related

while creating pdf for repaortlab layout error

raise LayoutError(ident)
reportlab.platypus.doctemplate.LayoutError:
Flowable <Table#0x7FA13491B898 1 rows x 1 cols(tallest row 900)> with cell(0,0) containing
'<Table#0x7FA1349110B8 1 rows x 1 cols(tallest row 894)> with cell(0,0) containing\n\'<Table#0x7FA1349894E0 1 rows x 1 cols(tallest row 24)> with cell(0,0) containing\\n"<Table#0x7FA134989438 1 rows x 1 cols(tallest row 18)> with cell(0,0) containing\\\\n\\\'Vulnerability ( Virustotal Domain Report )\\\'"\''(583.2755905511812 x 900), tallest cell 900.0 points, too large on page 6 in frame 'normal'(583.2755905511812 x 794.8897637795277*) of template 'Later'
I make a PDF(A4) size for it and got the error, while Ienter code here make A3 for it the problem is solved, i want the solution for A4 size.
def getToolsTables(data, tname):
doc = SimpleDocTemplate(
"somefilename.pdf",
pagesize=A2,
topMargin=25,
bottomMargin=0,
)
main_header = []
h_arr1 = []
h_arr1.append(tname)
main_header.append(h_arr1)
mainHeader = Table(main_header, colWidths="*")
finalTable = []
main_table_header_Style = TableStyle(
[
("BACKGROUND", (0, 0), (-1, 0), "#D3D3D3"),
("TEXTCOLOR", (0, 0), (-1, -1), colors.black),
("ALIGN", (0, 0), (-1, -1), "LEFT"),
("FONTSIZE", (0, 0), (-1, -1), 12),
("FONTNAME", (0, 0), (-1, -1), "Courier-Bold"),
("TOPPADDING", (0, 0), (-1, -1), 5),
("BOTTOMPADDING", (0, 0), (-1, -1), 7),
("LINEBELOW", (0, 0), (-1, 0), 1, colors.black),
]
)
tools_table_header_Style = TableStyle(
[
("BACKGROUND", (0, 0), (-1, 0), "lightblue"),
("TEXTCOLOR", (0, 0), (-1, -1), colors.black),
("ALIGN", (0, 0), (-1, -1), "LEFT"),
("FONTSIZE", (0, 0), (-1, -1), 11),
("FONTNAME", (0, 0), (-1, -1), "Courier-Bold"),
]
)
tools_table_header_Style1 = TableStyle(
[
("BACKGROUND", (0, 0), (-1, 0), "lightgreen"),
("TEXTCOLOR", (0, 0), (-1, -1), colors.black),
("ALIGN", (0, 0), (-1, -1), "CENTER"),
("FONTSIZE", (0, 0), (-1, -1), 11),
("FONTNAME", (0, 0), (-1, -1), "Courier-Bold"),
]
)
Style2 = TableStyle(
[
("ALIGN", (0, 0), (-1, -1), "LEFT"),
("LEFTPADDING", (0, 0), (-1, -1), 0),
("RIGHTPADDING", (0, 0), (-1, -1), 0),
("BOTTOMPADDING", (0, 0), (-1, -1), -10),
]
)
Style3 = TableStyle(
[
("VALIGN", (0, 0), (-1, -1), "TOP"),
("ALIGN", (0, 0), (-1, -1), "RIGHT"),
("LEFTPADDING", (0, 0), (-1, -1), 130),
("RIGHTPADDING", (0, 0), (-1, -1), 0),
("TOPPADDING", (0, 0), (-1, -1), 0),
]
)
Style4 = TableStyle([("VALIGN", (0, 0), (-1, -1), "TOP")])
mainHeader.setStyle(
main_table_header_Style
) # adding style to table main header
finalTable.append(mainHeader)
# Create Tools Array
tools_header = []
tools_body = []
if type(data) == dict:
all_table = []
for key, value in data.items():
temp = []
temp_table = []
temp.append(key)
tool_table_header = Table([temp], colWidths="*")
tool_table_header.setStyle(tools_table_header_Style)
temp_table.append(tool_table_header)
if key != "Builtwith":
t_h = []
t_b = []
for k, v in value.items():
t_h.append(k)
t_b.append(v)
t_body = []
for index, item in enumerate(t_h):
if item != "status":
arr1 = []
arr2 = []
if type(t_b[index]) is list:
temp_txt = ""
for txt in t_b[index]:
temp_txt += txt + ", "
arr1.append(item + ":")
text = t_b[index]
wraped_text = "\n".join(
wrap(str(temp_txt[:-3]), 60)
) # 60 is line width
arr1.append(wraped_text)
else:
# MY CODE
arr2.append(arr1)
n_table = Table(arr2, [200, 370])
n_table.setStyle(Style4)
t_body.append(n_table)
tool_header = Table([temp_table], colWidths="*")
tool_body = Table([[t_body]], [200, 370])
finalTable.append(
Table(
[[[tool_header, tool_body]]],
colWidths="*",
)
)

Use splitInRow argument in the Table class so that it will split the data on the other page, for example:
tool_body = Table([[t_body]], [200, 370], splitInRow=1)

Extract feature importance of ngrams in tfidfvectorizer in SVC(kernel='linear') model

I'm wondering what causes difference in output when it should be the same. It's like my program is ignoring the sorted function and feature_names. The sorting of the coef_ is quite crucial for me to find out which features are actually helping the predictions the most. I get the individual words from vectorizer.get_feature_names but not when it is in a loop or function definition. Does anybody have any idea what could be happening, or if anybody has other methods of extracting ngram feature weights and their names for an SVC with kernel='linear'.
My code:
## load data features with removed columns based on numeric feature selection
df = pd.read_csv('preprocessed_data_all.csv', usecols=['normalized_fixed', 'TAG', 'DEP', 'level1', 'avg_wordlength',
'lexical_variety',
'avg_sentlength',
'VBD_rel_cnt',
'VBN_rel_cnt',
'VBG_rel_cnt',
'MD_rel_cnt',
'np_rel_cnt',
'clause_rel_cnt',
'clause_rel_word_cnt'])
df = df.sample(n=2000)
# define X and y
X = df.drop('level1', axis=1)
y = df.level1.values
## create pipeline for word unigrams
bow_pipe = Pipeline([
("text", ItemSelector(key="normalized_fixed")),
("bow_vec", TfidfVectorizer(analyzer='word', tokenizer=word_tokenize, binary=False, lowercase=True))
])
## create pipeline for pos tags
pos_pipe = Pipeline([
("pos", ItemSelector(key="TAG")),
("pos_vec", TfidfVectorizer(analyzer='word', tokenizer=word_tokenize, binary=False, lowercase=True))
])
## create pipeline for dependency tags
dep_pipe = Pipeline([
("dep", ItemSelector(key="DEP")),
("dep_vec", TfidfVectorizer(analyzer='word', tokenizer=word_tokenize, binary=False, lowercase=True))
])
# define classifier
svm = SVC(kernel='linear', class_weight='balanced')
# define pipeline for most important unigram extraction
pipe = Pipeline([
("feats", FeatureUnion([
("bow", bow_pipe),
("tag", pos_pipe),
("dep", dep_pipe)
])),
("clf", svm)
])
pipe.fit(X, y)
# display feature importance of BOW model
levels = df['level1'].unique()
for level in levels:
featuredf = pd.DataFrame()
labelid = list(pipe.named_steps['clf'].classes_).index(level)
feature_names = pipe.named_steps['feats'].transformer_list[0][1].named_steps['bow_vec'].get_feature_names()
topn = sorted(zip(pipe.named_steps['clf'].coef_[labelid], feature_names))[-10:]
for coef, feat in topn:
featuredf = featuredf.append(pd.Series([level, feat, coef]), ignore_index = True)
display(featuredf)
My output:
0 1 2
0 A1 ! (0, 1834)\t-0.07826243560812945\n (0, 4347)\t-0.07826243560812945\n (0, 4760)\t-0.223132736239871\n (0, 5498)\t-0.07140284578344763\n (0, 6756)\t-0.16195282546411804\n (0, 8637)\t-0.06337764014791308\n (0, 8763)\t-0.07826243560812945\n (0, 9044)\t-0.08060172162144445\n (0, 901)\t-0.0026223432774063423\n (0, 5906)\t-0.16675468967573015\n (0, 6796)\t-0.04403627031278603\n (0, 8495)\t-0.2603055807883978\n (0, 8498)\t-0.17305812627971506\n (0, 8735)\t-0.34489400420874144\n (0, 9484)\t-0.11083343873432677\n (0, 2637)\t-0.18040783909656172\n (0, 2737)\t-0.5380874813828527\n (0, 3129)\t-0.013035612996414479\n (0, 3773)\t-0.08449907288128825\n (0, 4437)\t-0.013035612996414479\n (0, 4438)\t-0.026071225992828958\n (0, 5924)\t-0.013035612996414479\n (0, 7269)\t-0.3730438143689519\n (0, 7737)\t-0.705047869548585\n (0, 8722)\t-0.024098248030544236\n :\t:\n (0, 2842)\t0.026095216126881034\n (0, 2945)\t-0.08649110380251428\n (0, 3325)\t0.02860215372933612\n (0, 3495)\t0.02860215372933612\n (0, 3539)\t0.027135689240252094\n (0, 4142)\t0.02860215372933612\n (0, 4305)\t0.026095216126881034\n (0, 4711)\t0.025288162480521178\n (0, 5173)\t0.05720430745867224\n (0, 5745)\t0.08580646118800836\n (0, 6561)\t0.025288162480521178\n (0, 6865)\t0.023162287148712983\n (0, 6980)\t0.022121814035341927\n (0, 7349)\t0.02860215372933612\n (0, 7498)\t0.02860215372933612\n (0, 7573)\t0.024071227714247634\n (0, 7606)\t-0.3512603080433229\n (0, 8034)\t0.02860215372933612\n (0, 8304)\t-0.005938550730073638\n (0, 8445)\t-0.06546829964399035\n (0, 8634)\t0.027135689240252094\n (0, 9268)\t0.02860215372933612\n (0, 9471)\t0.026095216126881034\n (0, 9630)\t0.022781224878066095\n (0, 3739)\t0.03267210725715032
0 1 2
0 B1 ! (0, 353)\t-0.00449726057217602\n (0, 802)\t-0.05617787611978642\n (0, 973)\t-0.10543173735834135\n (0, 1847)\t-0.007780155148241354\n (0, 1989)\t-0.003934155442206846\n (0, 2017)\t-0.005086622578660749\n (0, 2204)\t-0.031113872051853505\n (0, 2405)\t-0.09318613349857544\n (0, 3024)\t-0.005086622578660749\n (0, 3283)\t-0.10089509076272042\n (0, 4556)\t-0.00449726057217602\n (0, 5175)\t-0.005086622578660749\n (0, 5454)\t-0.32011264216698354\n (0, 5724)\t-0.003934155442206846\n (0, 6015)\t-0.005086622578660749\n (0, 6330)\t-0.005086622578660749\n (0, 6473)\t-0.004194952284695256\n (0, 6534)\t-0.19221655261459114\n (0, 6582)\t-0.031591903060786936\n (0, 7980)\t-0.32174386411546047\n (0, 7992)\t-0.004825825736172337\n (0, 9514)\t-0.17326784128005032\n (0, 9556)\t-0.08135115057424913\n (0, 9654)\t-0.004194952284695256\n (0, 9746)\t-0.24722791235969363\n :\t:\n (0, 2550)\t0.02860215372933612\n (0, 2842)\t0.018006244391458114\n (0, 2945)\t-0.025529859622348973\n (0, 3325)\t0.02860215372933612\n (0, 3495)\t0.02860215372933612\n (0, 3539)\t0.027135689240252094\n (0, 4142)\t0.02860215372933612\n (0, 4305)\t0.018006244391458114\n (0, 4711)\t-0.012262917681600417\n (0, 5173)\t0.05720430745867224\n (0, 5745)\t0.08580646118800836\n (0, 6561)\t-0.05074854733491452\n (0, 6865)\t0.023162287148712983\n (0, 6980)\t-0.003648836702785919\n (0, 7349)\t0.02860215372933612\n (0, 7498)\t0.02860215372933612\n (0, 7573)\t0.024071227714247634\n (0, 7606)\t-0.20173441895833755\n (0, 8034)\t0.02860215372933612\n (0, 8304)\t0.026095216126881034\n (0, 8445)\t-0.049447249347229494\n (0, 8634)\t0.027135689240252094\n (0, 9268)\t0.02860215372933612\n (0, 9471)\t-0.032859710207201465\n (0, 9630)\t0.022781224878066095
0 1 2
0 A2 ! (0, 1510)\t-0.047241319436499236\n (0, 4554)\t-0.09138323899895806\n (0, 5454)\t-0.0062230357565567634\n (0, 7756)\t-0.061785302573242856\n (0, 281)\t-0.01653184338009155\n (0, 351)\t-0.01653184338009155\n (0, 450)\t-0.3274464832370879\n (0, 809)\t-0.013387638815271769\n (0, 2051)\t-0.014616379782250303\n (0, 2586)\t-0.01653184338009155\n (0, 2741)\t-0.15810190062867993\n (0, 3224)\t-0.06225932224260644\n (0, 3373)\t-0.12280247879038902\n (0, 3421)\t-0.015684237235273946\n (0, 3819)\t-0.01653184338009155\n (0, 3833)\t-0.3359646619748352\n (0, 4068)\t-0.015684237235273946\n (0, 4402)\t-0.07152757844346042\n (0, 4649)\t-0.3279430542171356\n (0, 5524)\t-0.0899771265578215\n (0, 5790)\t-0.3885263430136202\n (0, 7822)\t-0.059872091526754725\n (0, 505)\t-0.0711692477199759\n (0, 5724)\t-0.16023961429736408\n (0, 6286)\t-0.049366239531379814\n :\t:\n (0, 2550)\t0.02860215372933612\n (0, 2842)\t0.026095216126881034\n (0, 2945)\t-0.08531697506522545\n (0, 3325)\t0.02860215372933612\n (0, 3495)\t0.02860215372933612\n (0, 3539)\t0.027135689240252094\n (0, 4142)\t0.02860215372933612\n (0, 4305)\t0.026095216126881034\n (0, 4711)\t0.007931536811222977\n (0, 5173)\t0.05720430745867224\n (0, 5745)\t0.08580646118800836\n (0, 6561)\t-0.10456248357681736\n (0, 6865)\t-0.03968381644376268\n (0, 6980)\t-0.11581955114710678\n (0, 7349)\t0.02860215372933612\n (0, 7498)\t0.02860215372933612\n (0, 7573)\t-0.28868175238220484\n (0, 7606)\t-0.03988088938576907\n (0, 8034)\t0.02860215372933612\n (0, 8304)\t0.026095216126881034\n (0, 8445)\t0.008341811597571965\n (0, 8634)\t0.027135689240252094\n (0, 9268)\t0.02860215372933612\n (0, 9471)\t-0.0659929953660953\n (0, 9630)\t-0.05573508827608763
0 1 2
0 B2 ! (0, 1604)\t-0.5452053299558446\n (0, 1611)\t-0.14349203210584277\n (0, 1786)\t-0.07926751381540288\n (0, 4402)\t-0.061638227000430465\n (0, 4469)\t-0.18047516283733558\n (0, 4483)\t-0.12632546444958545\n (0, 7467)\t-0.1657501793150448\n (0, 7953)\t-0.2027592110690899\n (0, 7991)\t-0.0705445978132748\n (0, 9157)\t-0.1576966747397613\n (0, 9746)\t-0.13158162095004766\n (0, 776)\t-0.07804759361864515\n (0, 1432)\t-0.04319046246215665\n (0, 1630)\t-0.06742619934269474\n (0, 1903)\t-0.03634857244837165\n (0, 2742)\t-0.04319046246215665\n (0, 2816)\t-0.15050859335152222\n (0, 3562)\t-0.03940488059869191\n (0, 4318)\t-0.04097603902861966\n (0, 4490)\t-0.04319046246215665\n (0, 5187)\t-0.27333877764907855\n (0, 5252)\t-0.04319046246215665\n (0, 5551)\t-0.22302927657831634\n (0, 5790)\t-0.18300512305356684\n (0, 5852)\t-0.029396346557071712\n :\t:\n (0, 2550)\t0.02860215372933612\n (0, 2842)\t0.026095216126881034\n (0, 2945)\t-0.05047091757718261\n (0, 3325)\t0.02860215372933612\n (0, 3495)\t0.02860215372933612\n (0, 3539)\t0.027135689240252094\n (0, 4142)\t0.02860215372933612\n (0, 4305)\t0.026095216126881034\n (0, 4711)\t0.025288162480521178\n (0, 5173)\t0.05720430745867224\n (0, 5745)\t0.08580646118800836\n (0, 6561)\t0.025288162480521178\n (0, 6865)\t0.023162287148712983\n (0, 6980)\t-0.09066783596741043\n (0, 7349)\t0.02860215372933612\n (0, 7498)\t0.02860215372933612\n (0, 7573)\t0.024071227714247634\n (0, 7606)\t-0.08208799126770042\n (0, 8034)\t0.02860215372933612\n (0, 8304)\t0.026095216126881034\n (0, 8445)\t-0.011737613363530505\n (0, 8634)\t-0.014588840340588008\n (0, 9268)\t0.02860215372933612\n (0, 9471)\t0.026095216126881034\n (0, 9630)\t-0.05074822281767788
0 1 2
0 C1 ! (0, 244)\t-0.09884319674162795\n (0, 690)\t-0.1388650034822139\n (0, 960)\t-0.10605470450461775\n (0, 1373)\t-0.29793485494660743\n (0, 1584)\t-0.15220560572907585\n (0, 1603)\t-0.15220560572907585\n (0, 1604)\t-0.2943386139167361\n (0, 1638)\t-0.15220560572907585\n (0, 2080)\t-0.1444018536776252\n (0, 2680)\t-0.22203398402397742\n (0, 2722)\t-0.1388650034822139\n (0, 2774)\t-0.15220560572907585\n (0, 2822)\t-0.13106125143076322\n (0, 3071)\t-0.1444018536776252\n (0, 3265)\t-0.2691405776324631\n (0, 3627)\t-0.1444018536776252\n (0, 4014)\t-0.15220560572907585\n (0, 4073)\t-0.15220560572907585\n (0, 4247)\t-0.15220560572907585\n (0, 4659)\t-0.3056381346962476\n (0, 4726)\t-0.3044112114581517\n (0, 4868)\t-0.15220560572907585\n (0, 5014)\t-0.1388650034822139\n (0, 5074)\t-0.1444018536776252\n (0, 5450)\t-0.1865505674300888\n :\t:\n (0, 2550)\t0.02860215372933612\n (0, 2842)\t0.026095216126881034\n (0, 2945)\t0.020274287275611008\n (0, 3325)\t0.02860215372933612\n (0, 3495)\t0.02860215372933612\n (0, 3539)\t0.027135689240252094\n (0, 4142)\t0.02860215372933612\n (0, 4305)\t0.026095216126881034\n (0, 4711)\t0.025288162480521178\n (0, 5173)\t0.05720430745867224\n (0, 5745)\t0.08580646118800836\n (0, 6561)\t0.025288162480521178\n (0, 6865)\t0.023162287148712983\n (0, 6980)\t-0.09559883514855932\n (0, 7349)\t0.02860215372933612\n (0, 7498)\t0.02860215372933612\n (0, 7573)\t0.024071227714247634\n (0, 7606)\t-0.13840215323517818\n (0, 8034)\t0.02860215372933612\n (0, 8304)\t0.026095216126881034\n (0, 8445)\t0.02183231985676501\n (0, 8634)\t0.027135689240252094\n (0, 9268)\t0.02860215372933612\n (0, 9471)\t0.026095216126881034\n (0, 9630)\t-0.09844846169130346
0 1 2
0 C2 ! (0, 1510)\t-0.05959414411701482\n (0, 1925)\t-0.07930619936349027\n (0, 4554)\t-0.12239420823695288\n (0, 6337)\t-0.10751794751817559\n (0, 6919)\t-0.11905736382524099\n (0, 7940)\t-0.4509514511406135\n (0, 8674)\t-0.06634760477509609\n (0, 8876)\t-0.09955165597499022\n (0, 281)\t-0.11414308129642091\n (0, 351)\t-0.11414308129642091\n (0, 450)\t-0.6510470682457199\n (0, 2051)\t-0.2941575097638065\n (0, 2586)\t-0.11414308129642091\n (0, 2741)\t-0.2826990863413035\n (0, 3224)\t-0.19484491340690077\n (0, 3421)\t-0.15593785912375202\n (0, 3819)\t-0.11414308129642091\n (0, 3833)\t-0.5959237864651308\n (0, 4068)\t-0.17821843998174858\n (0, 7822)\t-0.18737390753407893\n (0, 505)\t-0.17144684026449575\n (0, 1605)\t-0.1761432003933918\n (0, 2087)\t-0.30641937334729397\n (0, 2435)\t-0.01959392434397809\n (0, 2544)\t-0.04205087201608662\n :\t:\n (0, 1543)\t-0.10270397939145476\n (0, 4091)\t0.03510805887202453\n (0, 4621)\t0.03774871894954156\n (0, 5548)\t0.11591078340050759\n (0, 7216)\t0.10996790758187949\n (0, 8462)\t0.11591078340050759\n (0, 6902)\t0.0953038712770817\n (0, 275)\t0.05201608398317632\n (0, 2309)\t-0.11518506286788033\n (0, 5602)\t0.130229016246211\n (0, 6856)\t0.028341275217697887\n (0, 9697)\t0.130229016246211\n (0, 5898)\t0.11137290054484401\n (0, 5921)\t0.09019079917603834\n (0, 6930)\t-0.41244426698962444\n (0, 7468)\t0.11137290054484401\n (0, 8776)\t0.08870699446777193\n (0, 981)\t0.1483908905002444\n (0, 2084)\t0.1483908905002444\n (0, 3159)\t0.02642985841317767\n (0, 3306)\t-0.02982547695328551\n (0, 3508)\t0.055716356911763666\n (0, 8305)\t0.025416449215215475\n (0, 8431)\t0.02642985841317767\n (0, 8682)\t0.027858178455881833
What the output format is supposed to be:
bs obećao -4.50534985071
bs pošto -4.50534985071
bs prava -4.50534985071
bs predstavlja -4.50534985071
bs prošlosedmičnom -4.50534985071
bs sjeveru -4.50534985071
bs taj -4.50534985071
bs vladavine -4.50534985071
bs će -4.50534985071
bs da -4.0998847426
pt teve -4.63472898823
pt tive -4.63472898823
pt todas -4.63472898823
pt vida -4.63472898823
pt de -4.22926388012
pt foi -4.22926388012
pt mais -4.22926388012
pt me -4.22926388012
pt as -3.94158180767
pt que -3.94158180767
Linked to second reply on How to get most informative features for scikit-learn classifier for different class?
Also linked to this is the exact question posted as the last reply on the second reply to this post's question:
Amazing #alvas I tried the above function but the output looks like this:POS aaeguno móvil (0, 60) -0.0375375709849 (0, 300) -0.0375375709849 (0, 3279) -0.0375375709849 instead of returning the class, followed by the word and the float. Any idea of why this is happening?. Thanks! – newWithPython Mar 15 '15 at 0:45
But no one has replied to this, and since I have a very low reputation, I cannot request more information there either.
It's taken a week out of my schedule and I really cannot afford to spend much longer on this. It's the last piece of the puzzle that is my thesis, which is not going to be perfect but I just need to get it done and graduate. So any help would be greatly appreciated!
Also let me know what I could add to make this question clearer, it's maybe my third or second one on this platform.

As it turns out, using sklearn's LinearSVC()produces the right output, so SVC(kernel='linear') requires other means of ngram importance extraction. I just switched to LinearSVC as it improved my model in general anyway.

networkx: how to control node postion on graphs

In order to draw a graph I have defined the pos as a dictionary:
pos={'0025': (0, 0), '0023': (400, 0), '0028': (800, 0), '0029': (1200, 0), '0045': (1600, 0), '0057': (2000, 0), '0062': (2400, 0), '0083': (2800, 0), '0107': (3200, 0), '0111': (3600, 0), '0141': (4000, 0), '0144': (4400, 0), '0026': (800.0, 1), '0050': (1200.0, 1), '0115': (2600.0, 1)}
But I do not get the nodes represented where I expected:
Notice that the relative position is OK but the scale is not right. Probably I am misunderstanding something.
Here is the code I wrote to draw the graph:
def draw_G_msn_tr(self):
pos={'0025': (0, 0), '0023': (400, 0), '0028': (800, 0), '0029': (1200, 0), '0045': (1600, 0), '0057': (2000, 0), '0062': (2400, 0), '0083': (2800, 0), '0107': (3200, 0), '0111': (3600, 0), '0141': (4000, 0), '0144': (4400, 0), '0026': (800.0, 1), '0050': (1200.0, 1), '0115': (2600.0, 1)}
nx.draw(self.G_msn_tr,pos=pos, node_color=self.node_colors, with_labels=True, node_size=900)
plt.axis("off")
plt.show()
I need more space between nodes in order to show edge labels
Thank you in advance for your support.
BR.

How to find 5 closest points based on cosine distance in S from P

Consider you are given n data points in the form of list of tuples like S=[(x1,y1),(x2,y2),(x3,y3),(x4,y4),(x5,y5),..,(xn,yn)] and a point P=(p,q)
your task is to find 5 closest points(based on cosine distance) in S from P
Ex:
S= [(1,2),(3,4),(-1,1),(6,-7),(0, 6),(-5,-8),(-1,-1)(6,0),(1,-1)]
P= (3,-4)
I have tried with below code
import math
data = [(1,2),(3,4),(-1,1),(6,-7),(0, 6),(-5,-8),(-1,-1)(6,0),(1,-1)]
data.sort(key=lambda x: math.sqrt((float(x.split(",")[0]) - 3)**2 +
(float(x.split(",")[1]) -(-4))**2))
print(data)
I should get 5 closest points in S from P.

You have a missing comma in the defenition of data
You have a list of tuples but for some reason you used split as if it was a list of strings.
If you fix these 2 errors it works. You just need to grab the first 5 elements from data:
import math
data = [(1, 2), (3, 4), (-1, 1), (6, -7), (0, 6), (-5, -8), (-1, -1), (6, 0), (1, -1)]
data.sort(key=lambda x: math.sqrt((float(x[0]) - 3) ** 2 +
(float(x[1]) - (-4)) ** 2))
print(data[:5])
Outputs
[(1, -1), (6, -7), (-1, -1), (6, 0), (1, 2)]
(Next time, if you get an error please explain it in your question)

cosine_dist = []
for a, b in S:
num = a * P[0] + b * P[1]
den = math.sqrt(a * a + b * b) * math.sqrt(P[0] * P[0] + P[1] * P[1])
cosine_dist.append(math.acos(num/den))
X = cosine_dist
Y = [S for S in sorted(zip(S,X), key=lambda i:i[1])]
k = Y[:5]
for i, j in k:
print(i)
P = (3, -4)
S = [(1, 2), (3, 4), (-1, 1), (6, -7), (0, 6), (-5, -8), (-1, -1), (6, 0), (1, -1)]

How to issue a command to all the lines in a visual block?

How can I perform something like dtR (delete until you meet the R in the line) across all the lines in a linewise visual block, or say, for the next n lines?
The long story
Let's suppose there is the following piece of code:
L0 = Rotated_Shape((1, 0), (-1, 0), (-1, 1))
L90 = Rotated_Shape((0, -1), (0, 1), (1, 1))
L180 = Rotated_Shape((-1, 0), (1, 0), (1, -1))
L270 = Rotated_Shape((0, 1), (0, -1), (-1, -1))
O0 = Rotated_Shape((1, 0), (1, -1), (0, -1))
O90 = Rotated_Shape((0, -1), (-1, -1), (-1, 0))
O180 = Rotated_Shape((-1, 0), (-1, 1), (0, 1))
O270 = Rotated_Shape((0, 1), (1, 1), (1, 0))
S0 = Rotated_Shape((-1, 0), (0, -1), (1, -1))
S90 = Rotated_Shape((0, 1), (-1, 0), (-1, -1))
S180 = Rotated_Shape((1, 0), (0, 1), (-1, 1))
S270 = Rotated_Shape((0, -1), (1, 0), (1, 1))
T0 = Rotated_Shape((0, 1), (-1, 0), (1, 0))
T90 = Rotated_Shape((1, 0), (0, 1), (0, -1))
T180 = Rotated_Shape((0, -1), (1, 0), (-1, 0))
T270 = Rotated_Shape((-1, 0), (0, -1), (0, 1))
Z0 = Rotated_Shape((1, 0), (0, -1), (-1, -1))
Z90 = Rotated_Shape((0, -1), (-1, 0), (-1, 1))
Z180 = Rotated_Shape((-1, 0), (0, 1), (1, 1))
Z270 = Rotated_Shape((0, 1), (1, 0), (1, -1))
I am now reworking this code and I want all these lines to become what is on the right side of the equation (assignment), i. e. delete the variable names, the equal sign and keep only the Rotated_Shape((... part.
I guess the best way to do this particular case is to search and replace a regular expression, but my first approach I didn't know how to do was to use the blockwise-visual or line-visual mode for each four of them and do something like dtR (delete until you meet the R in the line).

You can highlight those lines with a visual block and then use :help 'normal'.
The end result post-select looks something like: :'<,'>normal dtR
You can also abbreviate normal to norm.

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