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ML
Fake Data Generation For ML
📅 May 08, 2026
📓
Jupyter Notebook
Generate the data¶
Machine Learning मा model बनाउन data चाहिन्छ — तर सधैँ real data available हुँदैन। Real data महँगो, अधुरो, र collect गर्न गाह्रो हुन्छ। त्यसैले sklearn.datasets ले हामीलाई practice गर्नको लागि data generate गरिदिन्छ — जसमा हामी आफ्नो model test गर्न, algorithm बुझ्न, र experiment गर्न सक्छौं।
💡 जसरी driving सिक्दा पहिले simulator मा practice गर्छन् — त्यसरी नै ML सिक्दा पहिले यी generated data मा practice गर्छौं।
1. make_regression — संख्या अनुमान गर्न¶
from sklearn.datasets import make_regression
X, y = make_regression(n_samples=100, n_features=2, noise=10, random_state=42)
के गर्छ: घर को size, कोठाको संख्या → घरको मूल्य अनुमान जस्तो data बनाउँछ
Output:
y→ continuous number हुन्छ (जस्तै: 50000, 75000, 120000) Use Case:- घरको मूल्य अनुमान गर्न
- temperature predict गर्न
- sales forecast गर्न कुन Model मा प्रयोग:
Linear Regression, Ridge, Lasso
2. make_classification — वर्गीकरण गर्न¶
from sklearn.datasets import make_classification
X, y = make_classification(n_samples=100, n_features=2, n_classes=2, random_state=42)
के गर्छ: Email spam हो कि होइन, बिरामी छ कि छैन — यस्तो हो/होइन type को data बनाउँछ
Output:
y→ 0 या 1 हुन्छ (class label) Use Case:- Spam detection
- Disease prediction (cancer छ/छैन)
- Fraud detection कुन Model मा प्रयोग:
Logistic Regression, SVM, Decision Tree, Random Forest
3. make_blobs — Cluster बनाउन¶
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=100, centers=3, random_state=42)
के गर्छ: अलग-अलग समूह (blobs) मा data points बनाउँछ — जसरी map मा शहरहरू छुट्टिएका हुन्छन्
Output:
y→ कुन cluster मा छ (0, 1, 2...) Use Case:- Customer grouping (segmentation)
- Document clustering
- Image grouping कुन Model मा प्रयोग:
K-Means, DBSCAN, Gaussian Mixture
4. make_circles — गोलो shape को data¶
from sklearn.datasets import make_circles
X, y = make_circles(n_samples=100, noise=0.1, random_state=42)
के गर्छ: एउटा सानो गोलो को वरिपरि ठूलो गोलो को data बनाउँछ
. . . . .
. . ← ठूलो circle (class 0)
. * * * * .
. * * . ← सानो circle (class 1)
. * * * * .
. .
. . . . .
Use Case:
- Simple linear model काम नगर्ने ठाउँमा test गर्न
- Complex boundary खोज्न
- Neural Network test गर्न कुन Model मा प्रयोग:
SVM (RBF kernel), Neural Network, KNN
5. make_moons — अर्धचन्द्र shape को data¶
from sklearn.datasets import make_moons
X, y = make_moons(n_samples=100, noise=0.1, random_state=42)
के गर्छ: दुईवटा अर्धचन्द्र (half moon) आकारको data बनाउँछ जुन एकअर्कामा घुसेको हुन्छ
* * * * ← माथिको moon (class 0)
* * *
. .
. . ← तलको moon (class 1)
. .
Use Case:
- Non-linear boundary test गर्न
- Complex pattern पहिचान गर्न
- Simple model vs Advanced model compare गर्न कुन Model मा प्रयोग:
SVM, Neural Network, Random Forest
सबैको तुलना एकैठाउँ¶
| Function | Data Shape | Output (y) | मुख्य Use |
|---|---|---|---|
make_regression |
Random scattered | Number | मूल्य अनुमान |
make_classification |
Scattered clusters | 0 या 1 | Spam/Disease detect |
make_blobs |
गोल-गोल समूह | 0,1,2... | Customer grouping |
make_circles |
गोलो भित्र गोलो | 0 या 1 | Complex boundary test |
make_moons |
दुई अर्धचन्द्र | 0 या 1 | Non-linear test |
सबैको Common Parameters¶
n_samples → कतिवटा data points बनाउने (default: 100)
n_features → कतिवटा columns/features (default: 2)
noise → कति गडबडी थप्ने (0 = clean, 1 = धेरै noisy)
random_state → हरपटक उही data आउन fix गर्ने
कहिले कुन प्रयोग गर्ने?¶
Number predict गर्नु छ? → make_regression
हो/होइन classify गर्नु छ? → make_classification
Group/Cluster खोज्नु छ? → make_blobs
Simple model काम गर्दैन test? → make_circles / make_moons
सार¶
यी सबै functions real data नभएको बेला model practice गर्न,
नयाँ algorithm test गर्न, र model compare गर्न प्रयोग हुन्छन्।
Real project मा real data आउँछ — यिनीहरू learning र testing को लागि मात्र हुन्।
In [2]:
import matplotlib.pyplot as plt
In [2]:
from sklearn.datasets import make_regression
In [3]:
x,y = make_regression(n_features=1, noise=5, n_samples=10000,random_state=0)
In [4]:
x
Out[4]:
array([[ 0.58262411],
[-1.31093021],
[-0.76251627],
...,
[-0.22425893],
[ 0.17581895],
[-0.93942432]])
In [5]:
y
Out[5]:
array([ 7.01315442, -17.41605545, 1.1459828 , ..., -14.37795447,
-4.81283115, -12.89225575])
In [6]:
import matplotlib.pyplot as plt
plt.scatter(x,y)
plt.xlabel('x')
plt.ylabel('y')
plt.show()
In [ ]:
In [7]:
from sklearn.datasets import make_classification
In [8]:
x,y = make_classification(n_classes=2,n_features=5,n_samples=10000)
plt.scatter(x[:,0],x[:,1],c=y)
Out[8]:
<matplotlib.collections.PathCollection at 0x7fa652a71a00>
In [9]:
x
Out[9]:
array([[ 0.72167542, -0.76130253, 0.03902031, -0.24178992, -1.607381 ],
[ 2.49781801, 0.70250644, -1.04755798, 2.14788211, -0.56443988],
[ 1.78149664, 1.05768259, -0.94438208, 2.02972533, -1.73459734],
...,
[-1.24162706, -1.40115535, 0.89347626, -2.00845172, 0.91755289],
[ 0.79349862, 0.40009425, -0.39547668, 0.8405578 , 0.79506111],
[-0.68202973, 0.1560639 , 0.16276585, -0.27536308, 0.36624737]])
In [10]:
y
Out[10]:
array([0, 1, 1, ..., 0, 1, 0])
In [ ]:
In [12]:
from sklearn.datasets import make_blobs
In [13]:
x, y = make_blobs(n_samples=500, centers=3, n_features=2,random_state=0)
In [14]:
plt.scatter(x[:,0],x[:,1],c=y)
Out[14]:
<matplotlib.collections.PathCollection at 0x7fa5e0119130>
In [15]:
x
Out[15]:
array([[ 1.10318217, 4.70577669],
[-1.93284574, 3.64225077],
[-2.03442161, 1.86600216],
[ 1.6164016 , 2.68683128],
[-0.96000973, 4.49256592],
[ 0.94808785, 4.7321192 ],
[ 0.33265168, 2.08038418],
[-1.32893672, 3.99981748],
[-1.29885069, 3.11936221],
[ 1.72345841, 3.11484237],
[-2.42430494, 4.23035263],
[-1.54176172, 3.7392882 ],
[-2.3858764 , 2.0189401 ],
[-0.44552798, 2.28650627],
[-0.33963733, 3.84220272],
[ 0.85810603, 3.62360912],
[ 2.99831361, -1.84201351],
[ 3.13950603, 5.64031528],
[-1.02885161, 2.89169002],
[ 0.91464368, 4.19648205],
[ 2.39417165, 2.91870722],
[ 1.02533224, 0.5477203 ],
[ 1.96933675, 1.0919566 ],
[-2.31557327, 4.01252064],
[ 1.35269561, 3.20438654],
[-0.96289548, 1.63332996],
[ 1.14650428, 0.47334604],
[-0.5859864 , 3.32308634],
[ 0.10547293, 3.72493766],
[-0.99796039, 3.16403005],
[-0.80692028, 1.81497605],
[ 2.20656076, 5.50616718],
[ 0.06344785, 5.42080362],
[-1.68290216, 4.91010685],
[ 1.6706221 , 0.45382757],
[-0.03933555, 1.02138557],
[ 2.03958541, 1.05859183],
[ 3.15161437, 0.66344786],
[ 1.50095789, 0.427026 ],
[ 0.05105181, 1.27454018],
[-2.31919068, 2.38627635],
[ 1.40252881, 4.98069536],
[ 3.35941485, 5.24826681],
[ 3.15555186, 2.19568563],
[ 3.23664612, 0.2657599 ],
[ 0.47823763, 6.23331938],
[ 1.69057398, 1.05436752],
[ 1.66608824, 5.60563356],
[-1.72530291, 3.01207456],
[-2.70453298, 1.77768596],
[ 0.4666179 , 3.86571303],
[ 1.83310069, 3.65276173],
[ 0.340424 , 4.98022062],
[ 0.59139027, 4.39813892],
[-2.19461673, 4.599804 ],
[-2.04848369, 1.73319567],
[ 0.72305587, -1.07096103],
[ 1.36796848, 0.78018911],
[ 1.42013331, 4.63746165],
[ 0.58988701, 1.30700386],
[ 3.5685956 , 1.45079572],
[-0.12555918, 2.50750047],
[ 1.39671455, 0.38342969],
[ 1.06975678, -0.57417135],
[ 2.33954719, 2.64033244],
[-3.16986931, 2.51181047],
[ 1.37531642, 1.53119457],
[ 1.78753398, -0.23034767],
[ 0.06586674, 2.65930963],
[ 0.52073758, 4.32126649],
[-1.17408741, 2.76510784],
[ 0.08080352, 4.69068983],
[ 0.11504439, 6.21385228],
[-2.18483011, 3.8867649 ],
[ 4.75149157, 0.82373899],
[-1.23017084, 3.74786842],
[ 2.1047625 , 1.39150044],
[ 1.53774848, -0.0811662 ],
[ 1.13726275, 1.15016029],
[ 3.23557903, 4.26153018],
[ 2.07592967, 4.95905106],
[ 0.84032038, 5.44067869],
[-1.94921884, 3.02259629],
[ 0.34818252, 3.82276021],
[-3.15932732, 2.85074811],
[ 2.33570923, -0.09545995],
[-2.3300454 , 2.45354457],
[-1.29951124, 1.90114361],
[-0.51506158, 2.25993122],
[-2.37948986, 2.94084202],
[ 0.17286041, 3.61423755],
[ 1.33263648, 5.0103605 ],
[ 3.78798871, 1.58216477],
[-0.56452694, 4.36704932],
[-0.37271998, 3.09038668],
[ 2.93110028, 0.78255619],
[-1.48713728, 1.35088755],
[-0.11573195, 3.70368609],
[-0.15591501, 2.40803902],
[ 0.03182382, 3.89373763],
[ 3.7941402 , 1.89205805],
[-2.28477487, 2.86779817],
[-3.12874006, 2.03070132],
[ 1.0220286 , 4.11660348],
[ 0.29225918, 5.96333812],
[ 0.03886089, 0.35820903],
[ 1.1312175 , 4.68194985],
[ 3.52184624, 1.7502156 ],
[ 1.66780883, 4.99853647],
[ 1.79362208, 0.71541888],
[ 0.4334086 , 4.71983737],
[ 0.57682105, 4.67384322],
[ 0.77557835, 0.31223246],
[-0.14164247, 2.61478401],
[-1.83888012, 2.07759187],
[ 1.36742991, -0.31641374],
[-1.47532907, 3.78515889],
[ 2.6225578 , 0.67498856],
[ 1.58523464, 0.68093219],
[-0.91497682, 1.57638554],
[-1.87315346, 2.12324594],
[ 3.2460247 , 2.84942165],
[-0.03754805, 3.43918601],
[-2.53373577, 4.59945898],
[-2.02592065, 2.93923349],
[ 1.37861172, 3.61897724],
[-1.49630219, 2.84824068],
[ 1.5001611 , 4.39220941],
[-0.20044237, 1.95327584],
[ 0.70826671, 5.10624372],
[ 1.92516057, 0.99161689],
[ 1.76259804, 3.83736823],
[-2.17408545, 3.39012941],
[ 1.86461403, 0.50281415],
[-0.21258918, 3.79697097],
[ 2.73172825, 0.5156547 ],
[-2.37522454, 2.59221279],
[-2.46478424, 3.35099021],
[ 1.37724174, 2.19550945],
[ 0.87781755, 3.64030904],
[ 2.46452227, 6.1996765 ],
[-0.55436822, 5.05175051],
[-2.44601746, 3.11063611],
[ 2.69858199, -0.67295975],
[ 1.60150153, 2.70172967],
[ 0.06897171, 4.35573272],
[ 0.28170222, 4.15415279],
[ 2.34217201, -1.42293062],
[ 1.92421351, 2.03074354],
[ 0.81860306, 6.56051082],
[ 2.00956356, 1.11817132],
[ 0.5626511 , 3.55633252],
[-0.33025677, 5.96191801],
[ 1.88644899, 4.62100554],
[ 2.91319145, 2.03876553],
[ 0.86572942, 5.32396004],
[-0.20188996, 3.12321483],
[ 0.60873282, 1.69796161],
[-0.59045829, 2.88278708],
[ 0.93698726, 3.13569383],
[ 2.43799995, 0.86342138],
[ 0.87610959, 2.19909173],
[-1.91377486, 2.40758952],
[-1.45231761, 1.84078319],
[-1.53804963, 2.92938116],
[ 2.92423101, 1.17453557],
[ 0.30380963, 3.94423417],
[ 2.59857941, 1.33670662],
[-0.31658683, 4.5708382 ],
[ 2.40704763, 1.27689919],
[-1.16242276, 4.38920422],
[-2.46588559, 3.93490325],
[ 2.89689879, 0.64820508],
[ 1.47001186, 4.18768339],
[ 1.74806063, 5.12729148],
[-0.77150832, 3.78528967],
[-1.64167934, 3.2266335 ],
[ 1.83100859, 0.59541393],
[ 1.57561171, 1.51802196],
[ 1.50955555, -0.98692219],
[ 2.42609252, 1.03972547],
[ 1.61434489, 0.61730816],
[-1.58225656, 2.65394491],
[-1.89195923, 1.12655471],
[ 1.32622286, 1.09422106],
[ 1.96488551, 2.2652609 ],
[ 0.95924966, 4.68293906],
[ 2.95052779, 2.27262773],
[ 0.08848433, 2.32299086],
[-2.29260621, 3.47366923],
[-1.58549056, 2.60033917],
[ 0.02032508, 3.95780555],
[ 1.60146348, 1.3009282 ],
[ 3.28013809, 0.96251977],
[ 0.87305123, 4.71438583],
[-2.45969305, 4.16120165],
[ 1.98799191, -0.42073221],
[-0.17119857, 3.86596728],
[-1.01966506, 2.80165256],
[ 1.58842198, -0.51924245],
[-1.25130334, 4.35293165],
[ 3.3744044 , 0.01524484],
[ 1.74952306, 3.11990669],
[-0.06898329, 5.51493262],
[-2.32632641, 3.15866977],
[-0.20029671, -0.12484318],
[ 0.7073421 , 0.13609027],
[-0.3089063 , 0.91799784],
[-3.21513404, 2.80541628],
[-2.36458206, 2.32669916],
[ 1.5528609 , 4.09548857],
[ 0.31291132, -0.40557909],
[ 0.37404931, 0.0086923 ],
[-1.58437353, 2.52666521],
[-0.59649552, 2.74256586],
[ 1.67004323, 4.14421389],
[-2.34439711, 1.51653497],
[ 2.89921211, 5.78430212],
[-2.28901853, 2.03010212],
[-0.85633356, 2.21037656],
[ 2.25456772, 0.84706012],
[-1.48177 , 5.25750707],
[ 1.81324769, 2.41592483],
[ 1.66287852, -2.14847939],
[ 2.84382904, 5.20983199],
[ 2.12860164, 5.38340592],
[ 0.62835793, 4.4601363 ],
[ 3.08420301, 0.82537665],
[-1.60157199, 3.1765987 ],
[ 0.28422023, 5.84016438],
[-0.18108518, 3.99149508],
[-2.67451496, 2.55976819],
[ 0.22808214, 2.78489384],
[ 2.09389807, -0.75905144],
[-1.51467203, 4.48714188],
[-1.28732125, 2.5480811 ],
[ 1.37227679, 3.21072582],
[ 1.70183577, -0.71881053],
[ 1.49733495, 3.72799936],
[ 1.84070628, 3.56162231],
[-0.19685333, 6.24740851],
[ 2.06389542, 1.42466787],
[ 1.40297392, 0.37647435],
[ 1.83266184, -0.01541556],
[ 1.92352205, 4.14877723],
[-2.75352567, 3.88532841],
[-1.12048852, 2.72470556],
[-1.05647087, 3.22932933],
[-1.50584199, 3.01733672],
[-1.74938102, 2.05896235],
[ 1.60710216, 0.24832573],
[-2.23182537, 3.59785711],
[-0.80380352, 2.94249439],
[ 0.33883305, 3.90651551],
[-0.71422997, 3.50514164],
[-2.47439261, 3.16232572],
[-0.29421492, 5.27318404],
[ 2.51268313, -0.06694835],
[-2.05939393, 3.56293753],
[ 0.28672907, 1.25314545],
[ 2.51839785, 1.17675942],
[ 0.23151526, 3.47734879],
[ 0.34194798, 3.94104616],
[ 1.92335888, 1.30242547],
[ 1.48595547, 1.16756801],
[ 0.37895401, 4.0658656 ],
[-0.26182618, 3.12937927],
[ 3.70340245, 1.06189142],
[-1.21857277, 1.53979879],
[ 2.75372467, 0.90143455],
[ 2.03184442, 1.97685839],
[-2.97184421, 1.70733927],
[ 2.64514734, 0.53380485],
[ 1.11822324, 3.98445891],
[-0.35798839, 2.95706982],
[ 2.8219307 , 1.25395648],
[ 2.15504965, 4.12386249],
[ 2.9871159 , 1.23762864],
[ 0.50483818, 1.31498248],
[ 1.03722565, 0.8198089 ],
[ 0.10956444, -0.01511983],
[-3.02930058, 1.14021531],
[ 3.57526238, 2.61725297],
[ 0.60708824, 4.06440815],
[ 2.102906 , 3.22385582],
[ 0.90802847, 6.01713005],
[-0.60666832, 4.91416671],
[-0.82752353, 1.85665997],
[-1.62860129, 2.93716165],
[-0.23335252, 1.14914807],
[ 3.08967741, -0.09854898],
[-1.95156732, 2.08791766],
[-1.05000564, 3.06633184],
[ 0.9867701 , 6.08965782],
[ 0.2506727 , 2.92042337],
[ 2.63378902, 1.24731812],
[-2.27659436, 2.95069101],
[ 1.70536064, 4.43277024],
[-1.08587111, 3.09667513],
[ 1.84836385, 1.77784257],
[ 2.93218239, 4.69388065],
[ 2.52143395, 0.52742122],
[-0.59050547, 2.39224167],
[ 3.34125153, -0.60933474],
[-0.12811326, 4.35595241],
[-1.15935912, 3.10819382],
[-0.24916544, 5.1481503 ],
[ 1.74615308, 0.664197 ],
[ 1.59034945, 5.225994 ],
[-1.30132235, 4.30702758],
[ 0.83732901, 0.59270002],
[-0.77116372, 2.37874962],
[-1.56267209, 5.29862761],
[ 2.50904929, 5.7731461 ],
[-0.44779083, 3.81046744],
[ 1.7373078 , 4.42546234],
[ 2.27911108, 1.22728664],
[-2.05960681, 4.008632 ],
[-1.80333686, 2.65830528],
[ 0.25666569, 3.49079434],
[ 0.95194395, 3.56575642],
[ 1.68456352, -0.04795214],
[ 0.48715614, 2.61111649],
[ 1.45661358, -0.21823333],
[ 1.12031365, 5.75806083],
[-0.73000011, 6.25456272],
[ 1.55245082, 3.31011734],
[ 2.86978734, 0.95658925],
[ 0.3238615 , 3.91283395],
[-2.03226233, 2.10209072],
[ 2.99200998, 2.30999137],
[ 2.85942078, 2.95602827],
[ 1.12511102, 0.71907457],
[-0.66338435, 2.11312852],
[ 1.76343016, 0.13617145],
[ 0.21219797, 0.41968966],
[-0.17991235, 5.08498543],
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[-0.07228289, 2.88376939],
[-0.97094133, 3.81035615],
[ 1.08416295, 1.21248086],
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[ 2.98477263, 1.47988825],
[ 1.44193252, 2.76754364],
[ 1.07399505, 4.88674101],
[ 1.25619468, 4.20563694],
[ 1.00063906, 1.7179115 ],
[ 0.30193742, 4.33561789],
[-0.63762777, 4.09104705],
[-2.88668474, 2.87648145],
[ 1.616078 , 1.07900209],
[ 0.23670708, 5.84680192],
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[ 2.47075462, 2.2338023 ],
[ 3.18386159, 1.39366461],
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[ 2.29738548, 0.0089434 ],
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[ 0.5126741 , 4.7852688 ],
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[ 0.81974302, 1.63872115],
[ 2.16429987, 4.62072994],
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[ 1.92569089, 4.39133857],
[-1.46700933, 2.70535922],
[-2.94882388, 4.91583834],
[ 2.47034915, 4.09862906],
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[-0.10392052, 3.31396885],
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[ 1.83572649, -0.18637296],
[ 1.6801204 , -0.32853253],
[-1.6312475 , 1.92988032],
[ 2.09227324, 1.66556607],
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[ 2.85583232, 0.97592384],
[ 0.54111653, 6.15305106],
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[-1.5165547 , 3.63791602],
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[ 1.28933778, 3.44969159],
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[ 1.87021385, 0.09001517],
[-0.49646575, 0.87055865],
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[ 2.11567076, 3.06896151],
[ 3.12377692, 0.44427786],
[-4.10970065, 1.7639319 ],
[ 2.21919609, 0.99398502],
[-0.19912132, 2.81660078],
[-0.05797276, 4.98538185],
[-0.7796205 , 4.75472179],
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[ 1.66003854, -0.26175686],
[ 0.97029638, 0.67256061],
[ 0.2715698 , 5.24704805],
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[ 2.23860672, 2.56860669],
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[ 0.57309313, 5.5262324 ],
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[ 1.8971289 , 4.62251498],
[ 0.422633 , -0.67010406],
[-2.38345332, 1.37629486],
[ 1.58640333, -1.30377763],
[-0.31431938, 1.76171596],
[-1.05441439, 6.36828019],
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[ 2.99773564, 0.63006891],
[-1.19632726, 3.86712873],
[ 2.41002521, 1.51455021],
[-1.87486587, 1.5644934 ],
[ 2.17041539, 0.5185161 ],
[ 1.14294357, 4.93881876],
[-1.25573383, 2.11638538],
[ 4.0665242 , 0.85306823],
[ 2.25033722, -0.8838992 ],
[ 1.45460996, 2.44990684],
[ 0.91546507, 1.68262118],
[ 1.83831795, 1.34305691],
[ 0.46546494, 3.12315514],
[-1.05851878, 4.65376126],
[ 2.87685323, 0.90295631],
[-1.34297852, 2.5323925 ],
[ 0.93409863, 4.01690014],
[ 1.18454506, 5.28042636],
[ 1.09476314, 0.1045463 ],
[-1.07870873, 4.61406383],
[-2.9886367 , 2.23444249],
[-0.02394527, 2.75901623],
[-1.67048352, 2.53585681],
[ 3.13346483, -1.66152101],
[ 0.66725711, 2.62778352],
[-0.27652528, 5.08127768],
[ 2.37242815, 1.41770427],
[ 0.64930461, 0.30760601],
[ 0.32268723, 2.70371561],
[ 1.9991345 , 0.89627862],
[-2.11830668, 4.04230145],
[ 2.26874757, -0.31090999],
[ 1.77959699, 0.18793569],
[-0.18433654, 1.29916272],
[ 2.50904943, -0.93207675],
[-0.56621061, 4.24694511],
[ 0.58894326, 4.00148458],
[ 1.24964101, -0.22064826],
[ 0.10346342, 0.23777193],
[-0.51498751, 4.74317903],
[ 1.27263837, 0.78727436],
[-0.99785877, 3.34051088],
[-1.16105523, 4.21570753],
[ 1.9263585 , 4.15243012],
[ 1.3952112 , 1.07348261],
[ 1.11089903, 1.13576681],
[-1.93320714, 2.05383727],
[ 1.49954674, 4.132241 ],
[-2.37863321, 4.74060586],
[ 0.66471755, 4.35995267],
[ 3.28018678, 3.2437715 ],
[ 2.66038761, 1.79321965],
[ 2.87558932, 2.2576122 ],
[ 2.82667347, 1.92710249],
[-0.09448254, 5.35823905],
[ 2.50775661, 0.99555981]])
In [16]:
y
Out[16]:
array([0, 2, 2, 0, 0, 0, 0, 2, 2, 0, 2, 2, 2, 2, 0, 0, 1, 0, 2, 0, 1, 1,
1, 2, 0, 2, 1, 2, 0, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 0, 0, 1,
1, 0, 1, 0, 2, 2, 0, 0, 0, 0, 2, 2, 1, 1, 0, 2, 1, 2, 1, 1, 1, 2,
0, 1, 2, 0, 2, 0, 0, 2, 1, 2, 1, 1, 1, 0, 0, 0, 2, 0, 2, 1, 2, 2,
2, 2, 0, 0, 1, 0, 2, 1, 2, 2, 2, 0, 1, 2, 2, 0, 0, 1, 0, 1, 0, 1,
0, 0, 1, 2, 2, 1, 2, 1, 1, 2, 2, 0, 2, 2, 2, 0, 2, 0, 2, 0, 1, 0,
2, 1, 0, 1, 2, 2, 1, 0, 0, 2, 2, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,
1, 0, 2, 1, 2, 0, 1, 1, 2, 2, 2, 1, 0, 1, 0, 1, 2, 2, 1, 0, 0, 2,
2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 0, 1, 0, 2, 2, 0, 1, 1, 0, 2, 1, 0,
2, 1, 2, 1, 0, 0, 2, 1, 1, 1, 2, 2, 0, 1, 1, 2, 2, 0, 2, 0, 2, 2,
1, 2, 1, 1, 0, 0, 0, 1, 2, 0, 0, 2, 2, 1, 2, 2, 0, 1, 0, 0, 0, 1,
1, 1, 0, 2, 2, 2, 2, 2, 1, 2, 2, 0, 2, 2, 0, 1, 2, 1, 1, 0, 0, 1,
1, 0, 2, 1, 2, 1, 1, 2, 1, 0, 0, 1, 0, 1, 1, 1, 1, 2, 1, 0, 0, 0,
0, 2, 2, 1, 1, 2, 2, 0, 0, 1, 2, 0, 2, 1, 0, 1, 2, 1, 0, 2, 0, 1,
0, 2, 1, 2, 2, 0, 0, 0, 1, 2, 2, 0, 0, 1, 2, 1, 0, 0, 1, 1, 0, 2,
1, 0, 1, 2, 1, 1, 0, 2, 0, 2, 1, 2, 1, 0, 0, 0, 1, 0, 0, 2, 1, 0,
2, 2, 2, 0, 1, 1, 1, 2, 0, 1, 2, 0, 0, 0, 2, 0, 2, 0, 2, 2, 0, 2,
2, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 0, 0, 0, 1, 2, 0, 1, 0, 1, 0, 1,
2, 2, 0, 2, 1, 0, 1, 2, 2, 0, 1, 2, 1, 2, 0, 0, 0, 1, 2, 0, 0, 1,
2, 2, 0, 2, 1, 0, 2, 0, 1, 0, 2, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 2,
1, 1, 0, 2, 1, 2, 1, 2, 1, 0, 2, 1, 1, 1, 1, 1, 0, 2, 1, 2, 0, 0,
1, 2, 2, 0, 2, 1, 0, 0, 1, 1, 2, 1, 2, 1, 1, 1, 1, 2, 0, 1, 1, 0,
1, 2, 2, 0, 1, 1, 2, 0, 2, 0, 0, 1, 1, 1, 0, 1])
In [9]:
from sklearn.datasets import make_circles
x, y = make_circles(n_samples=500,random_state=0,noise=0.03)
In [10]:
plt.scatter(x[:,0],x[:,1],c=y)
Out[10]:
<matplotlib.collections.PathCollection at 0x7fbc888cdf10>
In [19]:
x
Out[19]:
array([[-6.37423990e-01, 7.70513243e-01],
[ 7.95960814e-01, 8.02893719e-02],
[ 5.40266246e-01, 5.90010494e-01],
[ 1.49905052e-01, -7.85829801e-01],
[ 4.45500493e-01, -6.64476719e-01],
[ 9.29776486e-01, 3.68124553e-01],
[-7.02809572e-02, 7.96906887e-01],
[ 7.69622137e-01, -2.18361548e-01],
[-6.56585756e-01, -7.54251381e-01],
[-7.62442511e-01, -6.47055962e-01],
[ 9.74526873e-01, -2.24270761e-01],
[ 8.00000000e-01, 0.00000000e+00],
[-7.43821189e-01, -2.94499642e-01],
[ 4.45500493e-01, 6.64476719e-01],
[-4.02906436e-01, -9.15241173e-01],
[-3.22325149e-01, 7.32192938e-01],
[ 5.54922645e-01, -5.76247220e-01],
[-7.01045344e-01, 3.85402939e-01],
[-7.45941145e-01, -6.66011867e-01],
[ 5.97904983e-01, 8.01566985e-01],
[ 4.25779292e-01, -9.04827052e-01],
[-7.79621498e-01, 1.79416609e-01],
[ 9.68583161e-01, -2.48689887e-01],
[-9.10105971e-01, 4.14375581e-01],
[ 4.25779292e-01, 9.04827052e-01],
[ 1.00528319e-02, -7.99936835e-01],
[ 8.63923417e-01, 5.03623202e-01],
[-7.83884042e-01, 1.59767984e-01],
[-7.45941145e-01, 6.66011867e-01],
[ 9.10105971e-01, -4.14375581e-01],
[-1.89199198e-01, -7.77305386e-01],
[-9.84564335e-01, 1.75023059e-01],
[-9.99684189e-01, 2.51300954e-02],
[ 8.78511966e-02, -9.96133609e-01],
[-7.99747351e-01, 2.01040764e-02],
[ 7.83884042e-01, -1.59767984e-01],
[ 3.01521461e-02, 7.99431578e-01],
[ 2.66255636e-01, -7.54392429e-01],
[-6.80795585e-01, 4.20139704e-01],
[ 9.54864545e-01, -2.97041582e-01],
[-1.89199198e-01, 7.77305386e-01],
[ 4.02906436e-01, 9.15241173e-01],
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[ 2.36498997e-01, -9.71631733e-01],
[ 9.84564335e-01, -1.75023059e-01],
[ 4.94287690e-01, -6.29030746e-01],
[-9.68583161e-01, 2.48689887e-01],
[-5.96752916e-01, -5.32809494e-01],
[ 7.43821189e-01, 2.94499642e-01],
[-2.36498997e-01, 9.71631733e-01],
[-3.94181873e-01, 6.96147004e-01],
[ 8.09016994e-01, 5.87785252e-01],
[-5.35826795e-01, -8.44327926e-01],
[-4.62058163e-01, 6.53071401e-01],
[ 7.28084777e-01, -3.31500465e-01],
[-3.32819545e-01, -9.42990536e-01],
[ 7.50987086e-01, -2.75714339e-01],
[ 5.56875616e-01, 8.30595899e-01],
[ 1.37790291e-01, -9.90461426e-01],
[-3.01521461e-02, 7.99431578e-01],
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[ 6.09954009e-01, -5.17644769e-01],
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[ 3.94181873e-01, 6.96147004e-01],
[ 9.79855052e-01, -1.99709981e-01],
[ 6.22769841e-01, 5.02153089e-01],
[ 5.56875616e-01, -8.30595899e-01],
[ 9.74526873e-01, 2.24270761e-01],
[-7.36185478e-01, 3.13098933e-01],
[ 1.87381315e-01, -9.82287251e-01],
[ 7.99747351e-01, 2.01040764e-02],
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[ 1.12856385e-01, -9.93611311e-01],
[-9.79855052e-01, 1.99709981e-01],
[ 3.79779096e-01, 9.25077207e-01],
[-3.79779096e-01, -9.25077207e-01]])
In [20]:
y
Out[20]:
array([0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1,
0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0,
0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0,
0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0,
1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0,
1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0,
1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1,
0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0,
1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1,
0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0,
1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,
1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0,
0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,
1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1,
0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,
0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1,
0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1,
0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1,
1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0,
1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0])
In [17]:
from sklearn.datasets import make_moons
x, y = make_moons(n_samples=500,random_state=10,noise=0.1)
In [18]:
plt.scatter(x[:,0],x[:,1],c=y)
Out[18]:
<matplotlib.collections.PathCollection at 0x7fbc7ad44580>
In [23]:
x
Out[23]:
array([[ 4.21665133e-01, 9.06751628e-01],
[ 1.27320653e-03, 4.49554068e-01],
[ 8.54311058e-02, 9.55698604e-02],
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[ 9.58190308e-01, 2.86131674e-01],
[ 8.56408255e-01, 5.16299236e-01],
[ 1.74398847e+00, -1.68192460e-01],
[ 1.00000000e+00, 0.00000000e+00],
[-8.22156012e-01, 5.69262235e-01],
[ 9.18082449e-01, -4.96639110e-01],
[ 1.60500103e+00, -2.96224691e-01],
[ 3.28151650e-01, 9.44625055e-01],
[ 6.83792333e-01, -4.48689998e-01],
[ 2.23251195e-01, -1.29810522e-01],
[ 1.98856059e+00, 3.49175703e-01],
[-6.24897966e-01, 7.80706432e-01],
[ 9.30663069e-01, -4.97593299e-01],
[ 2.86395472e-03, 4.24371254e-01],
[ 3.75386336e-01, 9.26868437e-01],
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[-5.32413558e-01, 8.46484379e-01],
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[-8.36257331e-01, 5.48337192e-01],
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[ 5.22009862e-01, -3.78365202e-01],
[-7.92392274e-01, 6.10011873e-01],
[-9.97136045e-01, 7.56287459e-02],
[ 1.90407356e+00, 7.26231256e-02],
[ 1.19573174e-01, 9.92825391e-01],
[ 1.36366262e+00, -4.31530729e-01],
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[ 6.14998444e-01, 7.88528322e-01],
[ 5.00000000e-01, -3.66025404e-01],
[ 9.50665878e-01, 3.10216681e-01],
[-4.89033992e-01, 8.72264727e-01],
[ 5.44324062e-01, -3.90145740e-01],
[ 1.97969347e+00, 2.99498874e-01],
[-5.84718878e-01, 8.11235991e-01],
[ 1.98657903e+00, 3.36715529e-01],
[ 8.81363066e-01, 4.72439568e-01],
[-7.60610777e-01, 6.49208168e-01],
[ 3.08676842e-01, -2.22545702e-01],
[ 1.26794752e+00, -4.63433510e-01],
[ 4.44409144e-01, 8.95823930e-01],
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[ 1.37146017e-01, -5.45326586e-03],
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[ 5.74638455e-02, 1.65895829e-01],
[-8.87253453e-01, 4.61282246e-01],
[ 9.93559948e-01, 1.13307678e-01],
[ 8.05683139e-01, -4.80938814e-01],
[-7.76748805e-01, 6.29810522e-01],
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[ 3.94998975e-01, -2.96224691e-01],
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[ 8.29272674e-01, 5.58844193e-01],
[-5.64064438e-01, 8.25730773e-01]])
In [24]:
y
Out[24]:
array([0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1,
0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0,
0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0,
0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0,
1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0,
1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0,
1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1,
0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0,
1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1,
0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0,
1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,
1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0,
0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,
1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1,
0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,
0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1,
0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1,
0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1,
1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0,
1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0])
In [3]:
from sklearn.datasets import make_classification
x,y = make_classification(n_classes=2,n_features=5,n_samples=10000)
plt.scatter(x[:,0],x[:,1],c=y)
Out[3]:
<matplotlib.collections.PathCollection at 0x7fd0989af970>
In [4]:
plt.scatter(x[:,0],x[:,2],c=y)
Out[4]:
<matplotlib.collections.PathCollection at 0x7fd0b95518b0>
In [5]:
plt.scatter(x[:,0],x[:,3],c=y)
Out[5]:
<matplotlib.collections.PathCollection at 0x7fd0aa6abcd0>
In [6]:
plt.scatter(x[:,0],x[:,4],c=y)
Out[6]:
<matplotlib.collections.PathCollection at 0x7fd0a9844d90>
--- end -----