How to solve Einsteins riddle with z3py the elegant way? - python
z3py guys have provided a code what is based here https://github.com/0vercl0k/z3-playground/blob/master/einstein_riddle_z3.py . However comparing to this https://artificialcognition.github.io/who-owns-the-zebra the solution is rather complicated, long and ugly. I do not really want to switch the libraries as z3py seems more advanced and maintained. So I started to work on my version, but I fail to declare some parts (lack of knowledge or not possible?). Here is what I have and where I get stuck (2 comments):
from z3 import *
color = Int('color')
nationality = Int('nationality')
beverage = Int('beverage')
cigar = Int('cigar')
pet = Int('pet')
house = Int('house')
color_variations = Or(color==1, color==2, color==3, color==4, color==5)
nationality_variations = Or(nationality==1, nationality==2, nationality==3, nationality==4, nationality==5)
beverage_variations = Or(beverage==1, beverage==2, beverage==3, beverage==4, beverage==5)
cigar_variations = Or(cigar==1, cigar==2, cigar==3, cigar==4, cigar==5)
pet_variations = Or(pet==1, pet==2, pet==3, pet==4, pet==5)
house_variations = Or(house==1, house==2, house==3, house==4, house==5)
s = Solver()
s.add(color_variations)
s.add(nationality_variations)
s.add(beverage_variations)
s.add(cigar_variations)
s.add(pet_variations)
s.add(house_variations)
# This is not right
#s.add(Distinct([color, nationality, beverage, cigar, pet]))
s.add(And(Implies(nationality==1, color==1), Implies(color==1, nationality==1))) #the Brit (nationality==1) lives in the red (color==1) house
s.add(And(Implies(nationality==2, pet==1), Implies(pet==1, nationality==2))) #the Swede (nationality==2) keeps dogs (pet==1) as pets
s.add(And(Implies(nationality==3, beverage==1), Implies(beverage==1, nationality==3))) #the Dane (nationality==3) drinks tea (beverage=1)
s.add(And(Implies(color==2, beverage==2), Implies(beverage==2, color==2))) #the green (color==2) house's owner drinks coffee (beverage==2)
s.add(And(Implies(cigar==1, pet==2), Implies(pet==2, cigar==1))) #the person who smokes Pall Mall (cigar==1) rears birds ([pet==2])
s.add(And(Implies(color==4, cigar==2), Implies(cigar==2, color==4))) #the owner of the yellow (color==4) house smokes Dunhill (cigar==2)
s.add(And(Implies(house==3, beverage==3), Implies(beverage==3, house==3))) #the man living in the center (hause==3) house drinks milk (beverage==3)
s.add(And(Implies(nationality==4, house==1), Implies(house==1, nationality==4))) #the Norwegian (nationality==4) lives in the first house (house==1)
s.add(And(Implies(cigar==3, beverage==4), Implies(beverage==4, cigar==3))) #the owner who smokes BlueMaster (cigar==3) drinks beer (beverage==4)
s.add(And(Implies(nationality==5, cigar==4), Implies(cigar==4, nationality==5))) #the German (nationality==5) smokes Prince (cigar==4)
# I can't figure our this part, so I can keep it short and efficient
# the green (color==2) house is on the left of the white (color==3) house
Currently looking into direction of ForAll and Functions
You should use an enumeration for the different kinds of things here. Also, you can't just get away with having one color variable: After all, each house has a different color, and you want to track it separately. A better idea is to make color, nationality, etc., all uninterpreted functions; mapping numbers to colors, countries, etc., respectively.
Here's the Haskell solution for this problem, using the SBV library which uses z3 via the SMTLib interface, following the strategy I described: https://hackage.haskell.org/package/sbv-8.8/docs/src/Documentation.SBV.Examples.Puzzles.Fish.html
Translating this strategy to Python, we have:
from z3 import *
# Sorts of things we have
Color , (Red , Green , White , Yellow , Blue) = EnumSort('Color' , ('Red' , 'Green' , 'White' , 'Yellow' , 'Blue'))
Nationality, (Briton , Dane , Swede , Norwegian, German) = EnumSort('Nationality', ('Briton' , 'Dane' , 'Swede' , 'Norwegian', 'German'))
Beverage , (Tea , Coffee , Milk , Beer , Water) = EnumSort('Beverage' , ('Tea' , 'Coffee' , 'Milk' , 'Beer' , 'Water'))
Pet , (Dog , Horse , Cat , Bird , Fish) = EnumSort('Pet' , ('Dog' , 'Horse' , 'Cat' , 'Bird' , 'Fish'))
Sport , (Football, Baseball, Volleyball, Hockey , Tennis) = EnumSort('Sport' , ('Football', 'Baseball', 'Volleyball', 'Hockey' , 'Tennis'))
# Uninterpreted functions to match "houses" to these sorts. We represent houses by regular symbolic integers.
c = Function('color', IntSort(), Color)
n = Function('nationality', IntSort(), Nationality)
b = Function('beverage', IntSort(), Beverage)
p = Function('pet', IntSort(), Pet)
s = Function('sport', IntSort(), Sport)
S = Solver()
# Create a new fresh variable. We don't care about its name
v = 0
def newVar():
global v
i = Int("v" + str(v))
v = v + 1
S.add(1 <= i, i <= 5)
return i
# Assert a new fact. This is just a synonym for add, but keeps everything uniform
def fact0(f):
S.add(f)
# Assert a fact about a new fresh variable
def fact1(f):
i = newVar()
S.add(f(i))
# Assert a fact about two fresh variables
def fact2(f):
i = newVar()
j = newVar()
S.add(i != j)
S.add(f(i, j))
# Assert two houses are next to each other
def neighbor(i, j):
return (Or(i == j+1, j == i+1))
fact1 (lambda i : And(n(i) == Briton, c(i) == Red)) # The Briton lives in the red house.
fact1 (lambda i : And(n(i) == Swede, p(i) == Dog)) # The Swede keeps dogs as pets.
fact1 (lambda i : And(n(i) == Dane, b(i) == Tea)) # The Dane drinks tea.
fact2 (lambda i, j: And(c(i) == Green, c(j) == White, i == j-1)) # The green house is left to the white house.
fact1 (lambda i : And(c(i) == Green, b(i) == Coffee)) # The owner of the green house drinks coffee.
fact1 (lambda i : And(s(i) == Football, p(i) == Bird)) # The person who plays football rears birds.
fact1 (lambda i : And(c(i) == Yellow, s(i) == Baseball)) # The owner of the yellow house plays baseball.
fact0 ( b(3) == Milk) # The man living in the center house drinks milk.
fact0 ( n(1) == Norwegian) # The Norwegian lives in the first house.
fact2 (lambda i, j: And(s(i) == Volleyball, p(j) == Cat, neighbor(i, j))) # The man who plays volleyball lives next to the one who keeps cats.
fact2 (lambda i, j: And(p(i) == Horse, s(j) == Baseball, neighbor(i, j))) # The man who keeps the horse lives next to the one who plays baseball.
fact1 (lambda i : And(s(i) == Tennis, b(i) == Beer)) # The owner who plays tennis drinks beer.
fact1 (lambda i : And(n(i) == German, s(i) == Hockey)) # The German plays hockey.
fact2 (lambda i, j: And(n(i) == Norwegian, c(j) == Blue, neighbor(i, j))) # The Norwegian lives next to the blue house.
fact2 (lambda i, j: And(s(i) == Volleyball, b(j) == Water, neighbor(i, j))) # The man who plays volleyball has a neighbor who drinks water.
# Determine who owns the fish
fishOwner = Const("fishOwner", Nationality)
fact1 (lambda i: And(n(i) == fishOwner, p(i) == Fish))
r = S.check()
if r == sat:
m = S.model()
print(m[fishOwner])
else:
print("Solver said: %s" % r)
When I run this, I get:
$ python a.py
German
Showing that the fish-owner is German. I think your original problem had a different but similar set of constraints, you can easily use the same strategy to solve your original.
It's also instructional to look at the output of:
print(m)
in the sat case. This prints:
[v5 = 4,
v9 = 1,
v16 = 2,
v12 = 5,
v14 = 1,
v2 = 2,
v0 = 3,
v10 = 2,
v18 = 4,
v15 = 2,
v6 = 3,
v7 = 1,
v4 = 5,
v8 = 2,
v17 = 1,
v11 = 1,
v1 = 5,
v13 = 4,
fishOwner = German,
v3 = 4,
nationality = [5 -> Swede,
2 -> Dane,
1 -> Norwegian,
4 -> German,
else -> Briton],
color = [5 -> White,
4 -> Green,
1 -> Yellow,
2 -> Blue,
else -> Red],
pet = [3 -> Bird,
1 -> Cat,
2 -> Horse,
4 -> Fish,
else -> Dog],
beverage = [4 -> Coffee,
3 -> Milk,
5 -> Beer,
1 -> Water,
else -> Tea],
sport = [1 -> Baseball,
2 -> Volleyball,
5 -> Tennis,
4 -> Hockey,
else -> Football]]
Ignore all the assignments to vN variables, those are the ones we used internally for modeling purposes. But you can see how z3 mapped each of the uninterpreted functions to the corresponding values. For all of these, the value mapped is the number of the house to the corresponding value that satisfies the puzzle constraints. You can programmatically extract a full solution to the puzzle as needed by using the information contained in this model.
Related
How do I join text if key value are the same
my code
import requests
request = requests.get("https://itspaudal-git.github.io/jsonapi/roku.json")
package_json = request.json()
menu = package_json['Chicago']['Menu']['Strawberry Pie']
for i in menu:
product = i['item']
weight = i['weight']
uom = i['uom']
container2products = {}
for j in i['tags']:
container = j['container']
container2products.setdefault(container,[])
container2products[container].append(product)
for container, products_list in container2products.items():
products_str = '&'.join(products_list)
print(products_str, container)
I was wondering if someone could point to me the right direction how to concatenate if key values are the same. My current output is
Whipping Cream 1 oz cup
Water tray 1
Cornstarch tray 1
Sugar 1 oz cup
fresh strawberries 20 oz cup
and I want it to be
Whipping Cream & Sugar 1 oz cup
Water & Constarch tray 1
fresh stawberries 20 oz cup
I have made a nfew changes to your code and also added some comment for undertsanding.
We are using a dictionary to store values instead of simple lists.
Hope it helps.
import requests
request = requests.get("https://itspaudal-git.github.io/jsonapi/roku.json")
package_json = request.json()
#This is dictionaru used to store items and thier quantities
items = {}
menu = package_json['Chicago']['Menu']['Strawberry Pie']
for i in menu:
product = i['item']
weight = i['weight']
uom = i['uom']
container2products = {}
for j in i['tags']:
container = j['container']
#add item in same key if present
if container in items:
items[container] = items[container] + [product]
else:
#add item in new key if not present
items[container] = [product]
#looping though the product quantities
for key in items:
print(" & ".join(items[key]), key)
pyspark- connect two columns array elements
I am very new to pays-ark.
I have a Dataframe including two columns and each column has strings in an array format:
How can I connected the element of array from first column to the value in the same position in an array of other column.
if I convert the Dataframe to Pandas Dataframe in data brick the below code works but it will not keep the arrays in a correct format.
for item in list_x:
df_head[item] = "x"
value = df_head['materialText'].values
headName = df_head['materialTextPart'].values
value_list = []
for k in range(len(df_head)):
# print(k)
if type(value[k]) == np.float:
continue;
else:
value_array =value[k][0:].split(',')
# print(value_array)
headName_array = headName[k][1:-2].split(',')
for m in range(len(headName_array)):
if (headName_array[m] == item) or (headName_array[m] ==' '+item) or (headName_array[m] ==' '+item.replace('s','')):
columnName = item
columnValue = df_head.loc[k,columnName]
if columnValue == 'x':
df_head.loc[k,columnName] = value_array[m]
else:
df_head.loc[k,columnName]= df_head.loc[k,columnName]+ ',' + value_array[m]
df_head[item] = df_head[item].replace('x', np.nan)
Example of columns:
["Fabric:", "Wall bracket:", "Top rail/ Bottom rail:"]
["100 % polyester (100% recycled), PET plastic", "Steel, Polycarbonate/ABS plastic, Powder coating", "Aluminium, Powder coating"]
materialTextPart
materialText
["Fabric:", "Wall bracket:", "Top rail/ Bottom rail:"]
["100 % polyester (100% recycled), PET plastic", "Steel, Polycarbonate/ABS plastic, Powder coating", "Aluminium, Powder coating"]
["Ticking:", "Filling:", "Ticking, underside:", "Comfort filling:", "Ticking:"]
["100 % polyester (100% recycled)", "100 % polyester", "100% polypropylene", "Polyurethane foam 28 kg/cu.m.", "100% polyester"]
As I mentioned in my comment -
from pyspark.sql.functions import *
from pyspark.sql.types import *
df = spark.createDataFrame( data = [
(["Fabric:", "Wall bracket:", "Top rail/ Bottom rail:"],
["100 % polyester (100% recycled), PET plastic", "Steel, Polycarbonate/ABS plastic, Powder coating", "Aluminium, Powder coating"]
)
],
schema = StructType([StructField("xs", ArrayType(StringType())), StructField("ys", ArrayType(StringType()))])
)
df.select(zip_with("xs", "ys", lambda x, y: concat(x,y)).alias("Array_Elements_Concat")).show(truncate=False)
Output
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|Array_Elements_Concat |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|[Fabric:100 % polyester (100% recycled), PET plastic, Wall bracket:Steel, Polycarbonate/ABS plastic, Powder coating, Top rail/ Bottom rail:Aluminium, Powder coating]|
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
A compass for a textadventure
I am trying to program an ascii compass for a text adventure. I found something like I want to do at this url: https://codereview.stackexchange.com/questions/207997/map-generator-for-a-text-based-role-playing-game I did this modification for me: north = "The Hills" west = "A realy dark room" #west = "A longer text destroy the positions" east = "Entrance to the Dungeon" south = "[X]" n = "N" s = "S" vert_line = "║" hzt_line = " ⯇ W ══ (C) ══ E ⯈ " print(north.center(72, " ")) print(r"▲".rjust(len(west)+10)) print(r""+n.rjust(len(west)+10)) print(r""+vert_line.rjust(len(west)+10)) print(r""+vert_line.rjust(len(west)+10)) print(west.rjust(len(east)) + hzt_line + east) print(r""+vert_line.rjust(len(west)+10)) print(r""+vert_line.rjust(len(west)+10)) print(r""+s.rjust(len(west)+10)) print(r"▼".rjust(len(west)+10)) print(south.center(len(west)+len(hzt_line)+len(east)-5, " ")) The problem is the calculation of the correct whitespace for the labels. It depends on the length of the text and breaks out of the center. I'm also stuck at the calculation for the whitespace .. the result should be like this: This is up ▲ N | To the left ◀ W ── [C] ── E ⯈ Dining Room | S ▼ Kitchen The code looks also really ugly, maybe anyone knows a smarter solution. Thank you very much for help! Rufnex
I ran into a problem playing with your code. For some reason the left arrow graphic messes up the centering. When I changed the character, this worked well: north = "The Hills" west = "A realy dark room" east = "Entrance to the Dungeon" south = "[X]" n = "N" s = "S" vert_line = "║" #hzt_line = " ⯇ W ══ (C) ══ E ⯈ " # for some reason the left arrow graphic messes up the centering hzt_line = " < W == (C) == E > " # synchronize east/west lengths and get line length labelsize= max(len(west),len(east)) westpad = west.rjust(labelsize) eastpad = east.ljust(labelsize) long_line = westpad+hzt_line+eastpad line_length = len(long_line) # print compass print(north.center(line_length)) print(r"▲".center(line_length)) print(n.center(line_length)) print(vert_line.center(line_length)) print(vert_line.center(line_length)) print(long_line.center(line_length)) print(vert_line.center(line_length)) print(vert_line.center(line_length)) print(s.center(line_length)) print(r"▼".center(line_length)) print(south.center(line_length))
Make sentence from value of dictionary
link for original txt file
https://medusa.ugent.be/en/exercises/187053144/description/wM6YaQUbWdHKPhQX/media/ICD.txt
This is what I got:
given_string = 'You are what you eat.'
dictionary ={'D89.1': 'Cryoglobulinemia', 'M87.332': 'Other secondary osteonecrosis of left radius', 'M25.57': 'Pain in ankle and joints of foot', 'H59.111': 'Intraoperative hemorrhage and hematoma of right eye and adnexa complicating an ophthalmic procedure', 'I82.5Z9': 'Chronic embolism and thrombosis of unspecified deep veins of unspecified distal lower extremity', 'T38.3X': 'Poisoning by, adverse effect of and underdosing of insulin and oral hypoglycemic [antidiabetic] drugs', 'H95.52': 'Postprocedural hematoma of ear and mastoid process following other procedure', 'Q90.1': 'Trisomy 21, mosaicism (mitotic nondisjunction)', 'X83.8': 'Intentional self-harm by other specified means', 'H02.145': 'Spastic ectropion of left lower eyelid', 'M67.341': 'Transient synovitis, right hand', 'P07.32': 'Preterm newborn, gestational age 29 completed weeks', 'R44.8': 'Other symptoms and signs involving general sensations and perceptions', 'R03.1': 'Nonspecific low blood-pressure reading', 'Q03': 'Congenital hydrocephalus', 'C11.0': 'Malignant neoplasm of superior wall of nasopharynx', 'C44.4': 'Other and unspecified malignant neoplasm of skin of scalp and neck', 'N48.5': 'Ulcer of penis', 'T50.2X1': 'Poisoning by carbonic-anhydrase inhibitors, benzothiadiazides and other diuretics, accidental (unintentional)', 'V92.13': 'Drowning and submersion due to being thrown overboard by motion of other powered watercraft', 'D30.0': 'Benign neoplasm of kidney', 'M08.06': 'Unspecified juvenile rheumatoid arthritis, knee', 'T41.5X4': 'Poisoning by therapeutic gases, undetermined', 'T59.3X2': 'Toxic effect of lacrimogenic gas, intentional self-harm', 'S84.91': 'Injury of unspecified nerve at lower leg level, right leg', 'Z80.4': 'Family history of malignant neoplasm of genital organs', 'M05.34': 'Rheumatoid heart disease with rheumatoid arthritis of hand', 'Y36.531': 'War operations involving thermal radiation effect of nuclear weapon, civilian', 'H59.88': 'Other intraoperative complications of eye and adnexa, not elsewhere classified', 'R29.91': 'Unspecified symptoms and signs involving the musculoskeletal system', 'M71.139': 'Other infective bursitis, unspecified wrist', 'S00.441': 'External constriction of right ear', 'V04': 'Pedestrian injured in collision with heavy transport vehicle or bus', 'C92.1': 'Chronic myeloid leukemia, BCR/ABL-positive', 'I82.60': 'Acute embolism and thrombosis of unspecified veins of upper extremity', 'I75.89': 'Atheroembolism of other site', 'S51.031': 'Puncture wound without foreign body of right elbow', 'Z01.110': 'Encounter for hearing examination following failed hearing screening', 'I06.8': 'Other rheumatic aortic valve diseases', 'Z68.25': 'Body mass index (BMI) 25.0-25.9, adult', 'A66': 'Yaws', 'S78.921': 'Partial traumatic amputation of right hip and thigh, level unspecified', 'F44': 'Dissociative and conversion disorders', 'O87.8': 'Other venous complications in the puerperium', 'K04.3': 'Abnormal hard tissue formation in pulp', 'V38.7': 'Person on outside of three-wheeled motor vehicle injured in noncollision transport accident in traffic accident', 'V36.1': 'Passenger in three-wheeled motor vehicle injured in collision with other nonmotor vehicle in nontraffic accident', 'B94.9': 'Sequelae of unspecified infectious and parasitic disease', 'K50.911': "Crohn's disease, unspecified, with rectal bleeding", 'S00.52': 'Blister (nonthermal) of lip and oral cavity', 'T43.1': 'Poisoning by, adverse effect of and underdosing of monoamine-oxidase-inhibitor antidepressants', 'B99.8': 'Other infectious disease', 'S97.12': 'Crushing injury of lesser toe(s)', 'S02.69': 'Fracture of mandible of other specified site', 'V29.10': 'Motorcycle passenger injured in collision with unspecified motor vehicles in nontraffic accident', 'Z68.35': 'Body mass index (BMI) 35.0-35.9, adult', 'A81.2': 'Progressive multifocal leukoencephalopathy', 'V44.4': 'Person boarding or alighting a car injured in collision with heavy transport vehicle or bus', 'M62.51': 'Muscle wasting and atrophy, not elsewhere classified, shoulder', 'M62.151': 'Other rupture of muscle (nontraumatic), right thigh', 'V52.2': 'Person on outside of pick-up truck or van injured in collision with two- or three-wheeled motor vehicle in nontraffic accident', 'E09.622': 'Drug or chemical induced diabetes mellitus with other skin ulcer', 'S43.492': 'Other sprain of left shoulder joint', 'M08.212': 'Juvenile rheumatoid arthritis with systemic onset, left shoulder', 'R00.0': 'Tachycardia, unspecified', 'G21.8': 'Other secondary parkinsonism', 'W58.01': 'Bitten by alligator', 'D46.1': 'Refractory anemia with ring sideroblasts', 'H61.32': 'Acquired stenosis of external ear canal secondary to inflammation and infection', 'H95.0': 'Recurrent cholesteatoma of postmastoidectomy cavity', 'Z72.4': 'Inappropriate diet and eating habits', 'Z68.41': 'Body mass index (BMI) 40.0-44.9, adult', 'S20.172': 'Other superficial bite of breast, left breast', 'I63.232': 'Cerebral infarction due to unspecified occlusion or stenosis of left carotid arteries', 'M14.811': 'Arthropathies in other specified diseases classified elsewhere, right shoulder', 'E13.41': 'Other specified diabetes mellitus with diabetic mononeuropathy', 'H02.53': 'Eyelid retraction', 'V95.49': 'Other spacecraft accident injuring occupant', 'D74.0': 'Congenital methemoglobinemia', 'D60.1': 'Transient acquired pure red cell aplasia', 'T52.1X2': 'Toxic effect of benzene, intentional self-harm', 'O71.2': 'Postpartum inversion of uterus', 'M08.439': 'Pauciarticular juvenile rheumatoid arthritis, unspecified wrist', 'M01.X72': 'Direct infection of left ankle and foot in infectious and parasitic diseases classified elsewhere', 'H95.3': 'Accidental puncture and laceration of ear and mastoid process during a procedure', 'C74.92': 'Malignant neoplasm of unspecified part of left adrenal gland', 'G00': 'Bacterial meningitis, not elsewhere classified', 'M19.011': 'Primary osteoarthritis, right shoulder', 'G72.49': 'Other inflammatory and immune myopathies, not elsewhere classified', 'Z68.34': 'Body mass index (BMI) 34.0-34.9, adult', 'V86.64': 'Passenger of military vehicle injured in nontraffic accident', 'L20.9': 'Atopic dermatitis, unspecified', 'S65.51': 'Laceration of blood vessel of other and unspecified finger', 'B67.1': 'Echinococcus granulosus infection of lung', 'S08.81': 'Traumatic amputation of nose', 'Z36.5': 'Encounter for antenatal screening for isoimmunization', 'S59.22': 'Salter-Harris Type II physeal fracture of lower end of radius', 'M66.359': 'Spontaneous rupture of flexor tendons, unspecified thigh', 'I69.919': 'Unspecified symptoms and signs involving cognitive functions following unspecified cerebrovascular disease', 'I25.700': 'Atherosclerosis of coronary artery bypass graft(s), unspecified, with unstable angina pectoris', 'V24.0': 'Motorcycle driver injured in collision with heavy transport vehicle or bus in nontraffic accident', 'S53.025': 'Posterior dislocation of left radial head', 'Q72.819': 'Congenital shortening of unspecified lower limb', 'G44.82': 'Headache associated with sexual activity', 'M93.2': 'Osteochondritis dissecans', 'V44.6': 'Car passenger injured in collision with heavy transport vehicle or bus in traffic accident', 'O90.89': 'Other complications of the puerperium, not elsewhere classified', 'T83.518': 'Infection and inflammatory reaction due to other urinary catheter', 'Z02.9': 'Encounter for administrative examinations, unspecified', 'S55.091': 'Other specified injury of ulnar artery at forearm level, right arm'}
Each character of the string must be replaced by randomly choosing among all possible Hippocrates-codes that encode the character, and return result contain code where character is in, and index of character in value
so. this is the answer that I supposed to get
A66.0 M62.51.29 V44.6.68 H95.3.70 M08.06.26 S51.031.39 V92.13.17 V95.49.25 P07.32.46 C11.0.44 V04.45 E13.41.30 G21.8.5 R00.0.4 V52.2.54 B67.1.38 V24.0.43 M01.X72.10 C74.92.35 G72.49.35 Z68.41.24
and, this is the answer that i got.
F44.6.4 S78.922.3 W36.1.17 S93.121.2 E10.32.39 A00.1.12 S90.464.3 T37.1X.9 T43.2.17 W24.0.3 Q60.3.5 V59.9.14 S66.911.5 W93.42 V14.1.34 Y92.139.14 T21.06.12 T65.89.6 Q95.3.4 S85.161.16 S93.121.7 T37.1X.18 V49.60.23 T37.1X5.7 F98.29.16 J10.89.14
for get that I wrote code like this
import re
import random
class Hippocrates:
def __init__(self, code):
self.code = code
def description(self, x):
line_list = []
split_point = []
k = []
v = []
with open(self.code) as f:
for line in f:
for i in line:
if i == " ":
split_point.append(line.find(i))
with open(self.code) as f:
for line in f:
line_list.append(line.rstrip())
for i in line_list:
a = i.split(" ", 1)
k.append(a[0])
v.append(a[1])
d = dict(zip(k, v))
for key, value in d.items():
if x == key:
return d[key]
else:
raise ValueError('invalid ICD-code')
def character(self, numb):
line_list = []
split_point = []
k = []
v = []
with open(self.code) as f:
for line in f:
for i in line:
if i == " ":
split_point.append(line.find(i))
with open(self.code) as f:
for line in f:
line_list.append(line.rstrip())
for i in line_list:
a = i.split(" ", 1)
k.append(a[0])
v.append(a[1])
d = dict(zip(k, v))
rev = numb[::-1]
revs = rev.split('.',1)
r1 =(revs[1][::-1])
r2 = (revs[0][::-1])
for key, value in d.items():
if r1 == key:
answer = d[key]
result = answer[int(r2)]
return result
else:
raise ValueError('invalid Hippocrates-code')
def codes(self, char):
line_list = []
split_point = []
k = []
v = []
r_v = []
code_result = []
des_result = []
des_result2 = []
location = []
final = []
with open(self.code) as f:
for line in f:
for i in line:
if i == " ":
split_point.append(line.find(i))
with open(self.code) as f:
for line in f:
line_list.append(line.rstrip())
for i in line_list:
a = i.split(" ", 1)
k.append(a[0])
v.append(a[1])
d = dict(zip(k, v))
for i in v:
for x in i:
if x == char:
r_v.append(i)
for key, value in d.items():
for i in r_v:
if i == value:
code_result.append(key)
for key in d.keys():
for i in code_result:
if i == key:
des_result.append(d[i])
for i in des_result:
if i not in des_result2:
des_result2.append(i)
for i in des_result2:
regex = re.escape(char)
a = [m.start() for m in re.finditer(regex,i)]
location.append(a)
location = (sum(location,[]))
for i in range(len(code_result)):
answer = (str(code_result[i]) +'.'+ str(location[i]))
final.append(answer)
return (set(final))
def encode(self, plaintxt):
line_list = []
split_point = []
#key of dictionary
k = []
#value of dictionary
v = []
#description that contain character with index
r = []
#list of possible choice
t = []
#randomly choosen result from t
li_di = []
#descriptoin
des = []
#index of char in description
index_char = []
#answer to print
resul = []
dictlist = []
answers = []
with open(self.code) as f:
for line in f:
for i in line:
if i == " ":
split_point.append(line.find(i))
with open(self.code) as f:
for line in f:
line_list.append(line.rstrip())
for i in line_list:
a = i.split(" ", 1)
k.append(a[0])
v.append(a[1])
d = dict(zip(k, v))
print(d)
for key, value in d.items():
for i in plaintxt:
if i in value:
answer = d[key] +':'+ str(d[key].index(i))
r.append(answer)
print(r)
a = len(plaintxt)
b=0
for i in range(len(r)):
t.append(r[b::a])
b+=1
if b == len(plaintxt):
break
for i in t:
li_di.append(random.choice(i))
for i in li_di:
sep = i.split(":", 1)
des.append(sep[0])
index_char.append(sep[1])
print(index_char)
for i in des:
for key, value in d.items():
if i == value:
resul.append(key)
print(resul)
for i in range(len(resul)):
answers.append(resul[i]+'.'+index_char[i]+'')
return(" ".join(answers))
the codes that represent character in given_string should be in same order with, original given string, but i messed it up. how can i fix this?
This should work for your encode function:
def encode(self, plaintxt):
code_map = {}
codes = []
with open(self.code) as f:
for line in f:
line = line.rstrip().split(' ', 1)
code_map[line[0]] = line[1]
for ch in plaintxt:
matches = []
for key, value in code_map.items():
pos = -1
while True:
pos = value.find(ch, pos + 1)
if pos != -1:
matches.append((key, pos))
else:
break
if not matches:
raise ValueError(f'Character {ch} cannot be encoded as there are no matches')
code_tuple = random.choice(matches)
code, idx = code_tuple
codes.append(f'{code}.{idx}')
return ' '.join(codes)
Edit: I updated this to make it more space-efficient, by getting rid of char_map and appending codes as it goes
First, it creates a dict of keys as codes and values as the corresponding strings. Then it iterates through the given plaintxt string, and searches all of the values of the dict for matches (including multiple matches in a single value), and adds this to a matches list of tuples, where each tuple contains a suitable code and the index of the match. If there are no matches, it raises a ValueError as soon as it runs into an issue. It chooses randomly from each list of tuples to choose some code and index pair, and appends this to a list on the fly, and then at the end it joins this list to make your encoded string.
If memory is not a problem, I think you should build an index of possible choices of each character from the dictionary. Here is an example code:
import random
def build_char_codes(d):
result = {}
for key, val in d.items():
for i in range(len(val)):
ch = val[i]
if ch not in result:
result[ch] = {key: [i]}
else:
result[ch][key] = result[ch].get(key, []) + [i]
return result
def get_code(ch, char_codes):
key = random.sample(char_codes[ch].keys(), 1)[0]
char_pos = random.choice(char_codes[ch][key])
code = '{}.{}'.format(key, char_pos)
return code
char_codes = build_char_codes(dictionary)
given_string = 'You are what you eat.'
codes = [get_code(ch, char_codes) for ch in given_string]
print(' '.join(codes))
Notes:
char_codes index all possible choices of each character in the dictionary
it sample all the key in dictionary first (uniformly random), and then it sample the position in the string (uniformly random). But it is not sampling uniformly among all the possible choices of a character.
In preparation for the transformation, you could create a dictionary with each letter in the ICD description mapping to a list of codes that contain it at various indexes.
Then, the transformation process would simply be a matter of picking one of the code.index from the entry in the dictionary for each letter in the given string:
preparation ...
with open(fileName,'r') as f:
icd = [line.split(" ",1) for line in f.read().split("\n")]
icdLetters = dict() # list of ICD codes with index for each possible letter
for code,description in icd:
for i,letter in enumerate(description):
icdLetters.setdefault(letter,[]).append(f"{code}.{i}")
transformation....
import random
given_string = 'You are what you eat.'
result = [ random.choice(icdLetters.get(c,["-"])) for c in given_string ]
output:
print(result)
['A66.0', 'T80.22.35', 'S53.136.34', 'C40.90.33', 'S53.136.43', 'Z96.621.12', 'B57.30.24', 'H59.121.55', 'V14.1.43', 'S93.121.47', 'H59.121.9', 'V04.92.17', 'T80.22.80', 'O16.1.22', 'T25.61.10', 'S53.136.34', 'F44.6.32', 'M67.232.29', 'M89.771.34', 'S93.121.7', 'Z68.36.29']
If you want to save some memory, your dictionary could store indexes in the main list of icd codes and descriptions instead of the formatted values:
with open(fileName,'r') as f:
icd = [line.split(" ",1) for line in f.read().split("\n")]
icdLetters = dict()
for codeIndex,(code,description) in enumerate(icd):
for letterIndex,letter in enumerate(description):
icdLetters.setdefault(letter,[]).append((codeIndex,letterIndex))
import random
def letterToCode(letter):
if letter not in icdLetters: return "-"
codeIndex,letterIndex = random.choice(icdLetters[letter])
return f"{icd[codeIndex][0]}.{letterIndex}"
given_string = 'You are what you eat.'
result = [ letterToCode(c) for c in given_string ]
osmnx road connecting two city aren't identified (missing node of border)
I have an Dijsktra Algorithm that I apply on a graph that I get from open street map. It works fine with a single graph.
But when I compose a graph of two neighboring city, the same algorithm doesn't find any way between the graphs .. I noticied that road connecting two city weren't identified by any osmid. :-/
Did I missed some thing ?
class Pathfinding(nx.MultiDiGraph):
def __init__(self, incomin_graphe_data = None, **attr):
nx.MultiDiGraph.__init__(self,incomin_graphe_data,**attr)
def dijsktra(self,depart,final):
inf = float("inf")
F ,ds, s= set(), None ,None
D = {i: inf for i in self.nodes}
D[ depart ] = 0
pred = {}
priority_queue = [(0 , depart)]
while s != final:
ds, s = heapq.heappop(priority_queue)
F.add(s)
for y in self.neighbors(s):
w = self[s][y][0]['lenght']
dy = ds + w
if y not in F :
if D[y] > dy :
D[y] = dy
heapq.heappush(priority_queue,(dy,y))
pred[y] = s
path = [s]
while s != depart:
s = pred[s]
path.append(s)
return path
There is the Dijkstra that I use and the following type graph are MultiDiGraph.
add_Aubervilliers = "Aubervilliers, France"
Graphe_Aubervilliers = ox.graph_from_place(add_Aubervilliers, network_type='drive')
add_Saint_denis = " Saint denis, France "
Graphe_Saint_denis = ox.graph_from_place(add_Saint_denis, network_type = "drive")
Graph_Paris_Auber = nx.compose_all([ Graphe_Saint_denis, Graphe_Aubervilliers ])
I also tryied with the following commande
add_Saint_denis = " Saint denis, France "
Graphe_Saint_denis = ox.graph_from_adress(add_Saint_denis, infrastructure='way['highway']')
But it give the same problem... Did I missed something ?
All you need is to set truncate_by_edge to True.