EntilZha

$ cargo build
   Compiling max_flow v0.1.0 (file:///Users/pedro/Documents/Code/max_flow)
src/lib.rs:128:56: 128:65 error: no method named `collect` found for type `BfsIterator<'_, V, i64, fn(_, i64, _) {flow_predicate}>` in the current scope
src/lib.rs:128         BfsIterator::new(self, source, flow_predicate).collect()
                                                                      ^~~~~~~~~
src/lib.rs:128:56: 128:65 note: the method `collect` exists but the following trait bounds were not satisfied: `BfsIterator<'_, V, i64, fn(_, i64, _) {flow_predicate}> : core::iter::Iterator`
src/lib.rs:128:56: 128:65 help: items from traits can only be used if the trait is implemented and in scope; the following trait defines an item `collect`, perhaps you need to implement it:
src/lib.rs:128:56: 128:65 help: candidate #1: `core::iter::Iterator`
error: aborting due to previous error
Could not compile `max_flow`.

EntilZha

In addition to The Black Hermit and Wizard of the Crow, this author is better known for a novel in which Mugo betrays the revolutionary Kihika, as well as another in which Munira burns down Wanjas brothel.

EntilZha

cd CO%                                                                                                                                                                                                      pedro on terminus.local in ~
$ cd Code/matplotlib
pedro on terminus.local in ~/Code/matplotlib (v2.x)
$ python setup.py build
============================================================================
Edit setup.cfg to change the build options

BUILDING MATPLOTLIB
            matplotlib: yes [1.5.1+405.gb6e0b9b]
                python: yes [2.7.11 (default, Jan 19 2016, 14:37:25)  [GCC

EntilZha

[info] Constructing Javadoc information...
[error] /Users/pedro/Documents/Code/spark/core/target/java/org/apache/spark/serializer/SerializationDebugger.java:159: error: cannot find symbol
[error]   static private  org.apache.spark.serializer.SerializationDebugger.ObjectStreamClassReflection reflect ()  { throw new RuntimeException(); }
[error]                                                                    ^
[error]   symbol:   class ObjectStreamClassReflection
[error]   location: class SerializationDebugger
[error] /Users/pedro/Documents/Code/spark/core/target/java/org/apache/spark/serializer/SerializationDebugger.java:22: error: class SerializationDebugger is already defined in package org.apache.spark.serializer
[error]   static private  class SerializationDebugger {
[error]                   ^
[error] /Users/pedro/Documents/Code/spark/mllib/target/java/org/apache/spark/ml/PipelineStage.java:6: error: Logging is not public in org.apache.spark.internal; cannot be accessed from outside package

EntilZha

import org.apache.spark.sql.expressions.MutableAggregationBuffer
import org.apache.spark.sql.expressions.UserDefinedAggregateFunction
import org.apache.spark.sql.Row
import sqlContext.implicits._
import org.apache.spark.sql.types.{StructType, StructField, DataType, ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType, DecimalType, StringType, BinaryType, BooleanType, TimestampType, DateType, ArrayType}
class MinBy(valueType: DataType, minType: DataType) extends UserDefinedAggregateFunction {
    def inputSchema: StructType = StructType(StructField("value", valueType) :: StructField("minCol", minType) :: Nil)
    
    def bufferSchema: StructType = StructType(StructField("value", valueType) :: StructField("minCol", minType) :: Nil)

EntilZha

---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
/home/user/.local/lib/python3.5/site-packages/IPython/core/formatters.py in __call__(self, obj)
    697                 type_pprinters=self.type_printers,
    698                 deferred_pprinters=self.deferred_printers)
--> 699             printer.pretty(obj)
    700             printer.flush()
    701             return stream.getvalue()

/home/user/.local/lib/python3.5/site-packages/IPython/lib/pretty.py in pretty(self, obj)

EntilZha

dummy_element_consumer
 data_size: 100, passes: 100
  grouper_it_0    3 0.003933991072699428
  grouper_it_1 3 0.0036308339331299067
  grouper_it_2 3 0.0039052229840308428
  grouper_impl 3 0.0020833380986005068
  grouper_it_0    10 0.0014350449200719595
  grouper_it_1 10 0.0015790120232850313
  grouper_it_2 10 0.0019499310292303562
  grouper_impl 10 0.001163481967523694

EntilZha

[package]
name = "preferential_attachment"
version = "0.1.0"

[dependencies]
rand = "0.3"

EntilZha

In [9]: model.predict(np.random.random((10, 3, 2)))
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
/home/pedro/anaconda3/lib/python3.6/site-pac

EntilZha

from qanta.guesser.tfidf import TfidfGuesser

guesser = TfidfGuesser.load('output/guesser/qanta.guesser.tfidf.TfidfGuesser')

questions = [
    "Name this first president of the united states",
    "This man invented the theory of general relativity"
]

n_guesses = 1